Category: 3. Business

Introduction

Age-related macular degeneration (AMD) is a progressive retinal disorder affecting millions of people worldwide []. In its advanced stages, characterized by neovascularization and geographic atrophy (GA), it can lead to significant vision loss, although symptoms may be subtle during the early and intermediate phases []. The Classification of Atrophy Meetings group has defined atrophy lesion development as incomplete retinal pigment epithelium (RPE) and outer retinal atrophy and complete RPE and outer retinal atrophy (cRORA) based on imaging methods []. GA, also known as cRORA, is the endpoint of dry AMD and is characterized by the loss of photoreceptors, RPE, and choriocapillaris [,]. With the advent of 2 approved therapies for GA secondary to AMD in 2023, namely pegcetacoplan (Syfovre) [] and avacincaptad pegol [], the treatment of GA represents a significant breakthrough. However, the effectiveness of these therapies relies heavily on early detection and the ability to monitor treatment response—a significant unmet need in current clinical practice. The recent approval of complement inhibitors underscores the necessity for precise, reproducible, and practical tools to not only identify GA at its earliest stages but also to objectively track morphological changes over time, thereby evaluating therapeutic efficacy [,]. Artificial intelligence (AI) is uniquely positioned to address this gap by enabling precise, reproducible, and automated quantification of GA progression and treatment response using noninvasive imaging modalities []. Unlike conventional methods that rely on subjective and time-consuming manual assessments, AI algorithms can detect subtle subclinical changes in retinal structures—such as photoreceptor integrity loss, RPE atrophy, and hyperreflective foci—long before they become clinically apparent. Thus, AI-based retinal imaging offers a critical foundation for early detection and timely intervention in GA.

Various imaging techniques, both invasive and noninvasive, can directly visualize GA lesions. Invasive methods, such as fluorescence angiography, often result in a poor patient experience and entail high costs due to pupil dilation and sodium fluorescein injection. While it remains the gold standard for assessing neovascular AMD and offers significant diagnostic insights for retinal vascular diseases, in most cases, noninvasive fundus images are used for GA diagnosis and management []. Color fundus photography (CFP), fundus autofluorescence (FAF), and near-infrared reflectance (NIR) are based on 2D images, which can generally produce results to quantify the atrophic area but fail to identify the retinal structure axially []. Compared with fundus imaging, optical coherence tomography (OCT) provides high-resolution, noninvasive 3D images of retinal structures for macular assessment. In addition, conventional B-scan (axial direction) OCT images can be integrated with en-face scans, facilitating the identification of atrophy borders similar to FAF [,]. Nonetheless, manual labeling is tedious, time-consuming, and impractical in a clinical setup []. There is an urgent and unmet need for early detection and management of GA using retinal image modalities. Recent advancements in AI, especially deep learning (DL), present a promising opportunity for enhancing GA detection, classification, segmentation, quantification, and prediction.

In the 1950s, AI referred to computer systems capable of performing complex tasks that historically only a human could do. So what is AI? How is it used in medicine today? And what may it do in the future? AI refers to the theory and development of computer systems capable of performing tasks that historically required human intelligence, such as recognizing speech, making decisions, and identifying patterns. AI is an umbrella term that encompasses a wide variety of technologies, including machine learning (ML) and DL []. ML is a subfield of AI that uses algorithms trained on datasets to create self-learning models capable of predicting outcomes and classifying information without human intervention []. ML refers to the general use of algorithms and data to create autonomous or semiautonomous machines. DL, meanwhile, is a subset of ML that layers algorithms into “neural networks” with 3 or more layers. Thus, it somewhat resembles the human brain, enabling machines to perform increasingly complex tasks []. DL algorithms generally have high and clinically acceptable diagnostic accuracy across different areas (ophthalmology, respiratory, breast cancer, etc) in radiology []. Within ophthalmology, DL algorithms showed reliable performance for detecting multiple findings in macular-centered retinal fundus images []. Therefore, automatic GA segmentation plays a vital role in the diagnosis and management of advanced AMD and its application in the clinical setting.

Given the rapid evolution of AI applications in ophthalmology and the growing clinical importance of GA, this study aimed to systematically review the current evidence on AI-based approaches for the detection and management of GA secondary to dry AMD using noninvasive imaging modalities. We aimed to evaluate diagnostic accuracy relative to reference standards and examine methodological challenges to inform the design of future research and clinical implementation.

Methods

Protocol and Registration

Before starting this systematic review and meta-analysis, we registered a protocol on the PROSPERO website. This review adhered to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and PRISMA-DTA (PRISMA of Diagnostic Test Accuracy) checklists [,].

Eligibility Criteria

We included studies using AI algorithms to detect, classify, identify, segment, quantify, or predict GA secondary to AMD from CFP, OCT, OCT angiography, FAF, or NIR. The data were from participants, with or without symptoms, who were diagnosed with GA (or cRORA) secondary to nonexudative AMD. Study designs were not restricted; multicenter or single-center, prospective or retrospective, post hoc analysis, clinical study, or model development studies were all accepted. Eyes with neovascular complications or macular atrophy from causes other than AMD, any previous anti-vascular endothelial growth factor treatment, any confounding retinopathy, or poor image quality were excluded.

Electronic Search Strategy

Two consecutive searches were conducted on PubMed, Embase, Web of Science, Scopus, Cochrane Library, and CINAHL. Because this review required the extraction of baseline data and items, considering the completeness of the data, we did not conduct any in press or print source searches and excluded conference proceedings and similar materials. The initial search was completed from the date of entry to December 1, 2024; the updated search, from December 1, 2024, to October 5, 2025. We used a search strategy for patient (GA) and index tests (AI and retinal images) that had been used in previous Cochrane Review without any search peer review process []. There were no restrictions on the date of publication. The language was limited to English. In , detailed search strategies for each database are provided. During this process, no filters were used. During the search process, we adhered to the PRISMA-S (Preferred Reporting Items for Systematic reviews and Meta-Analyses literature search extension) reporting guidelines [].

Selection Process

All relevant literature was imported into EndNote (version 20; Clarivate Analytics) software, and literature screening was conducted independently by 2 researchers (NS and JL) who specialize in ophthalmology. Duplicates were removed from the software, and the titles and abstracts of the literature were reviewed to identify those relevant to the topic. Finally, the full texts were downloaded and examined, leading to the selection of literature that met the inclusion criteria. In cases of inconsistencies in the final inclusion decisions made by the 2 researchers, a third professional (LL) was consulted to resolve the dispute.

Data Collection Process

Using standardized data items, the data were extracted independently from the included studies by 2 researchers (NS and JL). A third review author (LL) confirmed or adjudicated any discrepancies through group discussion. We retrieved the following data items: (1) study characteristics (author, year, study design, region, and theme), (2) dataset characteristics (databases, source of databases, training/validation/testing ratio, patient number, number of images or volumes, scan number, mean age, clinical registration number, and model evaluation method), (3) image and algorithm characteristics (devices, metrics, image modality, image resolution, and AI algorithms), (4) performance metrics (outcomes, performance of models, ground truth, and performance of the ophthalmologists), and (5) main results. All the information was retrieved from the main text and the tables provided in . Therefore, we did not seek additional data by contacting the authors or experts. In some studies, the authors reported multiple sets of performance data based on a subset of a single dataset. For example, they may have reported results such as sensitivity, specificity, accuracy, and so forth, conducted on the cross-validation set, the test set, or the development set. We referred to the relevant literature to select the optimal set of test performance results []. However, when the primary study provided performance results based on a single test, the development dataset was used to train the AI model, and an external validation set ultimately was used to determine the performance of the optimal model. We extracted the external validation set performance data [].

Risk of Bias and Application

We worked in pairs to assess the risk of bias and the applicability of the studies, which involved detection, classification, identification, segmentation, and quantification using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-AI [] and the modified QUADAS-2 tool [], while predictive studies used the Prediction Model Risk of Bias Assessment Tool (PROBAST) [].

In the current context, QUADAS-AI has not yet established a complete specification of items. Therefore, we referenced the examples provided by QUASAS-AI and the published literature to compile the revised QUADAS-AI items, which included 4 domains and 9 leading questions (Table S4 in ). The PROBAST tool comprises participants, predictors, outcomes, and analysis, containing 20 signaling questions across 4 domains (Table S5 in ). We also evaluated the applicability of the study based on the leading or signaling questions in the first 3 domains. A study with “yes” answers to all index questions was considered to have a low risk of bias. If the answer to any of the informational questions was “no,” there was a potential for bias, leading the authors to rate the risk of bias as high. “Indeterminate” grades were applied when detailed content was not provided in the literature, making it difficult for the evaluator to reach a judgment. They were used only when the reported data were insufficient. Throughout the process, disagreements between the 2 reviewers (NS and JL) were resolved by consulting the senior reviewer (LL).

Data Synthesis

As very few studies reported the number of true positives, true negatives, false positives, and false negatives, we restricted the quantitative analysis to determine the diagnostic accuracy of AI as a triaging tool for GA secondary to nonexudative AMD. However, a meta-analysis was not performed due to significant methodological heterogeneity across studies, arising from diverse AI architectures, imaging modalities, outcome metrics, and validation protocols. Instead, a systematic review was performed to qualitatively summarize performance trends. This approach allowed for a comprehensive evaluation of the AI capabilities in the detection and management of GA via noninvasive images.

Results

Study Selection

A total of 979 records related to the topic of this systematic review were searched across 6 different databases using a combination of subject terms and free-text terms. After removing duplicates, 335 records remained and were examined for titles and abstracts. Excluding studies not relevant to the research topic resulted in 200 reports. The full texts were then downloaded and reviewed in detail based on the eligibility criteria for the studies. In the final qualitative analysis, 41 studies were included. Of these, 10 focusing on GA diagnosis, 20 on GA assessment and progression, and 11 on GA prediction. presents the detailed flow diagram of the literature selection.

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AI in Detecting the Presence of GA

Ten of the 41 included studies focused on AI-based detection of GA using noninvasive retinal images (Table S1 in ). As listed in , the studies were published from 2018 to 2025. Four of the studies [-] focused on model development, 3 [-] were retrospective studies, and 3 [-] were prospective studies (1 multicenter cohort study, 1 multicenter and low-interventional clinical study, and 1 clinical study). Geographically, half were from the United States, with others from Israel, Italy, Switzerland, Germany, and a multicenter European collaboration. The studies addressed several detection-related tasks: 5 focused solely on GA detection [-,,], 2 covered detection and classification [,], and others integrated detection with quantification or segmentation [,,].

Dataset configurations varied: 6 studies used training, validation, and test sets [-,,]; 3 used only training and test sets [,,]; and 1 included a tuning set []. Collectively, these studies involved at least 7132 participants, with ages ranging from 50 to 85 years. Three studies were registered with ClinicalTrials.gov (NCT00734487, NCT01790802, and NCT03349801) [,,]. Cross-validation methods included 5-fold (40% of studies) [,,,], 10-fold (20%) [,], and triple-fold (10%) []; 30% did not report validation details.

Spectral-domain (SD)–OCT was the most frequently used imaging modality (6/10 of studies) [-,,,], followed by CFP (2/10) [,], and FAF or NIR (2/10 each) [,]. Most studies applied image preprocessing techniques—such as size standardization, orientation adjustment, intensity normalization, and noise reduction—to improve model performance. DL-based algorithms for GA detection have been developed for multiple image modalities. For example, Derradji et al [] trained a convolutional neural networks (CNNs), U-Net, to predict atrophic signs in the retina, based on the EfficientNet-b3 architecture. Kalra et al [] and de Vente et al [] also trained a DL model based on U-Net. Yao et al [] applied 3D OCT scans with ResNet18 pretrained on the ImageNet dataset, and Chiang et al [] developed CNN (ResNet18) to improve computational efficiency. Elsawy et al [] proposed Deep-GA-Net, a 3D backbone CNN with a 3D loss-based attention layer, and evaluated the effectiveness of using attention layers. Sarao et al [] used a deep CNN, the EfficientNet_b2 model, which was pretrained on the ImageNet dataset and is well-known for its high efficiency and small size. Keenan et al [] established their model using Inception v3, while Treder et al [] performed a deep CNN, a self-learning algorithm, processing input data with FAF images.

A total of 14 performance sets were extracted from the 10 studies. Key metrics included sensitivity, specificity, accuracy, positive predictive value, negative predictive value, intersection over union, area under the receiver operating characteristic curve, area under the precision-recall curve, F₁-score, precision, recall, Kappa, and dice similarity coefficient. Six OCT-based studies showed that DL models could detect GA with high accuracy, comparable to human graders [-,,,]. Two studies using CFP also reported strong performance [,], while FAF- and NIR-based approaches demonstrated excellent repeatability and reliability [,].

We conducted a thorough evaluation of the 10 diagnostic studies’ methodological quality for the “participant selection,” “index test,” “reference standard,” and “flow and timing” domains at the study level (). None of the studies had an overall low or unclear risk of bias; instead, every study had a high risk of bias in at least 1 of the 4 domains. Regarding “patient selection,” only 4 studies [,,,] described the eligibility criteria; the rest did not report them. One study [] used an open dataset (ImageNet) and did not include a test set. The small sample size of 4 studies [,,,] may have resulted in overfitting. In addition, 3 studies [,,] did not report image formats and resolutions. Five studies [,,-] had a high risk of bias in participant selection because the included participants were not only GA secondary to dry AMD but also had other unrelated diseases. Regarding the “Index test,” only 1 algorithm was externally validated using a different dataset []; all other items were evaluated as low risk.

AI in GA Assessment and Progression

Twenty studies explored AI for GA assessment and progression using noninvasive imaging, published between 2019 and 2025 (Table S2 in ). As shown in , these studies covered 11 segmentation [,,-], 2 algorithm optimization [,], 3 AMD progression classification [-], and 3 combined tasks such as identification, segmentation, and quantification [-]. One study focused solely on GA quantification []. Retrospective analyses accounted for 9 studies [,,,,,,,,], while 7 were model development [,-,,,], and the remainder were prospective [,], comparative [], or cross-sectional []. Geographically, contributions came from China (6/20), the United States (7/20), the United Kingdom (2/20), Australia (2/20), France (1/20), Israel (1/20), and Austria (1/20).

Dataset configurations varied: 9 out of 20 studies used training, validation, and test sets [,,-,-]; 11 studies used training and test sets [,,-,]; 2 studies used training and validation sets [,]; 1 study comprised training, tuning, and internal validation sets []; and 2 studies did not specify [,]. Across studies, at least 14,064 participants provided image data for analysis. Less than half of the studies (9/20, 45%) provided demographic information, with the average age of participants ranging from 55 to 94 years. Six studies were registered with ClinicalTrials.gov (NCT01342926, NCT02503332, NCT02479386, NCT02399072, and NCT04469140 [,,,,,]). To assess the generalization ability of the DL model, cross-validation methods included 5-fold (8/20 studies [,,,-,]), 4-fold (1/20 study []), 3-fold (1/20 study []), and other approaches (1/20 study []). Nine studies did not report validation specifics.

Multiple imaging modalities supported GA assessment: spectral domain optical coherence tomography (SD-OCT) was most common, followed by swept-source OCT (SS-OCT), 3D-OCT, FAF, and NIR. Preprocessing techniques were widely applied to standardize images and improve model performance. Algorithm architectures varied, with U-Net being the most frequently used. Other approaches included custom CNNs, self-learning algorithms, weakly supervised models, and multimodal networks. For example, Hu et al [] trained the DL models (ResNet-50, Xception, DenseNet169, and EfficientNetV2), evaluating them on a single fold of the validation dataset, with all F₁-scores exceeding 90%. Yang [] proposed an ensemble DL architecture that integrated 4 different CNNs, including ResNet50, EfficientNetB4, MobileNetV3, and Xception, to classify dry AMD progression stages. GA lesions on FAF were automatically segmented using multimodal DL networks (U-Net and Y-Net) fed with FAF and NIR images []. In contrast to the multimodal algorithms mentioned above (ie, the examples of DL models), Safai [] investigated 3 distinct segmentation architectures along with 4 commonly used encoders, resulting in 12 different AI model combinations to determine the optimal AI framework for GA segmentation on FAF images.

From 20 studies, 42 performance sets were collected. Common metrics included correlation coefficient, mean absolute error, Spearman correlation coefficient, intraclass coefficient, overlap ratio, Pearson correlation coefficient (r²), Kappa, specificity (SPE), sensitivity (SEN), accuracy, positive predictive value (PPV), F₁-score, P, R, intersection over union, and dice similarity coefficient (DSC). Regarding the segmentation, classification, identification, and quantification of GA in SD-OCT, 12 studies demonstrated performance comparable to that of clinical experts [,,,,,-,,]. AI was also capable of efficiently detecting, segmenting, and measuring GA in SS-OCT, 3D-OCT, and FAF images, according to 4 studies [,,,]. AI for GA segmentation in FAF and NIR images, with clinical data showing good segmentation performance [,,].

We performed a comprehensive assessment of the methodological quality of 16 GA assessment and progression studies encompassing 4 domains (). Only 8 studies detailed the eligibility criteria in the “patient selection” category, while the others had not been published. Three of the studies [-] lacked complete datasets, and 3 others [,,] had small datasets or limited volumes of data. In addition, 3 studies [,,] failed to provide information on image formats or resolutions. Two studies [,] were ranked as high risk regarding patient selection since the participants included other types of dry AMD (drusen, nascent GA). In terms of applicability, 18 studies were classified as low risk, while 2 were deemed high risk concerning patient selection. Concerning the “Index test,” only 3 algorithms underwent external validation with a different dataset [,,]. All other items were evaluated as low risk.

AI in Predicting GA Lesion Area and Progression

Eleven studies used AI for predicting GA lesion growth and progression using noninvasive imaging (Table S3 in ). These studies were published between 2021 and 2025, with some information provided in . The study designs consisted of 6 retrospective studies [-], 2 model development studies [,], 2 post hoc analyses [,], and 1 clinical evaluation of a DL algorithm []. Participants or images came from various regions: 6 studies were based in the United States [,-,], 3 in Australia [-], 1 in Switzerland [], and another involving multiple centers in China and the United States []. Research aims focused on GA growth prediction [,,-,,], combined prediction and evaluation of lesion features [], treatment response assessment [], and integrated segmentation-prediction tasks [,].

Dataset structures varied: 3 out of 11 studies used training-validation-test splits [,,]; 2 out of 11 studies used training-test sets [,]; 3 out of 11 studies used training-validation sets [,,]; and the rest adopted development–holdout [,] or development-holdout-independent test configurations []. In total, 6706 participants were included across studies. Fewer than half of the studies (4/11, 36.4%) reported demographic information, with mean age ranges spanning from 74 to 83 years [,,,]. Six studies [-,,] were ethically approved and registered on ClinicalTrials.gov under the following identifiers: NCT02503332, NCT02247479, NCT02247531, NCT02479386, NCT01229215, and NCT02399072. The DL model’s generalizability was assessed using leave-one-out cross-validation in 1 study [], 5-fold cross-validation in 7 studies [,,,,-], and 8-fold cross-validation in 1 study []. The remaining 2 studies [,] did not specify the cross-validation methodology.

Studies used 3D-OCT, SD-OCT, NIR, and FAF images, primarily sourced from Heidelberg, Zeiss, and Bioptigen devices. While most reported image metrics, 2 studies did not specify resolution details [,]. Commonly used DL architectures included Inception v3 [,], PSC-UNet [,], U-Net [,,], EfficientNet-b3 [], and VGG16 []. In addition, some studies introduced novel approaches, such as en-face intensity maps, SLIVER-net, 3D CNN, and a recurrent neural network, for improved GA progression forecasting.

According to various image modalities, datasets, and follow-up durations, we gathered 31 sets of performance data from 11 studies. The performance metrics included the Hausdorff distance, concordance index, overlap, SEN, SPE, accuracy, mean absolute error, Kappa, DSC, P, PPV, R, r², and negative predictive value. The findings for a single image modality (3D-OCT, SD-OCT, or FAF) demonstrated the development of DL algorithms to predict GA growth rate and progression with excellent performance characteristics comparable to trained experts [-,-]. Multimodal approaches combining FAF, NIR, and SD-OCT further showed feasibility for individualized lesion growth prediction and localization [,-].

In this systematic review, we used the PROBAST tool to rigorously evaluate prediction models across 4 domains, addressing 20 signaling questions for each paper reviewed. Within the “participants” domain, all studies used appropriate data sources; however, only 6 studies [-,,] clearly outlined their inclusion and exclusion criteria for participants, leaving the others unclear. In terms of “predictors,” these were defined and evaluated similarly for all participants, having no connection to outcome data and being available at baseline. All studies evaluated “yes” to the questions on outcome measurement methods, definitions, interference factors, and measurement time intervals. Concerning “analysis,” Dow [] and Zhang [] applied a small dataset with an insufficient number of participants. While Zhang performed internal validation, the lack of external validation notably limits the model’s generalizability, which was constructed with bi-directional long-short term memory networks and CNN frameworks. Two studies by Salvi [] and Yoshida [] lacked independent and external validation. Gigon [] failed to explicitly mention missing data handling, complex problems, and model overfitting. Conversely, all other items were evaluated as low risk, and the applications of the studies were universally ranked as low risk (Table S1 in ).

Discussion

Principal Findings

This systematic review evaluated the performance of AI, particularly DL algorithms, in detecting and managing GA secondary to dry AMD using noninvasive imaging modalities. Our findings demonstrate that AI models exhibit strong capabilities in accurately detecting, segmenting, quantifying, and predicting GA progression from OCT, FAF, CFP, and NIR imaging, achieving diagnostic accuracy comparable to that of human experts. However, this review also identified several methodological challenges, such as limited sample sizes, inconsistent annotation standards, and a general lack of external validation, which may hinder the clinical generalizability and practical application of these models. Despite these limitations, AI-based tools show significant potential for future use by both specialists and nonspecialists in primary and specialty care settings.

AI in Detecting GA With OCT, FAF, NIR, and CFP Images

Ten studies published between 2018 and 2025 were included, involving at least 7132 participants aged 50 to 85 years. Half of the studies were conducted in the United States, while others originated from European countries. SD-OCT was the most frequently used imaging modality (6/10 studies), followed by CFP (2/10 studies), NIR (1/10 studies), and FAF (1/10 studies). Image preprocessing techniques, such as standardization of size, orientation, and intensity, as well as noise reduction, were consistently applied to enhance model stability and training efficiency. However, 3 studies did not report critical image parameters, such as resolution, potentially limiting reproducibility. DL-based algorithms, including CNNs, were the primary methodologies used for GA detection. Cross-validation techniques, such as 5-fold and 10-fold methods, were used in half of the studies to assess model robustness, though 3 studies did not report validation strategies. AI, particularly DL algorithms, holds significant promise for the detection of GA using noninvasive imaging modalities. OCT, CFP, NIR, and FAF each demonstrated robust diagnostic potential, with performance metrics rivaling or exceeding human expertise.

AI for GA Management With OCT, FAF, and NIR Images

A total of 20 studies (14,064 participants) were published between 2019 and 2025, focusing on themes such as GA segmentation, classification, quantification, and progression prediction. The research designs and geographic regions are diverse. The studies included retrospective analysis (9/20), model development (7/20), and prospective, comparative, or cross-sectional studies (4/20). Significant contributions came from China (6/20) and the United States (7/20), with additional studies from the United Kingdom (2/20), Australia (2/20), France (1/20), Israel (1/20), and Austria (1/20). The studies used a variety of imaging modalities to assess GA, including SD-OCT, FAF, NIR, SS-OCT, and 3D-OCT. DL algorithms demonstrated remarkable performance in GA management tasks. U-Net was the most commonly used architecture. Multimodal approaches combined FAF and NIR images with DL networks to improve segmentation accuracy. Performance metrics, such as DSC, Kappa, SEN, SPE, and accuracy, consistently showed strong diagnostic accuracy, with several studies achieving performance comparable to clinical experts.

Eleven studies with 6706 participants, published between 2021 and 2025, concentrated on the application of AI for predicting and segmenting GA lesions, as well as their growth and progression. The methodologies were diverse, including retrospective studies, model development studies, post hoc analyses, and clinical algorithm assessment. Participants or images were gathered from regions such as the United States, Australia, Switzerland, and various centers in China and the United States, ensuring broad geographic representation. Demographic information was reported in fewer than half of the studies, with a mean age ranging from 74 to 83 years. Imaging modalities, such as 3D-OCT, SD-OCT, NIR, and FAF, were obtained from devices including Bioptigen, Heidelberg Spectralis HRA+OCT, and Cirrus OCT. While the image preprocessing parameters were consistent across most studies, some did not specify image resolution. Multiview CNN architectures and advanced frameworks, such as the bi-directional long-short term memory networks, were used. DL algorithms exhibited excellent predictive capabilities, with multimodal approaches enabling individualized GA lesion growth prediction.

Noninvasive Image Analysis Techniques for GA

GA, a late-stage form of dry AMD, is marked by the irreversible loss of photoreceptors, RPE, and choriocapillaris [,]. The application of noninvasive imaging modalities has revolutionized the detection and management of GA. A comparative summary of AI performance across these modalities is provided in Table S2 in . CFP serves as a standard initial assessment tool, useful for screening and early detection. It identifies GA lesions as visible underlying choroidal vessels and well-defined regions of RPE hypopigmentation []. FAF imaging using a blue excitation wavelength (488 nm) visualizes metabolic changes at the level of photoreceptor or RPE complex and is practical in assessing GA lesion size and progression with hypo-autofluorescence []. In contrast to nonatrophic areas, GA lesions on NIR (787-820 nm, longer than FAF) typically appear brighter and less harmful to the eye []. In addition, NIR can help detect the boundaries of foveal lesions, where image contrast is lower on FAF []. Recently, the Classification of Atrophy Meeting group recommended that atrophy in both patients with and those without neovascular AMD be defined based on specific drusen characteristics and other anatomical features, and it is most easily characterized by OCT [,]. OCT stands out as the gold standard for GA detection and classification, providing high-resolution, cross-sectional, and en face images of the retina and choroid. SD-OCT is widely used in research and clinical trials, offering precise measurement of GA area and growth rates, while SS-OCT and 3D-OCT offer superior structural insights and potential for AI-driven automation [,,]. Despite the higher cost and technical complexity of advanced OCT technologies, their detailed GA assessment capabilities make them indispensable tools in both clinical practice and research. Furthermore, OCT provides volumetric (3D) structural data, unlike the 2D en face projections of FAF, CFP, and NIR. It allows AI to learn not just the surface appearance of atrophy but also the cross-sectional structure alterations that define and precede GA []. As technology advances, the integration of AI and further developments in imaging techniques are expected to enhance the utility of these modalities, overcoming current limitations and expanding their applications in ophthalmology.

Advantages and Challenges of AI Architectures in Clinical Workflow

AI addresses critical limitations of traditional GA monitoring, such as labor-intensive manual grading and intergrader variability []. Therefore, automated algorithms enable rapid, standardized analysis of large fundus image datasets, reducing clinician workload and enhancing reproducibility []. Furthermore, our review revealed a clear trend in the choice of model architectures tailored to specific clinical tasks. A critical analysis of these architectures is provided in Table S3 in . Interestingly, with the advancement of AI algorithm architectures, numerous studies have emerged that use these technologies to identify atrophy caused by various retinal diseases and to evaluate treatment outcomes through image analysis. Miere et al [] pretrained a DL-based classifier to automatically distinguish GA from atrophy secondary to inherited retinal diseases on FAF according to etiology, using 2 approaches (a trained and validated method and a 10-fold cross-validation method), achieving good accuracy and excellent area under the receiver operating characteristic (AUROC) values. In addition, a study examined the association between treatment and changes in photoreceptor lamina thickness in patients with GA secondary to AMD. The effect of pegcetacoplan on photoreceptors in OCT was supported by this post hoc analysis, which demonstrated that treatment with the drug was linked with reduced outer retinal thinning []. Similarly, DL-based OCT image analysis assessed the therapeutic effectiveness of complement component 3 inhibition in delaying GA progression, with findings indicating decreased photoreceptor thinning and loss []. Recent studies demonstrating the application of AI algorithms in imaging further validate their potential as reliable supplements to human expertise in the diagnosis and management of GA.

Technical Challenges and Limitations

Despite the promising advancements in AI for GA detection and management, several technical challenges and limitations persist. A significant limitation of OCT-based AI models is their difficulty in distinguishing GA secondary to AMD from other forms of retinal atrophy; thus, the findings may not generalize to broader AMD cases or other retinal diseases, which limits their clinical applicability. In addition, images from different OCT devices show significant variability and imprecision, not offering good enough data acquisition []. Another major challenge is the variability in algorithm performance caused by differences in training data, image acquisition protocols, and disease definitions. These differences reduce reproducibility and limit practical deployment. For instance, the absence of standardized reporting in AI studies can result in discrepancies when interpreting results and hinder comparisons between different models. Moreover, despite the high-performance metrics (eg, SEN, SPE, DSC>0.85, and AUROC>0.95) reported by many studies, methodological limitations remain. All diagnostic studies included in this review were assessed as high risk in at least 1 domain (10/10), only 1 GA assessment study (1/20) was evaluated as low risk across all domains, and several prediction studies (7/11) were ranked as high or unclear risk in at least 1 domain, primarily due to small or nonrepresentative datasets and a lack of detailed reporting on image preprocessing and external validation. These methodological shortcomings may lead to an overestimation of AI model performance and reduced overall robustness, thereby decreasing the generalizability of the findings and limiting confidence in their real-world applicability. Future studies should prioritize the use of larger, more diverse datasets and implement rigorous validation frameworks to enhance performance metrics (including detection, segmentation, quantification, and prediction accuracy) and conduct prospective, multicenter validation studies to improve clinical applicability and generalizability. Furthermore, adherence to established reporting guidelines for AI studies (such as the Standards for Reporting Diagnostic Accuracy-AI and Checklist for Artificial Intelligence in Medical Imaging [,]) would improve comprehension and transparency, allow for more meaningful comparisons between systems, and facilitate meta-analyses.

Real-World Implications and Research Contributions

Overall, despite some limitations, AI is constantly evolving and holds great potential for transformation in the health care sector []. AI has the potential to accelerate existing forms of medical analysis; however, its algorithms require further testing to be fully trusted. Clinically, AI-based automated tools show strong potential to facilitate early detection, precise quantification, progression, and prediction of GA, thereby reducing the burden on retinal specialists and improving diagnostic consistency. Furthermore, DL algorithms have demonstrated effectiveness in identifying retinal image features associated with cognitive decline, dementia, Parkinson disease, and cardiovascular risk factors []. These findings indicate that AI-based retinal images hold promise for transforming primary care and systemic disease management. Although most AI applications remain in the validation phase, the integration of AI with multimodal imaging, novel biomarkers, and emerging therapeutics holds promise for transforming clinical management paradigms in GA and advancing personalized medicine. Future efforts should focus on developing standardized datasets, improving algorithmic generalizability, and conducting real-world validation studies to fully integrate AI into routine ophthalmic practice.

Conclusion

AI, especially DL-based algorithms, holds considerable promise for the detection and management of GA secondary to dry AMD, with performance comparable to trained experts. This systematic review synthesizes and critically appraises the current evidence, highlighting that AI’s capabilities extend across GA management—from initial detection and precise segmentation to the forecasting of lesion progression, which informs future research directions. Meanwhile, with the development of C5 inhibitors, AI-based noninvasive fundus image analysis is expected to detect, identify, and monitor GA at an early stage, thereby increasing the window of opportunity in the future. AI has strong potential to augment and streamline clinical workflows by offering automated, reproducible analysis that can assist clinicians in managing large volumes of imaging data; however, more studies are needed to further validate its effectiveness, repeatability, and accuracy.

By Vivien Lou Chen

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Aggressive fiscal-stimulus efforts by the cabinet of Japan’s first female prime minister, Sanae Takaichi, have created a spike in long-dated yields of Japanese government bonds and further weakness in the yen (USDJPY) in the past few weeks. It’s a situation that is being likened to the September-October 2022 crisis in the U.K., which stemmed from a crisis in confidence over a package of unfunded tax cuts proposed by then-Prime Minister Liz Truss’s government.

Read: Liz Truss redux? Simultaneous drop for Japanese currency and bonds draws eerie parallels

The U.S. needs to manage the cost of interest payments given a more than $38 trillion national debt, and this is a primary motivation for why the Trump administration wants to bring down long-term Treasury yields. Last week, Treasury Secretary Scott Bessent said in a speech in New York that the U.S. is making substantial progress in keeping most market-based rates down. He also said the 10-year “term premium,” or additional compensation demanded by investors to hold the long-dated maturity, is basically unchanged. Longer-duration yields matter because they provide a peg for borrowing rates used by U.S. households, businesses and the government.

Developments in Japan are now creating the risk that U.S. yields could rise alongside Japan’s yields. This week, Japanese government-bond yields hit their highest levels in almost two decades, with the country’s 10-year rate BX:TMBMKJP-10Y spiking above 1.78% to its highest level in more than 17 years. The 40-year yield BX:TMBMKJP-40Y climbed to an all-time high just above 3.7%.

In the U.S., 2- BX:TMUBMUSD02Y and 10-year yields BX:TMUBMUSD10Y finished Friday’s session at the lowest levels of the past three weeks, at 3.51% and almost 4.06% respectively. The 30-year U.S. yield BX:TMUBMUSD30Y fell to 4.71% or lowest level since Nov. 13.

There’s a risk now that U.S. yields may not fall as much as they otherwise might after factoring in market-implied expectations for a series of interest-rate cuts by the Federal Reserve into 2026.

Japan’s large U.S. footprint

Treasury yields are not going to necessarily follow rates on Japanese government bonds higher “on a one-for-one basis,” but there might be a limit on how low they can go, said Adam Turnquist, chief technical strategist at LPL Financial. He added that the impact of Japanese developments on the U.S. bond market could take years to play out, but “we care now because of the direction Japan’s policy is going in” and the possibility that this impact might occur even sooner.

Some of the catalysts that usually tend to push Treasury yields lower, such as any commentary from U.S. monetary policymakers that suggests the Fed might be inclined to cut rates, “might be muted because of the increased value of foreign debt,” Turnquist added.

U.S. government debt rallied for a second day on Friday, pushing yields lower, after New York Fed President John Williams said there is room to cut interest rates in the near term.

All three major U.S. stock indexes DJIA SPX COMP closed higher Friday, but notched sharp weekly losses, as investors attempted to calm doubts over the artificial-intelligence trade.

The troubling spike in yields on Japanese government bonds hasn’t fully spilled over into the U.S. bond market yet, but it remains a risk. “A repeat of the Truss episode is what people are afraid of,” said Marc Chandler, chief market strategist and managing director at Bannockburn Capital Markets.

Concerns about Japan gained added significance on Friday, when Takaichi’s cabinet approved a 21.3 trillion yen (or roughly $140 billion) economic stimulus package, which Reuters described as lavish. The amount of new spending being injected into the country’s economy from a supplementary budget, much of which is not repurposed from existing funds, is 17.7 trillion yen ($112 billion).

Anxiety over Takaichi’s stimulus efforts has resulted in a Japanese yen that has weakened against its major peers and fallen to a 10-month low ahead of Friday’s session, and in a spike in the country’s long-dated yields. Yields on 30-year BX:TMBMKJP-30Y Japanese government debt have risen this month to 3.33%.

Japan is the biggest foreign holder of Treasurys, with a roughly 13% share, according to the most recent data from the U.S. Treasury Department, and the concern is that the country’s investors might one day pull the rug by keeping more of their savings at home.

Bond-auction anxiety

Earlier in the week, a weak 20-year auction in Japan was cited as one reason why U.S. Treasury yields were a touch lower in early New York trading, which means that demand for U.S. government paper remained in place. Global investors are often incentivized to move their money based on which country offers the highest yields and best overall value.

“The conventional wisdom is that as yields rise in Japan, the Japanese are more likely to keep their savings at home rather than export it,” Chandler said. “The Japanese have been buyers of Treasurys and U.S. stocks, and if they decide to keep their money at home, those U.S. markets could lose a bid.”

For now, Japanese investors, which include insurers and pension funds, appear to be continuing to export their savings by buying more foreign government debt like Treasurys. Data from the U.S. Treasury Department shows that as of September, Japanese investors held just under $1.19 trillion in Treasurys, a number which has been climbing every month this year and is up from about $1.06 trillion last December.

One reason for this is the exchange rate. The yen has depreciated against almost every major currency this year. Japanese investors have been buying U.S. Treasurys because they can diversify against the yen, which is the weakest of the G-10 currencies on an unhedged basis, according to Chandler.

If concerns about the Takaichi government’s stimulus efforts translate into even higher yields in Japan, this could incentivize local investors to keep more of their savings at home, but might also mean rising yields for countries like the U.S.

-Vivien Lou Chen

This content was created by MarketWatch, which is operated by Dow Jones & Co. MarketWatch is published independently from Dow Jones Newswires and The Wall Street Journal.

  (END) Dow Jones Newswires

  11-21-25 1609ET
Copyright (c) 2025 Dow Jones & Company, Inc.

• We’re introducing Zoomer, Meta’s comprehensive, automated debugging and optimization platform for AI. 
• Zoomer works across all of our training and inference workloads at Meta and provides deep performance insights that enable energy savings, workflow acceleration, and efficiency gains in our AI infrastructure. 
• Zoomer has delivered training time reductions, and significant QPS improvements, making it the de-facto tool for AI performance optimization across Meta’s entire AI infrastructure.

At the scale that Meta’s AI infrastructure operates, poor performance debugging can lead to massive energy inefficiency, increased operational costs, and suboptimal hardware utilization across hundreds of thousands of GPUs. The fundamental challenge is achieving maximum computational efficiency while minimizing waste. Every percentage point of utilization improvement translates to significant capacity gains that can be redirected to innovation and growth.

Zoomer is Meta’s automated, one-stop-shop platform for performance profiling, debugging, analysis, and optimization of AI training and inference workloads. Since its inception, Zoomer has become the de-facto tool across Meta for GPU workload optimization, generating tens of thousands of profiling reports daily for teams across all of our apps. 

Why Debugging Performance Matters

Our AI infrastructure supports large-scale and advanced workloads across a global fleet of GPU clusters, continually evolving to meet the growing scale and complexity of generative AI.

At the training level it supports a diverse range of workloads, including powering models for ads ranking, content recommendations, and GenAI features.  

At the inference level, we serve hundreds of trillions of AI model executions per day.

Operating at this scale means putting a high priority on eliminating GPU underutilization. Training inefficiencies delay model iterations and product launches, while inference bottlenecks limit our ability to serve user requests at scale. Removing resource waste and accelerating workflows helps us train larger models more efficiently, serve more users, and reduce our environmental footprint.

AI Performance Optimization Using Zoomer

Zoomer is an automated debugging and optimization platform that works across all of our AI model types (ads recommendations, GenAI, computer vision, etc.) and both training and inference paradigms, providing deep performance insights that enable energy savings, workflow acceleration, and efficiency gains.  

Zoomer’s architecture consists of three essential layers that work together to deliver comprehensive AI performance insights: 

Infrastructure and Platform Layer

The foundation provides the enterprise-grade scalability and reliability needed to profile workloads across Meta’s massive infrastructure. This includes distributed storage systems using Manifold (Meta’s blob storage platform) for trace data, fault-tolerant processing pipelines that handle huge trace files, and low-latency data collection with automatic profiling triggers across thousands of hosts simultaneously. The platform maintains high availability and scale through redundant processing workers and can handle huge numbers of profiling requests during peak usage periods.

Analytics and Insights Engine

The core intelligence layer delivers deep analytical capabilities through multiple specialized analyzers. This includes: GPU trace analysis via Kineto integration and NVIDIA DCGM, CPU profiling through StrobeLight integration, host-level metrics analysis via dyno telemetry, communication pattern analysis for distributed training, straggler detection across distributed ranks, memory allocation profiling (including GPU memory snooping), request/response profiling for inference workloads, and much more. The engine automatically detects performance anti-patterns and also provides actionable recommendations.

Visualization and User Interface Layer

The presentation layer transforms complex performance data into intuitive, actionable insights. This includes interactive timeline visualizations showing GPU activity across thousands of ranks, multi-iteration analysis for long-running training workloads, drill-down dashboards with percentile analysis across devices, trace data visualization integrated with Perfetto for kernel-level inspection, heat map visualizations for identifying outliers across GPU deployments, and automated insight summaries that highlight critical bottlenecks and optimization opportunities.

How Zoomer Profiling Works: From Trigger to Insights

Understanding how Zoomer conducts a complete performance analysis provides insight into its sophisticated approach to AI workload optimization.

Profiling Trigger Mechanisms

Zoomer operates through both automatic and on-demand profiling strategies tailored to different workload types. For training workloads, which involve multiple iterations and can run for days or weeks, Zoomer automatically triggers profiling around iteration 550-555 to capture stable-state performance while avoiding startup noise. For inference workloads, profiling can be triggered on-demand for immediate debugging or through integration with automated load testing and benchmarking systems for continuous monitoring.

Comprehensive Data Capture

During each profiling session, Zoomer simultaneously collects multiple data streams to build a holistic performance picture: 

• GPU Performance Metrics: SM utilization, GPU memory utilization, GPU busy time, memory bandwidth, Tensor Core utilization, power consumption, clock frequencies, and power consumption data via DCGM integration.
• Detailed Execution Traces: Kernel-level GPU operations, memory transfers, CUDA API calls, and communication collectives via PyTorch Profiler and Kineto.
• Host-Level Performance Data: CPU utilization, memory usage, network I/O, storage access patterns, and system-level bottlenecks via dyno telemetry.
• Application-Level Annotations: Training iterations, forward/backward passes, optimizer steps, data loading phases, and custom user annotations.
• Inference-Specific Data: Rate of inference requests, server latency, active requests, GPU memory allocation patterns, request latency breakdowns via Strobelight’s Crochet profiler, serving parameter analysis, and thrift request-level profiling.
• Communication Analysis: NCCL collective operations, inter-node communication patterns, and network utilization for distributed workloads

Distributed Analysis Pipeline

Raw profiling data flows through sophisticated processing systems that deliver multiple types of automated analysis including:

• Straggler Detection: Identifies slow ranks in distributed training through comparative analysis of execution timelines and communication patterns.
• Bottleneck Analysis: Automatically detects CPU-bound, GPU-bound, memory-bound, or communication-bound performance issues.
• Critical Path Analysis: Systematically identifies the longest execution paths to focus optimization efforts on highest-impact opportunities.
• Anti-Pattern Detection: Rule-based systems that identify common efficiency issues and generate specific recommendations.
• Parallelism Analysis: Deep understanding of tensor, pipeline, data, and expert parallelism interactions for large-scale distributed training.
• Memory Analysis: Comprehensive analysis of GPU memory usage patterns, allocation tracking, and leak detection.
• Load Imbalance Analysis: Detects workload distribution issues across distributed ranks and recommendations for optimization.

Multi-Format Output Generation

Results are presented through multiple interfaces tailored to different user needs: interactive timeline visualizations showing activity across all ranks and hosts, comprehensive metrics dashboards with drill-down capabilities and percentile analysis, trace viewers integrated with Perfetto for detailed kernel inspection, automated insights summaries highlighting key bottlenecks and recommendations, and actionable notebooks that users can clone to rerun jobs with suggested optimizations.

Specialized Workload Support

For massive distributed training for specialized workloads, like GenAI, Zoomer contains a purpose-built platform supporting LLM workloads that offers specialized capabilities including GPU efficiency heat maps and N-dimensional parallelism visualization. For inference, specialized analysis covers everything from single GPU models, soon expanding to massive distributed inference across thousands of servers.

A Glimpse Into Advanced Zoomer Capabilities

Zoomer offers an extensive suite of advanced capabilities designed for different AI workload types and scales. While a comprehensive overview of all features would require multiple blog posts, here’s a glimpse at some of the most compelling capabilities that demonstrate Zoomer’s depth:

Training Powerhouse Features:

• Straggler Analysis: Helps identify ranks in distributed training jobs that are significantly slower than others, causing overall job delays due to synchronization bottlenecks. Zoomer provides information that helps diagnose root causes like sharding imbalance or hardware issues.
• Critical Path Analysis: Identification of the longest execution paths in PyTorch applications, enabling accurate performance improvement projections. 
• Advanced Trace Manipulation: Sophisticated tools for compression, filtering, combination, and segmentation of massive trace files (2GB+ per rank), enabling analysis of previously impossible-to-process large-scale training jobs

Inference Excellence Features:

• Single-Click QPS Optimization: A workflow that identifies bottlenecks and triggers automated load tests with one click, reducing optimization time while delivering QPS improvements of +2% to +50% across different models, depending on model characteristics. 
• Request-Level Deep Dive: Integration with Crochet profiler provides Thrift request-level analysis, enabling identification of queue time bottlenecks and serving inefficiencies that traditional metrics miss.
• Realtime Memory Profiling: GPU memory allocation tracking, providing live insights into memory leaks, allocation patterns, and optimization opportunities.

GenAI Specialized Features:

• LLM Zoomer for Scale: A purpose-built platform supporting 100k+ GPU workloads with N-dimensional parallelism visualization, GPU efficiency heat maps across thousands of devices, and specialized analysis for tensor, pipeline, data, and expert parallelism interactions.
• Post-Training Workflow Support: Enhanced capabilities for GenAI post-training tasks including SFT, DPO, and ARPG workflows with generator and trainer profiling separation.

Universal Intelligence Features:

• Holistic Trace Analysis (HTA): Advanced framework for diagnosing distributed training bottlenecks across communication overhead, workload imbalance, and kernel inefficiencies, with automatic load balancing recommendations.
• Zoomer Actionable Recommendations Engine (Zoomer AR): Automated detection of efficiency anti-patterns with machine learning-driven recommendation systems that generate auto-fix diffs, optimization notebooks, and one-click job re-launches with suggested improvements.
• Multi-Hardware Profiling: Native support across NVIDIA GPUs, AMD MI300X, MTIA, and CPU-only workloads with consistent analysis and optimization recommendations regardless of hardware platform.

Zoomer’s Optimization Impact: From Debugging to Energy Efficiency

Performance debugging with Zoomer creates a cascading effect that transforms low-level optimizations into massive efficiency gains. 

The optimization pathway flows from: identifying bottlenecks → improving key metrics → accelerating workflows → reducing resource consumption → saving energy and costs.

Zoomer’s Training Optimization Pipeline

Zoomer’s training analysis identifies bottlenecks in GPU utilization, memory bandwidth, and communication patterns. 

Example of Training Efficiency Wins: 

• Algorithmic Optimizations: We delivered power savings through systematic efficiency improvements across the training fleet, by fixing reliability issues for low efficiency jobs.
• Training Time Reduction Success: In 2024, we observed a 75% training time reduction for Ads relevance models, leading to 78% reduction in power consumption.
• Memory Optimizations: One-line code changes for performance issues due to inefficient memory copy identified by Zoomer, delivered 20% QPS improvements with minimal engineering effort. 

Inference Optimization Pipeline:

Inference debugging focuses on latency reduction, throughput optimization, and serving efficiency. Zoomer identifies opportunities in kernel execution, memory access patterns, and serving parameter tuning to maximize requests per GPU.

Inference Efficiency Wins:

• GPU and CPU Serving parameters Improvements: Automated GPU and CPU bottleneck identification and parameter tuning, leading to 10% to 45% reduction in power consumption.
• QPS Optimization: GPU trace analysis used to boost serving QPS and optimize serving capacity.

Zoomer’s GenAI and Large-Scale Impact

For massive distributed workloads, even small optimizations compound dramatically. 32k GPU benchmark optimizations achieved 30% speedups through broadcast issue resolution, while 64k GPU configurations delivered 25% speedups in just one day of optimization.

The Future of AI Performance Debugging

As AI workloads expand in size and complexity, Zoomer is advancing to meet new challenges focused on several innovation fronts: broadening unified performance insights across heterogeneous hardware (including MTIA and next-gen accelerators), building advanced analyzers for proactive optimization, enabling inference performance tuning through serving param optimization, and democratizing optimization with automated, intuitive tools for all engineers. As Meta’s AI infrastructure continues its rapid growth, Zoomer plays an important role in helping us innovate efficiently and sustainably.

WILMINGTON, Mass., Nov. 21, 2025 /PRNewswire/ — Analog Devices, Inc. (Nasdaq: ADI) today announced that the Company’s Executive Vice President & Chief Financial Officer, Richard Puccio, will discuss business topics and trends at the UBS Global Technology Conference, taking place at the Phoenician Hotel, located in Scottsdale, Arizona on Tuesday, December 2, 2025, at 10:15 a.m. MST.

The webcast for the conference may be accessed live via the Investor Relations section of Analog Devices’ website at investor.analog.com. An archived replay will also be available following the webcast for at least 30 days.

About Analog Devices, Inc.

Analog Devices, Inc. (NASDAQ: ADI) is a global semiconductor leader that bridges the physical and digital worlds to enable breakthroughs at the Intelligent Edge. ADI combines analog, digital, and software technologies into solutions that help drive advancements in digitized factories, mobility, and digital healthcare, combat climate change, and reliably connect humans and the world. With revenue of more than $9 billion in FY24 and approximately 24,000 people globally, ADI ensures today’s innovators stay Ahead of What’s Possible. Learn more at www.analog.com and on LinkedIn and Twitter (X).

For more information, please contact:
Jeff Ambrosi
Senior Director of Investor Relations
Analog Devices, Inc.
781-461-3282
[email protected]

SOURCE Analog Devices, Inc.

By Gordon Gottsegen

Bitcoin has dropped as much as 35% from its October high

Triple leverage and elevated volatility around bitcoin could be a dangerous combination.

Bitcoin is having a bad week on top of a rough month: The benchmark cryptocurrency is currently down more than 33% from its October all-time high of $126,272.76, wiping out more than $1.2 trillion in market cap.

If you somehow foresaw this huge drop in the price of bitcoin (BTCUSD) and took out a bearish position, you could’ve made a lot of money on your trade. If you guessed incorrectly, you would’ve lost a lot. However, thanks to a new leveraged financial product, traders will be able to up the ante on bitcoin’s swings – either multiplying their money, or potentially losing everything.

Exchange-traded-fund analyst Eric Balchunas posted on X that he spotted listings for new triple-leveraged bitcoin and ether (ETHUSD) ETFs, meaning that traders would be able to triple their exposure to the upside or downside for both cryptocurrencies, depending on which product they trade.

According to Balchunas, these are the first tripled-leverage bitcoin and ether ETFs to launch, and are coming to European markets next week.

Leveraged ETFs have been growing in popularity recently, with ETF providers filing for triple- and even quintuple-leveraged ETFs in the U.S. These products offer traders the ability to multiply earnings on daily price swings, but they also risk taking heavy losses if the underlying asset swings too far in the wrong direction.

“These leveraged products, not only in crypto but stocks also, are nothing more than gambling instruments. The SEC needs to fulfill their duty of investor protection and limit them. What’s to stop issuers from going 5x, 10x or 100x?” Joe Saluzzi, co-head of equity trading at Themis Trading, told MarketWatch.

Leveraged ETFs bring even more risk to volatile assets like crypto. There have been days in the past where specific crypto tokens have more than doubled, or bear markets where they’ve dropped by half. All it takes is a 33% move in the wrong direction for someone trading a triple-leveraged ETF to lose their money.

“If it’s tracking correctly, you’d be down 99%. … You will basically be wiped out,” Todd Sohn, ETF strategist at Strategas Asset Management, told MarketWatch. “The odds of that happening when you are playing with more volatile instruments has clearly increased.”

Some investors in Europe learned this lesson the hard way last month. On Oct. 6, the GraniteShares 3x Short AMD Daily exchange-traded product was terminated by its issuer after shares of Advanced Micro Devices Inc. (AMD) rose by more than 33% intraday. This product was an inverse fund; such funds climb when the price of the underlying asset falls, and decline when the price of the underlying asset rises. So, the 33% intraday swing in AMD shares drove the value of the exchange-traded product to zero.

Although the triple-leveraged bitcoin and ether ETFs would be the first in the market, investors in the U.S. already have access to double-leveraged ETFs like 2x Bitcoin Strategy ETF BITX, ProShares Ultra Bitcoin ETF BITU and T-Rex 2X Long Bitcoin Daily Target ETF BTCL. And on a week like this one, when bitcoin is falling, those ETFs fall even more: All three of them have lost more than 20% this week.

Most of these leveraged financial products are intended to be held for short durations. According to filings for the products, the advertised leverage only applies to daily moves, meaning holding them for longer periods can cause returns to deviate meaningfully from what one might expect. Over the long term, some of these funds have even underperformed their underlying stocks or assets.

Leveraged ETFs appeal to a specific kind of trader with a very high tolerance for risk. But not everyone who comes across these products may know that. That’s why Sohn said it’s important for traders to “know the ingredients” of leveraged ETFs before trading them, adding that investors should only trade what they’re willing to lose.

“If you are betting – trading, whatever you want to call it – on a levered product and you get wiped out, make sure it’s not going to ruin your life,” Sohn said.

-Gordon Gottsegen

This content was created by MarketWatch, which is operated by Dow Jones & Co. MarketWatch is published independently from Dow Jones Newswires and The Wall Street Journal.

  (END) Dow Jones Newswires

  11-21-25 1559ET
Copyright (c) 2025 Dow Jones & Company, Inc.

By Vivien Lou Chen

Developments in Japan are creating a risk that investors in the U.S. Treasury market may one day pull the rug out by keeping more of their savings at home

Why turmoil around Japan’s new government could wash up in U.S. financial markets.

Recent developments overseas have the potential to complicate the White House’s agenda to bring down borrowing costs, while heightening competition for investors in the U.S. and Japanese bond markets.

Aggressive fiscal-stimulus efforts by the cabinet of Japan’s first female prime minister, Sanae Takaichi, have created a spike in long-dated yields of Japanese government bonds and further weakness in the yen (USDJPY) in the past few weeks. It’s a situation that is being likened to the September-October 2022 crisis in the U.K., which stemmed from a crisis in confidence over a package of unfunded tax cuts proposed by then-Prime Minister Liz Truss’s government.

Read: Liz Truss redux? Simultaneous drop for Japanese currency and bonds draws eerie parallels

The U.S. needs to manage the cost of interest payments given a more than $38 trillion national debt, and this is a primary motivation for why the Trump administration wants to bring down long-term Treasury yields. Last week, Treasury Secretary Scott Bessent said in a speech in New York that the U.S. is making substantial progress in keeping most market-based rates down. He also said the 10-year “term premium,” or additional compensation demanded by investors to hold the long-dated maturity, is basically unchanged. Longer-duration yields matter because they provide a peg for borrowing rates used by U.S. households, businesses and the government.

Developments in Japan are now creating the risk that U.S. yields could rise alongside Japan’s yields. This week, Japanese government-bond yields hit their highest levels in almost two decades, with the country’s 10-year rate BX:TMBMKJP-10Y spiking above 1.78% to its highest level in more than 17 years. The 40-year yield BX:TMBMKJP-40Y climbed to an all-time high just above 3.7%.

In the U.S., 2-year BX:TMUBMUSD02Y, 10-year BX:TMUBMUSD10Y and 30-year U.S. yields BX:TMUBMUSD30Y finished Thursday’s session at their lowest levels of the past one to two weeks, and kept falling further on Friday. The benchmark 10-year yield was about 4.06% on Friday.

There’s a risk now that U.S. yields may not fall as much as they otherwise might after factoring in market-implied expectations for a series of interest-rate cuts by the Federal Reserve into 2026.

Japan’s large U.S. footprint

Treasury yields are not going to necessarily follow rates on Japanese government bonds higher “on a one-for-one basis,” but there might be a limit on how low they can go, said Adam Turnquist, chief technical strategist at LPL Financial. He added that the impact of Japanese developments on the U.S. bond market could take years to play out, but “we care now because of the direction Japan’s policy is going in” and the possibility that this impact might occur even sooner.

Some of the catalysts that usually tend to push Treasury yields lower, such as any commentary from U.S. monetary policymakers that suggests the Fed might be inclined to cut rates, “might be muted because of the increased value of foreign debt,” Turnquist added.

U.S. government debt was rallying for a second day on Friday, pushing most yields beyond their lowest levels of the past one or two weeks, after New York Fed President John Williams said there is room to cut interest rates in the near term.

All three major U.S. stock indexes DJIA SPX COMP traded sharply higher Friday, but remained on pace for weekly losses, as investors attempted to calm doubts over the artificial-intelligence trade. Separately, some traders suggested bitcoin (BTCUSD) bets were a factor in Thursday’s stock-market selloff.

The troubling spike in yields on Japanese government bonds hasn’t fully spilled over into the U.S. bond market yet, but it remains a risk. “A repeat of the Truss episode is what people are afraid of,” said Marc Chandler, chief market strategist and managing director at Bannockburn Capital Markets.

Concerns about Japan gained added significance on Friday, when Takaichi’s cabinet approved a 21.3 trillion yen (or roughly $140 billion) economic stimulus package, which Reuters described as lavish. The amount of new spending being injected into the country’s economy from a supplementary budget, much of which is not repurposed from existing funds, is 17.7 trillion yen ($112 billion).

Anxiety over Takaichi’s stimulus efforts has resulted in a Japanese yen that has weakened against its major peers and fallen to a 10-month low ahead of Friday’s session, and in a spike in the country’s long-dated yields. Yields on 30-year BX:TMBMKJP-30Y Japanese government debt have risen this month to 3.33%.

Japan is the biggest foreign holder of Treasurys, with a roughly 13% share, according to the most recent data from the U.S. Treasury Department, and the concern is that the country’s investors might one day pull the rug by keeping more of their savings at home.

Bond-auction anxiety

Earlier in the week, a weak 20-year auction in Japan was cited as one reason why U.S. Treasury yields were a touch lower in early New York trading, which means that demand for U.S. government paper remained in place. Global investors are often incentivized to move their money based on which country offers the highest yields and best overall value.

“The conventional wisdom is that as yields rise in Japan, the Japanese are more likely to keep their savings at home rather than export it,” Chandler said. “The Japanese have been buyers of Treasurys and U.S. stocks, and if they decide to keep their money at home, those U.S. markets could lose a bid.”

For now, Japanese investors, which include insurers and pension funds, appear to be continuing to export their savings by buying more foreign government debt like Treasurys. Data from the U.S. Treasury Department shows that as of September, Japanese investors held just under $1.19 trillion in Treasurys, a number which has been climbing every month this year and is up from about $1.06 trillion last December.

One reason for this is the exchange rate. The yen has depreciated against almost every major currency this year. Japanese investors have been buying U.S. Treasurys because they can diversify against the yen, which is the weakest of the G-10 currencies on an unhedged basis, according to Chandler.

If concerns about the Takaichi government’s stimulus efforts translate into even higher yields in Japan, this could incentivize local investors to keep more of their savings at home, but might also mean rising yields for countries like the U.S.

-Vivien Lou Chen

This content was created by MarketWatch, which is operated by Dow Jones & Co. MarketWatch is published independently from Dow Jones Newswires and The Wall Street Journal.

  (END) Dow Jones Newswires

  11-21-25 1541ET
Copyright (c) 2025 Dow Jones & Company, Inc.

• Tech stocks slumped this week as investors’ skepticism about the AI rally overpowered another strong earnings report from Nvidia, though many experts are optimistic that earnings growth will bring investors back.

• Federal Reserve officials, meanwhile, are deeply divided about what to do at their policy meeting next month, adding uncertainty to an already anxious market.

The stock market is in limbo. It could be there for a while.

After weeks of softness in tech stocks, bulls were hoping a blowout earnings report from Nvidia (NVDA) would revive the faltering AI trade. They got strong earnings—but not the payout. Stocks sold off Thursday as the Cboe Volatility Index (VIX), or the “Fear Index,” jumped to its highest level since April’s tariff debacle.

Stocks rebounded on Friday, but many of Wall Street’s favorite AI stocks—Nvidia, Broadcom (AVGO), Palantir (PLTR), Oracle (ORCL), and Vistra (VST)—fell yet again, indicating AI sentiment remains in the dumps. And market experts are now trying to navigate the road ahead after a week of confusing signals and volatile action.

Tech stocks have fueled the bull market of the past three years, and will have a big impact on market sentiment and stock performance going forward. The Federal Reserve’s interest rate decision next month will also be pivotal in setting a direction for stocks.

The AI rally has been imperiled before. Tech stocks slumped in July 2024 amid concerns about over-investing in AI, but they found their footing and moved higher through the end of the year. Overspending fears resurfaced in January when Chinese startup DeepSeek burst onto the scene. That setback, too, was short-lived.

“We are going through another ‘DeepSeek Moment,’” wrote Wedbush analyst Dan Ives, one of Wall Street’s ardent tech bulls, on Friday. Ives compared today’s AI bubble debate to historical examples of tech skeptics getting it wrong, like dismissals of the iPhone in 2008 and Microsoft’s pivot to cloud computing in 2014.

“This AI Revolution is just beginning today,” he wrote. “We believe tech stocks and the AI winners should be bought given our view this is Year 3 of what will be a 10-year cycle.”

“The big risk to the tech sector—and thus the broader equity market—is not a sudden collapse in valuations,” wrote Barclays analyst Ajay Rajadhyaksha on Thursday. “It is that earnings—which have been on [an] absolute roll over the last 3 years—suddenly start to disappoint, which then sparks an exodus.”

Rajadhyaksha doesn’t think such an outcome is likely, though he concedes there are AI-related risks that investors should keep an eye on. Tech companies are increasingly turning to credit markets to finance their AI investments, which, until recently, have been funded primarily by cash flows. That increases the wider economy’s exposure to the AI boom, and adds to tech’s interest-rate sensitivity. Power constraints, he said, could also force a slowdown in AI spending, possibly dealing a blow to “picks and shovels” suppliers like Nvidia.

“A major change in market leadership appears unlikely absent a significant dislocation in the macro environment,” concludes Rajadhyaksha.

The Federal Reserve’s December policy meeting could be another overhang that keeps stocks wayward in the next few weeks. Policymakers appear deeply divided on how aggressively to lower interest rates. Some see in signs of a weakening labor market good reason to cut rates despite evidence inflation is ticking higher. Their hawkish counterparts say economic uncertainty urges caution. The government shutdown has left behind gaps in official data.

Yesterday’s September jobs report—the last snapshot of the labor market Fed officials will see before their meeting begins Dec. 9—sent conflicting signals. The U.S. added more jobs than expected, but the unemployment rate rose to its highest level in four years. Deutsche Bank economists on Thursday called the report a Rorschach test that gives each camp within the Fed plenty of ammunition to make its case.

Experts say that the Fed’s rate decisions could be decisive in renewing or extinguishing the AI rally. Rate cuts, they argue, would likely fuel the rally by injecting liquidity into the market. If rates remain where they are, tech stocks could struggle to regain their momentum.

Investors are highly uncertain about the Fed’s next steps. Futures market data has the odds of a December rate cut, considered a near certainty a month ago, below 40% yesterday. Those odds jumped back to 70% on Friday after one official indicated he was open to cutting next month.

“In a vacuum of unclear rate and labor-market signals, markets are prone to exaggerated volatility, with short-term trading dominated by sentiment and technical structure,” wrote Bitunix analysts.

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