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1. Is an Ultra Large Natural Image-Based Foundation Model Superior to a Retina-Specific Model for Detecting Ocular and Systemic Diseases?

Author: Hou, Qingshan, Zhou, Yukun, Goh, Jocelyn Hui Lin, Zou, Ke, Yew, Samantha Min Er, Srinivasan, Sahana, Wang, Meng, Lo, Thaddaeus, Lei, Xiaofeng, Wagner, Siegfried K., Chia, Mark A., Yang, Dawei, Jiang, Hongyang, Ran, AnRan, Santos, Rui, Somfai, Gabor Mark, Zhou, Juan Helen, Chen, Haoyu, Chen, Qingyu, Cheung, Carol Yim-Lui, Keane, Pearse A., and Tham, Yih Chung
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract: The advent of foundation models (FMs) is transforming medical domain. In ophthalmology, RETFound, a retina-specific FM pre-trained sequentially on 1.4 million natural images and 1.6 million retinal images, has demonstrated high adaptability across clinical applications. Conversely, DINOv2, a general-purpose vision FM pre-trained on 142 million natural images, has shown promise in non-medical domains. However, its applicability to clinical tasks remains underexplored. To address this, we conducted head-to-head evaluations by fine-tuning RETFound and three DINOv2 models (large, base, small) for ocular disease detection and systemic disease prediction tasks, across eight standardized open-source ocular datasets, as well as the Moorfields AlzEye and the UK Biobank datasets. DINOv2-large model outperformed RETFound in detecting diabetic retinopathy (AUROC=0.850-0.952 vs 0.823-0.944, across three datasets, all P<=0.007) and multi-class eye diseases (AUROC=0.892 vs. 0.846, P<0.001). In glaucoma, DINOv2-base model outperformed RETFound (AUROC=0.958 vs 0.940, P<0.001). Conversely, RETFound achieved superior performance over all DINOv2 models in predicting heart failure, myocardial infarction, and ischaemic stroke (AUROC=0.732-0.796 vs 0.663-0.771, all P<0.001). These trends persisted even with 10% of the fine-tuning data. These findings showcase the distinct scenarios where general-purpose and domain-specific FMs excel, highlighting the importance of aligning FM selection with task-specific requirements to optimise clinical performance.
Published: 2025

2. Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection?

Author: Yew, Samantha Min Er, Lei, Xiaofeng, Goh, Jocelyn Hui Lin, Chen, Yibing, Srinivasan, Sahana, Chee, Miao-li, Pushpanathan, Krithi, Zou, Ke, Hou, Qingshan, Da Soh, Zhi, Xue, Cancan, Yu, Marco Chak Yan, Sabanayagam, Charumathi, Tai, E Shyong, Sim, Xueling, Wang, Yaxing, Jonas, Jost B., Nangia, Vinay, Yang, Gabriel Dawei, Ran, Emma Anran, Cheung, Carol Yim-Lui, Feng, Yangqin, Zhou, Jun, Goh, Rick Siow Mong, Zhou, Yukun, Keane, Pearse A., Liu, Yong, Cheng, Ching-Yu, and Tham, Yih-Chung
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract: Background: RETFound, a self-supervised, retina-specific foundation model (FM), showed potential in downstream applications. However, its comparative performance with traditional deep learning (DL) models remains incompletely understood. This study aimed to evaluate RETFound against three ImageNet-pretrained supervised DL models (ResNet50, ViT-base, SwinV2) in detecting ocular and systemic diseases. Methods: We fine-tuned/trained RETFound and three DL models on full datasets, 50%, 20%, and fixed sample sizes (400, 200, 100 images, with half comprising disease cases; for each DR severity class, 100 and 50 cases were used. Fine-tuned models were tested internally using the SEED (53,090 images) and APTOS-2019 (3,672 images) datasets and externally validated on population-based (BES, CIEMS, SP2, UKBB) and open-source datasets (ODIR-5k, PAPILA, GAMMA, IDRiD, MESSIDOR-2). Model performance was compared using area under the receiver operating characteristic curve (AUC) and Z-tests with Bonferroni correction (P<0.05/3). Interpretation: Traditional DL models are mostly comparable to RETFound for ocular disease detection with large datasets. However, RETFound is superior in systemic disease detection with smaller datasets. These findings offer valuable insights into the respective merits and limitation of traditional models and FMs.
Published: 2025

3. Can OpenAI o1 Reason Well in Ophthalmology? A 6,990-Question Head-to-Head Evaluation Study

Author: Srinivasan, Sahana, Ai, Xuguang, Zou, Minjie, Zou, Ke, Kim, Hyunjae, Lo, Thaddaeus Wai Soon, Pushpanathan, Krithi, Kong, Yiming, Li, Anran, Singer, Maxwell, Jin, Kai, Antaki, Fares, Chen, David Ziyou, Liu, Dianbo, Adelman, Ron A., Chen, Qingyu, and Tham, Yih Chung
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: Question: What is the performance and reasoning ability of OpenAI o1 compared to other large language models in addressing ophthalmology-specific questions? Findings: This study evaluated OpenAI o1 and five LLMs using 6,990 ophthalmological questions from MedMCQA. O1 achieved the highest accuracy (0.88) and macro-F1 score but ranked third in reasoning capabilities based on text-generation metrics. Across subtopics, o1 ranked first in ``Lens'' and ``Glaucoma'' but second to GPT-4o in ``Corneal and External Diseases'', ``Vitreous and Retina'' and ``Oculoplastic and Orbital Diseases''. Subgroup analyses showed o1 performed better on queries with longer ground truth explanations. Meaning: O1's reasoning enhancements may not fully extend to ophthalmology, underscoring the need for domain-specific refinements to optimize performance in specialized fields like ophthalmology., Comment: 44 pages
Published: 2025

4. Enhancing Community Vision Screening -- AI Driven Retinal Photography for Early Disease Detection and Patient Trust

Author: Lei, Xiaofeng, Tham, Yih-Chung, Goh, Jocelyn Hui Lin, Feng, Yangqin, Bai, Yang, Da Soh, Zhi, Goh, Rick Siow Mong, Xu, Xinxing, Liu, Yong, and Cheng, Ching-Yu
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract: Community vision screening plays a crucial role in identifying individuals with vision loss and preventing avoidable blindness, particularly in rural communities where access to eye care services is limited. Currently, there is a pressing need for a simple and efficient process to screen and refer individuals with significant eye disease-related vision loss to tertiary eye care centers for further care. An ideal solution should seamlessly and readily integrate with existing workflows, providing comprehensive initial screening results to service providers, thereby enabling precise patient referrals for timely treatment. This paper introduces the Enhancing Community Vision Screening (ECVS) solution, which addresses the aforementioned concerns with a novel and feasible solution based on simple, non-invasive retinal photography for the detection of pathology-based visual impairment. Our study employs four distinct deep learning models: RETinal photo Quality Assessment (RETQA), Pathology Visual Impairment detection (PVI), Eye Disease Diagnosis (EDD) and Visualization of Lesion Regions of the eye (VLR). We conducted experiments on over 10 datasets, totaling more than 80,000 fundus photos collected from various sources. The models integrated into ECVS achieved impressive AUC scores of 0.98 for RETQA, 0.95 for PVI, and 0.90 for EDD, along with a DICE coefficient of 0.48 for VLR. These results underscore the promising capabilities of ECVS as a straightforward and scalable method for community-based vision screening., Comment: 11 pages, 4 figures, published in MICCAI2024 OMIA XI workshop
Published: 2024

5. LMOD: A Large Multimodal Ophthalmology Dataset and Benchmark for Large Vision-Language Models

Author: Qin, Zhenyue, Yin, Yu, Campbell, Dylan, Wu, Xuansheng, Zou, Ke, Tham, Yih-Chung, Liu, Ninghao, Zhang, Xiuzhen, and Chen, Qingyu
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: The prevalence of vision-threatening eye diseases is a significant global burden, with many cases remaining undiagnosed or diagnosed too late for effective treatment. Large vision-language models (LVLMs) have the potential to assist in understanding anatomical information, diagnosing eye diseases, and drafting interpretations and follow-up plans, thereby reducing the burden on clinicians and improving access to eye care. However, limited benchmarks are available to assess LVLMs' performance in ophthalmology-specific applications. In this study, we introduce LMOD, a large-scale multimodal ophthalmology benchmark consisting of 21,993 instances across (1) five ophthalmic imaging modalities: optical coherence tomography, color fundus photographs, scanning laser ophthalmoscopy, lens photographs, and surgical scenes; (2) free-text, demographic, and disease biomarker information; and (3) primary ophthalmology-specific applications such as anatomical information understanding, disease diagnosis, and subgroup analysis. In addition, we benchmarked 13 state-of-the-art LVLM representatives from closed-source, open-source, and medical domains. The results demonstrate a significant performance drop for LVLMs in ophthalmology compared to other domains. Systematic error analysis further identified six major failure modes: misclassification, failure to abstain, inconsistent reasoning, hallucination, assertions without justification, and lack of domain-specific knowledge. In contrast, supervised neural networks specifically trained on these tasks as baselines demonstrated high accuracy. These findings underscore the pressing need for benchmarks in the development and validation of ophthalmology-specific LVLMs., Comment: 2025 NAACL: Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics Project Page: https://kfzyqin.github.io/lmod/
Published: 2024

6. Language Enhanced Model for Eye (LEME): An Open-Source Ophthalmology-Specific Large Language Model

Author: Gilson, Aidan, Ai, Xuguang, Xie, Qianqian, Srinivasan, Sahana, Pushpanathan, Krithi, Singer, Maxwell B., Huang, Jimin, Kim, Hyunjae, Long, Erping, Wan, Peixing, Del Priore, Luciano V., Ohno-Machado, Lucila, Xu, Hua, Liu, Dianbo, Adelman, Ron A., Tham, Yih-Chung, and Chen, Qingyu
Subjects: Computer Science - Computation and Language
Abstract: Large Language Models (LLMs) are poised to revolutionize healthcare. Ophthalmology-specific LLMs remain scarce and underexplored. We introduced an open-source, specialized LLM for ophthalmology, termed Language Enhanced Model for Eye (LEME). LEME was initially pre-trained on the Llama2 70B framework and further fine-tuned with a corpus of ~127,000 non-copyrighted training instances curated from ophthalmology-specific case reports, abstracts, and open-source study materials. We benchmarked LEME against eight other LLMs, namely, GPT-3.5, GPT-4, three Llama2 models (7B, 13B, 70B), PMC-LLAMA 13B, Meditron 70B, and EYE-Llama (another ophthalmology-specific LLM). Evaluations included four internal validation tasks: abstract completion, fill-in-the-blank, multiple-choice questions (MCQ), and short-answer QA. External validation tasks encompassed long-form QA, MCQ, patient EHR summarization, and clinical QA. Evaluation metrics included Rouge-L scores, accuracy, and expert evaluation of correctness, completeness, and readability. In internal validations, LEME consistently outperformed its counterparts, achieving Rouge-L scores of 0.20 in abstract completion (all p<0.05), 0.82 in fill-in-the-blank (all p<0.0001), and 0.22 in short-answer QA (all p<0.0001, except versus GPT-4). In external validations, LEME excelled in long-form QA with a Rouge-L of 0.19 (all p<0.0001), ranked second in MCQ accuracy (0.68; all p<0.0001), and scored highest in EHR summarization and clinical QA (ranging from 4.24 to 4.83 out of 5 for correctness, completeness, and readability). LEME's emphasis on robust fine-tuning and the use of non-copyrighted data represents a breakthrough in open-source ophthalmology-specific LLMs, offering the potential to revolutionize execution of clinical tasks while democratizing research collaboration.
Published: 2024

7. Block Expanded DINORET: Adapting Natural Domain Foundation Models for Retinal Imaging Without Catastrophic Forgetting

Author: Zoellin, Jay, Merk, Colin, Buob, Mischa, Saad, Amr, Giesser, Samuel, Spitznagel, Tahm, Turgut, Ferhat, Santos, Rui, Zhou, Yukun, Wagner, Sigfried, Keane, Pearse A., Tham, Yih Chung, DeBuc, Delia Cabrera, Becker, Matthias D., and Somfai, Gabor M.
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, I.4.0, I.2.10, J.3
Abstract: Integrating deep learning into medical imaging is poised to greatly advance diagnostic methods but it faces challenges with generalizability. Foundation models, based on self-supervised learning, address these issues and improve data efficiency. Natural domain foundation models show promise for medical imaging, but systematic research evaluating domain adaptation, especially using self-supervised learning and parameter-efficient fine-tuning, remains underexplored. Additionally, little research addresses the issue of catastrophic forgetting during fine-tuning of foundation models. We adapted the DINOv2 vision transformer for retinal imaging classification tasks using self-supervised learning and generated two novel foundation models termed DINORET and BE DINORET. Publicly available color fundus photographs were employed for model development and subsequent fine-tuning for diabetic retinopathy staging and glaucoma detection. We introduced block expansion as a novel domain adaptation strategy and assessed the models for catastrophic forgetting. Models were benchmarked to RETFound, a state-of-the-art foundation model in ophthalmology. DINORET and BE DINORET demonstrated competitive performance on retinal imaging tasks, with the block expanded model achieving the highest scores on most datasets. Block expansion successfully mitigated catastrophic forgetting. Our few-shot learning studies indicated that DINORET and BE DINORET outperform RETFound in terms of data-efficiency. This study highlights the potential of adapting natural domain vision models to retinal imaging using self-supervised learning and block expansion. BE DINORET offers robust performance without sacrificing previously acquired capabilities. Our findings suggest that these methods could enable healthcare institutions to develop tailored vision models for their patient populations, enhancing global healthcare inclusivity., Comment: J.Zoellin, C. Merk and M. Buob contributed equally as shared-first authors. D. Cabrera DeBuc, M. D. Becker and G. M. Somfai contributed equally as senior authors for this work
Published: 2024

8. Enhancing Large Language Models with Domain-specific Retrieval Augment Generation: A Case Study on Long-form Consumer Health Question Answering in Ophthalmology

Author: Gilson, Aidan, Ai, Xuguang, Arunachalam, Thilaka, Chen, Ziyou, Cheong, Ki Xiong, Dave, Amisha, Duic, Cameron, Kibe, Mercy, Kaminaka, Annette, Prasad, Minali, Siddig, Fares, Singer, Maxwell, Wong, Wendy, Jin, Qiao, Keenan, Tiarnan D. L., Hu, Xia, Chew, Emily Y., Lu, Zhiyong, Xu, Hua, Adelman, Ron A., Tham, Yih-Chung, and Chen, Qingyu
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: Despite the potential of Large Language Models (LLMs) in medicine, they may generate responses lacking supporting evidence or based on hallucinated evidence. While Retrieval Augment Generation (RAG) is popular to address this issue, few studies implemented and evaluated RAG in downstream domain-specific applications. We developed a RAG pipeline with 70,000 ophthalmology-specific documents that retrieve relevant documents to augment LLMs during inference time. In a case study on long-form consumer health questions, we systematically evaluated the responses including over 500 references of LLMs with and without RAG on 100 questions with 10 healthcare professionals. The evaluation focuses on factuality of evidence, selection and ranking of evidence, attribution of evidence, and answer accuracy and completeness. LLMs without RAG provided 252 references in total. Of which, 45.3% hallucinated, 34.1% consisted of minor errors, and 20.6% were correct. In contrast, LLMs with RAG significantly improved accuracy (54.5% being correct) and reduced error rates (18.8% with minor hallucinations and 26.7% with errors). 62.5% of the top 10 documents retrieved by RAG were selected as the top references in the LLM response, with an average ranking of 4.9. The use of RAG also improved evidence attribution (increasing from 1.85 to 2.49 on a 5-point scale, P<0.001), albeit with slight decreases in accuracy (from 3.52 to 3.23, P=0.03) and completeness (from 3.47 to 3.27, P=0.17). The results demonstrate that LLMs frequently exhibited hallucinated and erroneous evidence in the responses, raising concerns for downstream applications in the medical domain. RAG substantially reduced the proportion of such evidence but encountered challenges.
Published: 2024

9. Common and Rare Fundus Diseases Identification Using Vision-Language Foundation Model with Knowledge of Over 400 Diseases

Author: Wang, Meng, Lin, Tian, Lin, Aidi, Yu, Kai, Peng, Yuanyuan, Wang, Lianyu, Chen, Cheng, Zou, Ke, Liang, Huiyu, Chen, Man, Yao, Xue, Zhang, Meiqin, Huang, Binwei, Zheng, Chaoxin, Zhang, Peixin, Chen, Wei, Luo, Yilong, Chen, Yifan, Xia, Honghe, Shi, Tingkun, Zhang, Qi, Guo, Jinming, Chen, Xiaolin, Wang, Jingcheng, Tham, Yih Chung, Liu, Dianbo, Wong, Wendy, Thakur, Sahil, Fenner, Beau, Fang, Danqi, Liu, Siying, Liu, Qingyun, Huang, Yuqiang, Zeng, Hongqiang, Meng, Yanda, Zhou, Yukun, Jiang, Zehua, Qiu, Minghui, Zhang, Changqing, Chen, Xinjian, Wang, Sophia Y, Lee, Cecilia S, Sobrin, Lucia, Cheung, Carol Y, Pang, Chi Pui, Keane, Pearse A, Cheng, Ching-Yu, Chen, Haoyu, and Fu, Huazhu
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract: Previous foundation models for retinal images were pre-trained with limited disease categories and knowledge base. Here we introduce RetiZero, a vision-language foundation model that leverages knowledge from over 400 fundus diseases. To RetiZero's pre-training, we compiled 341,896 fundus images paired with text descriptions, sourced from public datasets, ophthalmic literature, and online resources, encompassing a diverse range of diseases across multiple ethnicities and countries. RetiZero exhibits superior performance in several downstream tasks, including zero-shot disease recognition, image-to-image retrieval, and internal- and cross-domain disease identification. In zero-shot scenarios, RetiZero achieves Top5 accuracy scores of 0.8430 for 15 fundus diseases and 0.7561 for 52 fundus diseases. For image retrieval, it achieves Top5 scores of 0.9500 and 0.8860 for the same disease sets, respectively. Clinical evaluations show that RetiZero's Top3 zero-shot performance surpasses the average of 19 ophthalmologists from Singapore, China and the United States. Furthermore, RetiZero significantly enhances clinicians' accuracy in diagnosing fundus disease. These findings underscore the value of integrating the RetiZero foundation model into clinical settings, where a variety of fundus diseases are encountered.
Published: 2024

10. HRDC challenge: a public benchmark for hypertension and hypertensive retinopathy classification from fundus images: HRDC challenge: a public benchmark for hypertension...

Author: Qian, Bo, Wang, Xiangning, Guan, Zhouyu, Yang, Dawei, Ran, Anran, Li, Tingyao, Wang, Zheyuan, Wen, Yang, Shu, Xinming, Xie, Jinyang, Liu, Shichang, Xing, Guanyu, Silva-Rodríguez, Julio, Kobbi, Riadh, Li, Ping, Chen, Tingli, Bi, Lei, Kim, Jinman, Jia, Weiping, Li, Huating, Qin, Jing, Zhang, Ping, Cheng, Ching-Yu, Heng, Pheng-Ann, Wong, Tien Yin, Cheung, Carol Y., Tham, Yih-Chung, Thalmann, Nadia Magnenat, and Sheng, Bin
Published: 2025
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11. A Peer-to-Peer Live-Streaming Intervention for Children During COVID-19 Homeschooling to Promote Physical Activity and Reduce Anxiety and Eye Strain: Cluster Randomized Controlled Trial

Author: Zheng, Yingfeng, Wang, Wei, Zhong, Yuxin, Wu, Fengchun, Zhu, Zhuoting, Tham, Yih-Chung, Lamoureux, Ecosse, Xiao, Liang, Zhu, Erta, Liu, Haoning, Jin, Ling, Liang, Linyi, Luo, Lixia, He, Mingguang, Morgan, Ian, Congdon, Nathan, and Liu, Yizhi
Subjects: Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract: BackgroundThe COVID-19 pandemic has led to worldwide school closures, with millions of children confined to online learning at home. As a result, children may be susceptible to anxiety and digital eye strain, highlighting a need for population interventions. ObjectiveThe objective of our study was to investigate whether a digital behavior change intervention aimed at promoting physical activity could reduce children’s anxiety and digital eye strain while undergoing prolonged homeschooling during the COVID-19 pandemic. MethodsIn this cluster randomized controlled trial, homeschooled grade 7 students at 12 middle schools in southern China were recruited through local schools and randomly assigned by the school to receive (1:1 allocation): (1) health education information promoting exercise and ocular relaxation, and access to a digital behavior change intervention, with live streaming and peer sharing of promoted activities (intervention), or (2) health education information only (control). The primary outcome was change in self-reported anxiety score. Secondary outcomes included change in self-reported eye strain and sleep quality. ResultsOn March 16, 2020, 1009 children were evaluated, and 954 (94.5%) eligible children of consenting families were included in the intention-to-treat analysis. Children in the intervention (n=485, 6 schools) and control (n=469, 6 schools) groups were aged 13.5 (SD 0.5) years, and 52.3% (n=499) were male. The assigned interventions were completed by 896 children (intervention: n=467, 96.3%; control: n=429, 91.5%). The 2-week change in square-root–transformed self-reported anxiety scores was greater in the intervention (–0.23, 95% CI –0.27 to –0.20) vs control group (0.12, 95% CI 0.09-0.16; unadjusted difference –0.36, 95% CI –0.63 to –0.08; P=.02). There was a significant reduction in square-root–transformed eye strain in the intervention group (–0.08, 95% CI –0.10 to 0.06) compared to controls (0.07, 95% CI 0.05-0.09; difference –0.15, 95% CI –0.26 to –0.03; P=.02). Change in sleep quality was similar between the two groups. ConclusionsThis digital behavior change intervention reduced children’s anxiety and eye strain during COVID-19–associated online schooling. Trial RegistrationClinicalTrials.gov NCT04309097; http://clinicaltrials.gov/ct2/show/NCT04309097
Published: 2021
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12. Associations between shorter leucocyte telomere length and increased risk of age-related macular degeneration in women: insights from the United Kingdom Biobank study

Author: Xue, Can Can, Nusinovici, Simon, Yu, Marco, Chee, Miao-Li, Teo, Kelvin, Su, Xinyi, Cheung, Chui Ming Gemmy, Sabanayagam, Charumathi, Cheng, Ching-Yu, and Tham, Yih-Chung
Published: 2024
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13. Telehealth Demand Trends During the COVID-19 Pandemic in the Top 50 Most Affected Countries: Infodemiological Evaluation

Author: Wong, Mark Yu Zheng, Gunasekeran, Dinesh Visva, Nusinovici, Simon, Sabanayagam, Charumathi, Yeo, Khung Keong, Cheng, Ching-Yu, and Tham, Yih-Chung
Subjects: Public aspects of medicine, RA1-1270
Abstract: BackgroundThe COVID-19 pandemic has led to urgent calls for the adoption of telehealth solutions. However, public interest and demand for telehealth during the pandemic remain unknown. ObjectiveWe used an infodemiological approach to estimate the worldwide demand for telehealth services during COVID-19, focusing on the 50 most affected countries and comparing the demand for such services with the level of information and communications technology (ICT) infrastructure available. MethodsWe used Google Trends, the Baidu Index (China), and Yandex Keyword Statistics (Russia) to extract data on worldwide and individual countries’ telehealth-related internet searches from January 1 to July 7, 2020, presented as relative search volumes (RSV; range 0-100). Daily COVID-19 cases and deaths were retrieved from the World Health Organization. Individual countries’ ICT infrastructure profiles were retrieved from the World Economic Forum Report. ResultsAcross the 50 countries, the mean RSV was 18.5 (SD 23.2), and the mean ICT index was 62.1 (SD 15.0). An overall spike in worldwide telehealth-related RSVs was observed from March 11, 2020 (RSV peaked to 76.0), which then tailed off in June-July 2020 (mean RSV for the period was 25.8), but remained higher than pre-March RSVs (mean 7.29). By country, 42 (84%) manifested increased RSVs over the evaluation period, with the highest observed in Canada (RSV=100) and the United States (RSV=96). When evaluating associations between RSV and the ICT index, both the United States and Canada demonstrated high RSVs and ICT scores (≥70.3). In contrast, European countries had relatively lower RSVs (range 3.4-19.5) despite high ICT index scores (mean 70.3). Several Latin American (Brazil, Chile, Colombia) and South Asian (India, Bangladesh, Pakistan) countries demonstrated relatively higher RSVs (range 13.8-73.3) but low ICT index scores (mean 44.6), indicating that the telehealth demand outstrips the current ICT infrastructure. ConclusionsThere is generally increased interest and demand for telehealth services across the 50 countries most affected by COVID-19, highlighting the need to scale up telehealth capabilities, during and beyond the pandemic.
Published: 2021
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14. Enhancing Community Vision Screening: AI-Driven Retinal Photography for Early Disease Detection and Patient Trust

Author: Lei, Xiaofeng, Tham, Yih-Chung, Goh, Jocelyn Hui Lin, Feng, Yangqin, Bai, Yang, Soh, Zhi Da, Goh, Rick Siow Mong, Xu, Xinxing, Liu, Yong, Cheng, Ching-Yu, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bhavna, Antony, editor, Chen, Hao, editor, Fang, Huihui, editor, Fu, Huazhu, editor, and Lee, Cecilia S., editor
Published: 2025
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15. EyeFound: A Multimodal Generalist Foundation Model for Ophthalmic Imaging

Author: Shi, Danli, Zhang, Weiyi, Chen, Xiaolan, Liu, Yexin, Yang, Jiancheng, Huang, Siyu, Tham, Yih Chung, Zheng, Yingfeng, and He, Mingguang
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract: Artificial intelligence (AI) is vital in ophthalmology, tackling tasks like diagnosis, classification, and visual question answering (VQA). However, existing AI models in this domain often require extensive annotation and are task-specific, limiting their clinical utility. While recent developments have brought about foundation models for ophthalmology, they are limited by the need to train separate weights for each imaging modality, preventing a comprehensive representation of multi-modal features. This highlights the need for versatile foundation models capable of handling various tasks and modalities in ophthalmology. To address this gap, we present EyeFound, a multimodal foundation model for ophthalmic images. Unlike existing models, EyeFound learns generalizable representations from unlabeled multimodal retinal images, enabling efficient model adaptation across multiple applications. Trained on 2.78 million images from 227 hospitals across 11 ophthalmic modalities, EyeFound facilitates generalist representations and diverse multimodal downstream tasks, even for detecting challenging rare diseases. It outperforms previous work RETFound in diagnosing eye diseases, predicting systemic disease incidents, and zero-shot multimodal VQA. EyeFound provides a generalizable solution to improve model performance and lessen the annotation burden on experts, facilitating widespread clinical AI applications for retinal imaging., Comment: 21 pages, 2 figures, 4 tables
Published: 2024

16. A Clinical-oriented Multi-level Contrastive Learning Method for Disease Diagnosis in Low-quality Medical Images

Author: Hou, Qingshan, Cheng, Shuai, Cao, Peng, Yang, Jinzhu, Liu, Xiaoli, Zaiane, Osmar R., and Tham, Yih Chung
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Representation learning offers a conduit to elucidate distinctive features within the latent space and interpret the deep models. However, the randomness of lesion distribution and the complexity of low-quality factors in medical images pose great challenges for models to extract key lesion features. Disease diagnosis methods guided by contrastive learning (CL) have shown significant advantages in lesion feature representation. Nevertheless, the effectiveness of CL is highly dependent on the quality of the positive and negative sample pairs. In this work, we propose a clinical-oriented multi-level CL framework that aims to enhance the model's capacity to extract lesion features and discriminate between lesion and low-quality factors, thereby enabling more accurate disease diagnosis from low-quality medical images. Specifically, we first construct multi-level positive and negative pairs to enhance the model's comprehensive recognition capability of lesion features by integrating information from different levels and qualities of medical images. Moreover, to improve the quality of the learned lesion embeddings, we introduce a dynamic hard sample mining method based on self-paced learning. The proposed CL framework is validated on two public medical image datasets, EyeQ and Chest X-ray, demonstrating superior performance compared to other state-of-the-art disease diagnostic methods.
Published: 2024

17. Pilot and feasibility studies in ophthalmology: fundamental keys to success

Author: Nanji, Keean, Phillips, Mark, Thabane, Lehana, Tham, Yih Chung, Wong, Tien Yin, Steel, David H., Munk, Marion R., Wykoff, Charles C., and Chaudhary, Varun
Published: 2025
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18. Integrated image-based deep learning and language models for primary diabetes care

Author: Li, Jiajia, Guan, Zhouyu, Wang, Jing, Cheung, Carol Y., Zheng, Yingfeng, Lim, Lee-Ling, Lim, Cynthia Ciwei, Ruamviboonsuk, Paisan, Raman, Rajiv, Corsino, Leonor, Echouffo-Tcheugui, Justin B., Luk, Andrea O. Y., Chen, Li Jia, Sun, Xiaodong, Hamzah, Haslina, Wu, Qiang, Wang, Xiangning, Liu, Ruhan, Wang, Ya Xing, Chen, Tingli, Zhang, Xiao, Yang, Xiaolong, Yin, Jun, Wan, Jing, Du, Wei, Quek, Ten Cheer, Goh, Jocelyn Hui Lin, Yang, Dawei, Hu, Xiaoyan, Nguyen, Truong X., Szeto, Simon K. H., Chotcomwongse, Peranut, Malek, Rachid, Normatova, Nargiza, Ibragimova, Nilufar, Srinivasan, Ramyaa, Zhong, Pingting, Huang, Wenyong, Deng, Chenxin, Ruan, Lei, Zhang, Cuntai, Zhang, Chenxi, Zhou, Yan, Wu, Chan, Dai, Rongping, Koh, Sky Wei Chee, Abdullah, Adina, Hee, Nicholas Ken Yoong, Tan, Hong Chang, Liew, Zhong Hong, Tien, Carolyn Shan-Yeu, Kao, Shih Ling, Lim, Amanda Yuan Ling, Mok, Shao Feng, Sun, Lina, Gu, Jing, Wu, Liang, Li, Tingyao, Cheng, Di, Wang, Zheyuan, Qin, Yiming, Dai, Ling, Meng, Ziyao, Shu, Jia, Lu, Yuwei, Jiang, Nan, Hu, Tingting, Huang, Shan, Huang, Gengyou, Yu, Shujie, Liu, Dan, Ma, Weizhi, Guo, Minyi, Guan, Xinping, Yang, Xiaokang, Bascaran, Covadonga, Cleland, Charles R., Bao, Yuqian, Ekinci, Elif I., Jenkins, Alicia, Chan, Juliana C. N., Bee, Yong Mong, Sivaprasad, Sobha, Shaw, Jonathan E., Simó, Rafael, Keane, Pearse A., Cheng, Ching-Yu, Tan, Gavin Siew Wei, Jia, Weiping, Tham, Yih-Chung, Li, Huating, Sheng, Bin, and Wong, Tien Yin
Published: 2024
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19. A Catalogue of Structural Variation across Ancestrally Diverse Asian Genomes

Author: Tan, Joanna Hui Juan, Li, Zhihui, Porta, Mar Gonzalez, Rajaby, Ramesh, Lim, Weng Khong, Tan, Ye An, Jimenez, Rodrigo Toro, Teo, Renyi, Hebrard, Maxime, Ow, Jack Ling, Ang, Shimin, Jeyakani, Justin, Chong, Yap Seng, Lim, Tock Han, Goh, Liuh Ling, Tham, Yih Chung, Leong, Khai Pang, Chin, Calvin Woon Loong, Davila, Sonia, Karnani, Neerja, Cheng, Ching-Yu, Chambers, John, Tai, E. Shyong, Liu, Jianjun, Sim, Xueling, Sung, Wing Kin, Prabhakar, Shyam, Tan, Patrick, and Bertin, Nicolas
Published: 2024
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20. Correction: Prognostic potentials of AI in ophthalmology: systemic disease forecasting via retinal imaging

Author: Tan, Yong Yu, Kang, Hyun Goo, Lee, Chan Joo, Kim, Sung Soo, Park, Sungha, Thakur, Sahil, Da Soh, Zhi, Cho, Yunnie, Peng, Qingsheng, Lee, Kwanghyun, Tham, Yih-Chung, Rim, Tyler Hyungtaek, and Cheng, Ching-Yu
Published: 2024
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21. Prognostic potentials of AI in ophthalmology: systemic disease forecasting via retinal imaging

Author: Tan, Yong Yu, Kang, Hyun Goo, Lee, Chan Joo, Kim, Sung Soo, Park, Sungha, Thakur, Sahil, Da Soh, Zhi, Cho, Yunnie, Peng, Qingsheng, Lee, Kwanghyun, Tham, Yih-Chung, Rim, Tyler Hyungtaek, and Cheng, Ching-yu
Published: 2024
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22. X-chromosome and kidney function: evidence from a multi-trait genetic analysis of 908,697 individuals reveals sex-specific and sex-differential findings in genes regulated by androgen response elements

Author: Scholz, Markus, Horn, Katrin, Pott, Janne, Wuttke, Matthias, Kühnapfel, Andreas, Nasr, M. Kamal, Kirsten, Holger, Li, Yong, Hoppmann, Anselm, Gorski, Mathias, Ghasemi, Sahar, Li, Man, Tin, Adrienne, Chai, Jin-Fang, Cocca, Massimiliano, Wang, Judy, Nutile, Teresa, Akiyama, Masato, Åsvold, Bjørn Olav, Bansal, Nisha, Biggs, Mary L., Boutin, Thibaud, Brenner, Hermann, Brumpton, Ben, Burkhardt, Ralph, Cai, Jianwen, Campbell, Archie, Campbell, Harry, Chalmers, John, Chasman, Daniel I., Chee, Miao Ling, Chee, Miao Li, Chen, Xu, Cheng, Ching-Yu, Cifkova, Renata, Daviglus, Martha, Delgado, Graciela, Dittrich, Katalin, Edwards, Todd L., Endlich, Karlhans, Michael Gaziano, J., Giri, Ayush, Giulianini, Franco, Gordon, Scott D., Gudbjartsson, Daniel F., Hallan, Stein, Hamet, Pavel, Hartman, Catharina A., Hayward, Caroline, Heid, Iris M., Hellwege, Jacklyn N., Holleczek, Bernd, Holm, Hilma, Hutri-Kähönen, Nina, Hveem, Kristian, Isermann, Berend, Jonas, Jost B., Joshi, Peter K., Kamatani, Yoichiro, Kanai, Masahiro, Kastarinen, Mika, Khor, Chiea Chuen, Kiess, Wieland, Kleber, Marcus E., Körner, Antje, Kovacs, Peter, Krajcoviechova, Alena, Kramer, Holly, Krämer, Bernhard K., Kuokkanen, Mikko, Kähönen, Mika, Lange, Leslie A., Lash, James P., Lehtimäki, Terho, Li, Hengtong, Lin, Bridget M., Liu, Jianjun, Loeffler, Markus, Lyytikäinen, Leo-Pekka, Magnusson, Patrik K. E., Martin, Nicholas G., Matsuda, Koichi, Milaneschi, Yuri, Mishra, Pashupati P., Mononen, Nina, Montgomery, Grant W., Mook-Kanamori, Dennis O., Mychaleckyj, Josyf C., März, Winfried, Nauck, Matthias, Nikus, Kjell, Nolte, Ilja M., Noordam, Raymond, Okada, Yukinori, Olafsson, Isleifur, Oldehinkel, Albertine J., Penninx, Brenda W. J. H., Perola, Markus, Pirastu, Nicola, Polasek, Ozren, Porteous, David J., Poulain, Tanja, Psaty, Bruce M., Rabelink, Ton J., Raffield, Laura M., Raitakari, Olli T., Rasheed, Humaira, Reilly, Dermot F., Rice, Kenneth M., Richmond, Anne, Ridker, Paul M., Rotter, Jerome I., Rudan, Igor, Sabanayagam, Charumathi, Salomaa, Veikko, Schneiderman, Neil, Schöttker, Ben, Sims, Mario, Snieder, Harold, Stark, Klaus J., Stefansson, Kari, Stocker, Hannah, Stumvoll, Michael, Sulem, Patrick, Sveinbjornsson, Gardar, Svensson, Per O., Tai, E-Shyong, Taylor, Kent D., Tayo, Bamidele O., Teren, Andrej, Tham, Yih-Chung, Thiery, Joachim, Thio, Chris H. L., Thomas, Laurent F., Tremblay, Johanne, Tönjes, Anke, van der Most, Peter J., Vitart, Veronique, Völker, Uwe, Wang, Ya Xing, Wang, Chaolong, Wei, Wen Bin, Whitfield, John B., Wild, Sarah H., Wilson, James F., Winkler, Thomas W., Wong, Tien-Yin, Woodward, Mark, Sim, Xueling, Chu, Audrey Y., Feitosa, Mary F., Thorsteinsdottir, Unnur, Hung, Adriana M., Teumer, Alexander, Franceschini, Nora, Parsa, Afshin, Köttgen, Anna, Schlosser, Pascal, and Pattaro, Cristian
Published: 2024
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23. VisionFM: a Multi-Modal Multi-Task Vision Foundation Model for Generalist Ophthalmic Artificial Intelligence

Author: Qiu, Jianing, Wu, Jian, Wei, Hao, Shi, Peilun, Zhang, Minqing, Sun, Yunyun, Li, Lin, Liu, Hanruo, Liu, Hongyi, Hou, Simeng, Zhao, Yuyang, Shi, Xuehui, Xian, Junfang, Qu, Xiaoxia, Zhu, Sirui, Pan, Lijie, Chen, Xiaoniao, Zhang, Xiaojia, Jiang, Shuai, Wang, Kebing, Yang, Chenlong, Chen, Mingqiang, Fan, Sujie, Hu, Jianhua, Lv, Aiguo, Miao, Hui, Guo, Li, Zhang, Shujun, Pei, Cheng, Fan, Xiaojuan, Lei, Jianqin, Wei, Ting, Duan, Junguo, Liu, Chun, Xia, Xiaobo, Xiong, Siqi, Li, Junhong, Lo, Benny, Tham, Yih Chung, Wong, Tien Yin, Wang, Ningli, and Yuan, Wu
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract: We present VisionFM, a foundation model pre-trained with 3.4 million ophthalmic images from 560,457 individuals, covering a broad range of ophthalmic diseases, modalities, imaging devices, and demography. After pre-training, VisionFM provides a foundation to foster multiple ophthalmic artificial intelligence (AI) applications, such as disease screening and diagnosis, disease prognosis, subclassification of disease phenotype, and systemic biomarker and disease prediction, with each application enhanced with expert-level intelligence and accuracy. The generalist intelligence of VisionFM outperformed ophthalmologists with basic and intermediate levels in jointly diagnosing 12 common ophthalmic diseases. Evaluated on a new large-scale ophthalmic disease diagnosis benchmark database, as well as a new large-scale segmentation and detection benchmark database, VisionFM outperformed strong baseline deep neural networks. The ophthalmic image representations learned by VisionFM exhibited noteworthy explainability, and demonstrated strong generalizability to new ophthalmic modalities, disease spectrum, and imaging devices. As a foundation model, VisionFM has a large capacity to learn from diverse ophthalmic imaging data and disparate datasets. To be commensurate with this capacity, in addition to the real data used for pre-training, we also generated and leveraged synthetic ophthalmic imaging data. Experimental results revealed that synthetic data that passed visual Turing tests, can also enhance the representation learning capability of VisionFM, leading to substantial performance gains on downstream ophthalmic AI tasks. Beyond the ophthalmic AI applications developed, validated, and demonstrated in this work, substantial further applications can be achieved in an efficient and cost-effective manner using VisionFM as the foundation.
Published: 2023

24. Association between sleep and myopia in children and adolescents: a systematic review and meta-analysis

Author: Jin, Eric, Lee, Chae Eun, Li, Hengtong, Tham, Yih-Chung, and Chen, David Z.
Published: 2024
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25. AI-enhanced digital technologies for myopia management: advancements, challenges, and future prospects

Author: Ali, Saba Ghazanfar, Zhang, Chenxi, Guan, Zhouyu, Chen, Tingli, Wu, Qiang, Li, Ping, Yang, Po, Ghazanfar, Zainab, Jung, Younhyun, Chen, Yuting, Sheng, Bin, Tham, Yih-Chung, Wang, Xiangning, and Wen, Yang
Published: 2024
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26. Sex Differences in Cardiovascular-Kidney-Metabolic Syndrome: 30-Year US Trends and Mortality Risks

Author: Ji, Hongwei, Sabanayagam, Charumathi, Matsushita, Kunihiro, Cheng, Ching-Yu, Rim, Tyler Hyungtaek, Sheng, Bin, Li, Huating, Tham, Yih-Chung, Cheng, Susan, and Wong, Tien-Yin
Published: 2024
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27. Genetic insights into resting heart rate and its role in cardiovascular disease.

Author: van de Vegte, Yordi, Eppinga, Ruben, van der Ende, M, Hagemeijer, Yanick, Mahendran, Yuvaraj, Salfati, Elias, Smith, Albert, Tan, Vanessa, Arking, Dan, Ntalla, Ioanna, Appel, Emil, Schurmann, Claudia, Brody, Jennifer, Rueedi, Rico, Polasek, Ozren, Sveinbjornsson, Gardar, Lecoeur, Cecile, Ladenvall, Claes, Zhao, Jing, Isaacs, Aaron, Wang, Lihua, Luan, Jianan, Hwang, Shih-Jen, Mononen, Nina, Auro, Kirsi, Jackson, Anne, Bielak, Lawrence, Zeng, Linyao, Shah, Nabi, Nethander, Maria, Campbell, Archie, Rankinen, Tuomo, Pechlivanis, Sonali, Qi, Lu, Zhao, Wei, Rizzi, Federica, Tanaka, Toshiko, Robino, Antonietta, Cocca, Massimiliano, Lange, Leslie, Müller-Nurasyid, Martina, Roselli, Carolina, Zhang, Weihua, Kleber, Marcus, Guo, Xiuqing, Lin, Henry, Pavani, Francesca, Galesloot, Tessel, Noordam, Raymond, Milaneschi, Yuri, Schraut, Katharina, den Hoed, Marcel, Degenhardt, Frauke, Trompet, Stella, van den Berg, Marten, Pistis, Giorgio, Tham, Yih-Chung, Weiss, Stefan, Sim, Xueling, Li, Hengtong, van der Most, Peter, Nolte, Ilja, Lyytikäinen, Leo-Pekka, Said, M, Witte, Daniel, Iribarren, Carlos, Launer, Lenore, Ring, Susan, de Vries, Paul, Sever, Peter, Linneberg, Allan, Bottinger, Erwin, Padmanabhan, Sandosh, Psaty, Bruce, Sotoodehnia, Nona, Kolcic, Ivana, Arnar, David, Gudbjartsson, Daniel, Holm, Hilma, Balkau, Beverley, Silva, Claudia, Newton-Cheh, Christopher, Nikus, Kjell, Salo, Perttu, Mohlke, Karen, Peyser, Patricia, Schunkert, Heribert, Lorentzon, Mattias, Lahti, Jari, Rao, Dabeeru, Cornelis, Marilyn, Faul, Jessica, Smith, Jennifer, Stolarz-Skrzypek, Katarzyna, Bandinelli, Stefania, Concas, Maria, Sinagra, Gianfranco, Meitinger, Thomas, Waldenberger, Melanie, and Sinner, Moritz
Subjects: Humans, Cardiovascular Diseases, Risk Factors, Heart Rate, Genetic Predisposition to Disease, Atrial Fibrillation, Mendelian Randomization Analysis, Genome-Wide Association Study, Polymorphism, Single Nucleotide
Abstract: Resting heart rate is associated with cardiovascular diseases and mortality in observational and Mendelian randomization studies. The aims of this study are to extend the number of resting heart rate associated genetic variants and to obtain further insights in resting heart rate biology and its clinical consequences. A genome-wide meta-analysis of 100 studies in up to 835,465 individuals reveals 493 independent genetic variants in 352 loci, including 68 genetic variants outside previously identified resting heart rate associated loci. We prioritize 670 genes and in silico annotations point to their enrichment in cardiomyocytes and provide insights in their ECG signature. Two-sample Mendelian randomization analyses indicate that higher genetically predicted resting heart rate increases risk of dilated cardiomyopathy, but decreases risk of developing atrial fibrillation, ischemic stroke, and cardio-embolic stroke. We do not find evidence for a linear or non-linear genetic association between resting heart rate and all-cause mortality in contrast to our previous Mendelian randomization study. Systematic alteration of key differences between the current and previous Mendelian randomization study indicates that the most likely cause of the discrepancy between these studies arises from false positive findings in previous one-sample MR analyses caused by weak-instrument bias at lower P-value thresholds. The results extend our understanding of resting heart rate biology and give additional insights in its role in cardiovascular disease development.
Published: 2023

28. Localizing Anatomical Landmarks in Ocular Images using Zoom-In Attentive Networks

Author: Lei, Xiaofeng, Li, Shaohua, Xu, Xinxing, Fu, Huazhu, Liu, Yong, Tham, Yih-Chung, Feng, Yangqin, Tan, Mingrui, Xu, Yanyu, Goh, Jocelyn Hui Lin, Goh, Rick Siow Mong, and Cheng, Ching-Yu
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract: Localizing anatomical landmarks are important tasks in medical image analysis. However, the landmarks to be localized often lack prominent visual features. Their locations are elusive and easily confused with the background, and thus precise localization highly depends on the context formed by their surrounding areas. In addition, the required precision is usually higher than segmentation and object detection tasks. Therefore, localization has its unique challenges different from segmentation or detection. In this paper, we propose a zoom-in attentive network (ZIAN) for anatomical landmark localization in ocular images. First, a coarse-to-fine, or "zoom-in" strategy is utilized to learn the contextualized features in different scales. Then, an attentive fusion module is adopted to aggregate multi-scale features, which consists of 1) a co-attention network with a multiple regions-of-interest (ROIs) scheme that learns complementary features from the multiple ROIs, 2) an attention-based fusion module which integrates the multi-ROIs features and non-ROI features. We evaluated ZIAN on two open challenge tasks, i.e., the fovea localization in fundus images and scleral spur localization in AS-OCT images. Experiments show that ZIAN achieves promising performances and outperforms state-of-the-art localization methods. The source code and trained models of ZIAN are available at https://github.com/leixiaofeng-astar/OMIA9-ZIAN.
Published: 2022

29. Large language models for diabetes training: a prospective study

Author: Li, Haoxuan, Jiang, Zehua, Guan, Zhouyu, Bao, Yuqian, Liu, Yuexing, Hu, Tingting, Li, Jiajia, Liu, Ruhan, Wu, Liang, Cheng, Di, Ji, Hongwei, Wang, Yong, Wang, Ya-Xing, Cheung, Carol Y., Zheng, Yingfeng, Wang, Jihong, Li, Zhen, Wu, Weibing, Lim, Cynthia Ciwei, Bee, Yong Mong, Tan, Hong Chang, Ekinci, Elif I., Klonoff, David C., Echouffo-Tcheugui, Justin B., Mathioudakis, Nestoras, Corsino, Leonor, Simó, Rafael, Sabanayagam, Charumathi, Tan, Gavin Siew Wei, Cheng, Ching-Yu, Wong, Tien Yin, Li, Huating, Cai, Chun, Mao, Lijuan, Lim, Lee-Ling, Tham, Yih-Chung, Sheng, Bin, and Jia, Weiping
Published: 2025
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30. Interplay between Lipids and Complement Proteins—How Multiomics Data Integration Can Help Unravel Age-related Macular Degeneration Pathophysiology: A Proof-of-concept Study

Author: Nusinovici, Simon, Zhou, Lei, Raghavan, Lavanya, Tham, Yih Chung, Li, Hengtong, Cheung, Danny, Wang, Xiaomeng, Cheung, Chui Ming Gemmy, Wong, Tien Yin, Chakravarthy, Usha, and Cheng, Ching-Yu
Published: 2025
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31. Lipid-lowering drug and complement factor H genotyping–personalized treatment strategy for age-related macular degeneration

Author: Xue, Can Can, Teo, Kelvin Y.C., Tham, Yih Chung, Li, Hengtong, Thakur, Sahil, Sabanayagam, Charumathi, Fan, Qiao, Silver, David L., Wang, Xiaomeng, Cheung, Chui Ming Gemmy, Wong, Tien Yin, Chakravarthy, Usha, Cheng, Ching-Yu, and Nusinovici, Simon
Published: 2024
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32. Omega-3 Polyunsaturated Fatty Acids as a Protective Factor for Myopia

Author: Xue, Can Can, Li, Hengtong, Dong, Xing-Xuan, Yu, Marco, Soh, Zhi Da, Chong, Crystal Chun Yuen, Jiang, Chen, Choquet, Helene, Zebardast, Nazlee, Zekavat, Seyedeh Maryam, Hysi, Pirro G., Saw, Seang Mei, Fan, Qiao, Tham, Yih-Chung, Pan, Chen-Wei, and Cheng, Ching-Yu
Published: 2024
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33. Deep Imbalanced Regression Model for Predicting Refractive Error from Retinal Photos

Author: Yew, Samantha Min Er, Lei, Xiaofeng, Chen, Yibing, Goh, Jocelyn Hui Lin, Pushpanathan, Krithi, Xue, Can Can, Wang, Ya Xing, Jonas, Jost B., Sabanayagam, Charumathi, Koh, Victor Teck Chang, Xu, Xinxing, Liu, Yong, Cheng, Ching-Yu, and Tham, Yih-Chung
Published: 2025
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34. A deep learning system for predicting time to progression of diabetic retinopathy

Author: Dai, Ling, Sheng, Bin, Chen, Tingli, Wu, Qiang, Liu, Ruhan, Cai, Chun, Wu, Liang, Yang, Dawei, Hamzah, Haslina, Liu, Yuexing, Wang, Xiangning, Guan, Zhouyu, Yu, Shujie, Li, Tingyao, Tang, Ziqi, Ran, Anran, Che, Haoxuan, Chen, Hao, Zheng, Yingfeng, Shu, Jia, Huang, Shan, Wu, Chan, Lin, Shiqun, Liu, Dan, Li, Jiajia, Wang, Zheyuan, Meng, Ziyao, Shen, Jie, Hou, Xuhong, Deng, Chenxin, Ruan, Lei, Lu, Feng, Chee, Miaoli, Quek, Ten Cheer, Srinivasan, Ramyaa, Raman, Rajiv, Sun, Xiaodong, Wang, Ya Xing, Wu, Jiarui, Jin, Hai, Dai, Rongping, Shen, Dinggang, Yang, Xiaokang, Guo, Minyi, Zhang, Cuntai, Cheung, Carol Y., Tan, Gavin Siew Wei, Tham, Yih-Chung, Cheng, Ching-Yu, Li, Huating, Wong, Tien Yin, and Jia, Weiping
Published: 2024
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35. AI-integrated ocular imaging for predicting cardiovascular disease: advancements and future outlook

Author: Huang, Yu, Cheung, Carol Y, Li, Dawei, Tham, Yih Chung, Sheng, Bin, Cheng, Ching Yu, Wang, Ya Xing, and Wong, Tien Yin
Published: 2024
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36. Abstract 4117640: Thirty-Year Prevalence and Prognosis of the Cardiovascular-Kidney-Metabolic Syndrome in U.S. Females and Males

Author: Ji, Hongwei, sabanayagam, charumathi, Matsushita, Kunihiro, Cheng, Ching-Yu, Rim, Tyler Hyungtaek, Sheng, Bin, Li, Huating, Tham, Yih Chung, Cheng, Susan, and Wong, Tien
Published: 2024
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37. Worldwide trends in diabetes prevalence and treatment from 1990 to 2022: a pooled analysis of 1108 population-representative studies with 141 million participants

Author: Zhou, Bin, Rayner, Archie W, Gregg, Edward W, Sheffer, Kate E, Carrillo-Larco, Rodrigo M, Bennett, James E, Shaw, Jonathan E, Paciorek, Christopher J, Singleton, Rosie K, Barradas Pires, Ana, Stevens, Gretchen A, Danaei, Goodarz, Lhoste, Victor PF, Phelps, Nowell H, Heap, Rachel A, Jain, Lakshya, D'Ailhaud De Brisis, Yse, Galeazzi, Agnese, Kengne, Andre P, Mishra, Anu, Ikeda, Nayu, Lin, Hsien-Ho, Aguilar-Salinas, Carlos A, Anjana, Ranjit Mohan, Ben Romdhane, Habiba, Davletov, Kairat, Ganapathy, Shubash, Heidemann, Christin, Khader, Yousef Saleh, Khang, Young-Ho, Laxmaiah, Avula, Mbanya, Jean Claude N, Mohan, Viswanathan, Narayan, KM Venkat, Pavkov, Meda E, Sobngwi, Eugène, Wade, Alisha N, Younger-Coleman, Novie O, Zdrojewski, Tomasz, Abarca-Gómez, Leandra, Abbasi-Kangevari, Mohsen, Abdul Rahim, Hanan F, Abu-Rmeileh, Niveen M, Adambekov, Shalkar, Adams, Robert J, Aekplakorn, Wichai, Afzal, Shoaib, Agdeppa, Imelda A, Aghazadeh-Attari, Javad, Agyemang, Charles, Ahmad, Noor Ani, Ahmadi, Ali, Ahmadi, Naser, Ahmadi, Nastaran, Ahmed, Soheir H, Ahrens, Wolfgang, Ajlouni, Kamel, Al-Hamli, Sarah F, Al-Hinai, Halima, Al-Lawati, Jawad A, Al Asfoor, Deena, Alarouj, Monira, AlBuhairan, Fadia, AlDhukair, Shahla, Ali, Mohamed M, Ali, Mohammed K, Alieva, Anna V, Alinezhad, Farbod, Alkandari, Abdullah, Alkerwi, Ala'a, Aly, Eman, Amarapurkar, Deepak N, Andersen, Lars Bo, Anderssen, Sigmund A, Andrade, Dolores S, Ansari-Moghaddam, Alireza, Aounallah-Skhiri, Hajer, Araújo, Joana, Aris, Tahir, Arku, Raphael E, Arlappa, Nimmathota, Aryal, Krishna K, Aspelund, Thor, Assah, Felix K, Assembekov, Batyrbek, Au Yeung, Shiu Lun, Auvinen, Juha, Avdičová, Mária, Azad, Kishwar, Azevedo, Ana, Azimi-Nezhad, Mohsen, Azizi, Fereidoun, Bacopoulou, Flora, Balakrishna, Nagalla, Balanova, Yulia, Bamoshmoosh, Mohamed, Banach, Maciej, Bandosz, Piotr, Banegas, José R, Barbagallo, Carlo M, Barcelo, Alberto, Baretić, Maja, Barrera, Lena, Barreto, Marta, Basit, Abdul, Batieha, Anwar M, Batista, Aline P, Baur, Louise A, Belavendra, Antonisamy, Benedek, Theodora, Benet, Mikhail, Benzeval, Michaela, Berkinbayev, Salim, Bernabe-Ortiz, Antonio, Berrios Carrasola, Ximena, Bettiol, Heloísa, Beybey, Augustin F, Bhargava, Santosh K, Bi, Yufang, Bika Lele, Elysée Claude, Bikbov, Mukharram M, Bista, Bihungum, Bjerregaard, Peter, Bjertness, Espen, Bjertness, Marius B, Björkelund, Cecilia, Bloch, Katia V, Blokstra, Anneke, Bobak, Martin, Boehm, Bernhard O, Boggia, Jose G, Boissonnet, Carlos P, Bojesen, Stig E, Bonaccio, Marialaura, Bonilla-Vargas, Alice, Borghs, Herman, Botomba, Steve, Bovet, Pascal, Brajkovich, Imperia, Brenner, Hermann, Brewster, Lizzy M, Brian, Garry R, Briceño, Yajaira, Brito, Miguel, Bueno, Gloria, Bugge, Anna, Buntinx, Frank, Cabrera de León, Antonio, Caixeta, Roberta B, Can, Günay, Cândido, Ana Paula C, Capanzana, Mario V, Čapková, Naděžda, Capuano, Eduardo, Capuano, Rocco, Capuano, Vincenzo, Cardoso, Viviane C, Carlsson, Axel C, Casanueva, Felipe F, Censi, Laura, Cervantes–Loaiza, Marvin, Chadjigeorgiou, Charalambos A, Chamnan, Parinya, Chamukuttan, Snehalatha, Chan, Queenie, Charchar, Fadi J, Chaturvedi, Nish, Chen, Chien-Jen, Chen, Huashuai, Chen, Long-Sheng, Cheng, Ching-Yu, Cheraghian, Bahman, Chetrit, Angela, Chiou, Shu-Ti, Chirlaque, María-Dolores, Chudek, Jerzy, Cifkova, Renata, Cirillo, Massimo, Claessens, Frank, Clarke, Janine, Cohen, Emmanuel, Concin, Hans, Cooper, Cyrus, Cosmin, Cojocaru R, Costanzo, Simona, Cowan, Melanie J, Cowell, Chris, Crampin, Amelia C, Crujeiras, Ana B, Cruz, Juan J, Cureau, Felipe V, Cuschieri, Sarah, D'Arrigo, Graziella, d'Orsi, Eleonora, da Silva-Ferreira, Haroldo, Dallongeville, Jean, Damasceno, Albertino, Dankner, Rachel, Dastgiri, Saeed, Dauchet, Luc, De Curtis, Amalia, de Gaetano, Giovanni, De Henauw, Stefaan, De Ridder, David, Deepa, Mohan, DeGennaro, Vincent Jr, Demarest, Stefaan, Dennison, Elaine, Deschamps, Valérie, Dhimal, Meghnath, Dika, Zivka, Djalalinia, Shirin, Donfrancesco, Chiara, Dorobantu, Maria, Dragano, Nico, Drygas, Wojciech, Du, Shufa, Du, Yong, Duante, Charmaine A, Duboz, Priscilla, Duda, Rosemary B, Dushpanova, Anar, Dzerve, Vilnis, Dziankowska-Zaborszczyk, Elzbieta, Ebrahimi, Narges, Eddie, Ricky, Eftekhar, Ebrahim, Efthymiou, Vasiliki, Egbagbe, Eruke E, Eggertsen, Robert, Eghtesad, Sareh, Ejembi, Clara Ladi, El-Khateeb, Mohammad, El Ati, Jalila, Eldemire-Shearer, Denise, Elosua, Roberto, Enang, Ofem, Erasmus, Rajiv T, Erem, Cihangir, Ergor, Gul, Eriksen, Louise, Eriksson, Johan G, Esmaeili, Ali, Evans, Roger G, Fagherazzi, Guy, Fahimfar, Noushin, Fakhradiyev, Ildar, Fakhretdinova, Albina A, Fall, Caroline H, Faramarzi, Elnaz, Farjam, Mojtaba, Farzadfar, Farshad, Farzi, Yosef, Fattahi, Mohammad Reza, Fawwad, Asher, Felix-Redondo, Francisco J, Ferguson, Trevor S, Fernández-Bergés, Daniel, Fernando, Desha R, Ferrao, Thomas, Ferrari, Marika, Ferrario, Marco M, Ferreccio, Catterina, Ferrer, Eldridge, Feskens, Edith JM, Fink, Günther, Flood, David, Forsner, Maria, 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Mirjalili, Mohammad Reza, Mirkopoulou, Daphne, Modesti, Pietro A, Moghaddam, Sahar Saeedi, Mohammad, Kazem, Mohammadi, Mohammad Reza, Mohammadi, Zahra, Mohammadifard, Noushin, Mohammadpourhodki, Reza, Mohd Yusoff, Muhammad Fadhli, Mohebbi, Iraj, Møller, Niels C, Molnár, Dénes, Momenan, Amirabbas, Montenegro Mendoza, Roger A, Moosazadeh, Mahmood, Moradpour, Farhad, Morejon, Alain, Moreno, Luis A, Morgan, Karen, Morin, Suzanne N, Moschonis, George, Moslem, Alireza, Mosquera, Mildrey, Mossakowska, Malgorzata, Mostafa, Aya, Mostafavi, Seyed-Ali, Mota, Eugen, Mota, Jorge, Mota, Maria, Motlagh, Mohammad Esmaeel, Motta, Jorge, Msyamboza, Kelias P, Mu, Thet Thet, Muiesan, Maria L, Munroe, Patricia B, Mursu, Jaakko, Musa, Kamarul Imran, Mustafa, Norlaila, Muyer, Muel Telo MC, Nabipour, Iraj, Nagel, Gabriele, Naidu, Balkish M, Najafi, Farid, Námešná, Jana, Nang, Ei Ei K, Nangia, Vinay B, Naseri, Take, Navarro-Ramírez, Ana J, Neelapaichit, Nareemarn, Nejatizadeh, Azim, Nenko, Ilona, Nervi, Flavio, Ng, Tze Pin, Nguyen, Chung T, Nguyen, Nguyen D, Nguyen, Quang Ngoc, Ni, Michael Y, Nie, Peng, Nieto-Martínez, Ramfis E, Ning, Guang, Ninomiya, Toshiharu, Nishi, Nobuo, Noale, Marianna, Noboa, Oscar A, Noda, Mitsuhiko, Nordestgaard, Børge G, Noto, Davide, Nsour, Mohannad Al, Nuhoğlu, Irfan, Nyirenda, Moffat, O'Neill, Terence W, Oh, Kyungwon, Ohtsuka, Ryutaro, Omar, Mohd Azahadi, Onat, Altan, Ong, Sok King, Onodugo, Obinna, Ordunez, Pedro, Ornelas, Rui, Ortiz, Pedro J, Osmond, Clive, Ostovar, Afshin, Otero, Johanna A, Ottendahl, Charlotte B, Otu, Akaninyene, Owusu-Dabo, Ellis, Pahomova, Elena, Palmieri, Luigi, Pan, Wen-Harn, Panagiotakos, Demosthenes, Panda-Jonas, Songhomitra, Pang, Zengchang, Panza, Francesco, Paoli, Mariela, Park, Suyeon, Parsaeian, Mahboubeh, Patalen, Chona F, Patel, Nikhil D, Pechlaner, Raimund, Pećin, Ivan, Pedro, João M, Peixoto, Sergio Viana, Peltonen, Markku, Pereira, Alexandre C, Pessôa dos Prazeres, Thaliane Mayara, Peykari, Niloofar, Phall, Modou 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Rodríguez-Anderson, Wendy E, Rodríguez-Artalejo, Fernando, Rodriguez-Perez, María del Cristo, Rodríguez-Villamizar, Laura A, Rodríguez, Andrea Y, Roggenbuck, Ulla, Rohloff, Peter, Rojas-Martinez, Rosalba, Romeo, Elisabetta L, Rosengren, Annika, Roy, Joel GR, Rubinstein, Adolfo, Ruiz-Castell, Maria, Russo, Paola, Rust, Petra, Rutkowski, Marcin, Sabanayagam, Charumathi, Sabbaghi, Hamideh, Sachdev, Harshpal S, Sadjadi, Alireza, Safarpour, Ali Reza, Safi, Sare, Safiri, Saeid, Saghi, Mohammad Hossien, Saidi, Olfa, Sakata, Satoko, Saki, Nader, Šalaj, Sanja, Salanave, Benoit, Salonen, Jukka T, Salvetti, Massimo, Sánchez-Abanto, Jose, Santos, Diana A, Santos, Lèlita C, Santos, Maria Paula, Santos, Rute, Santos, Tamara R, Saramies, Jouko L, Sardinha, Luis B, Sarrafzadegan, Nizal, Sato, Yoko, Saum, Kai-Uwe, Savin, Stefan, Sawada, Norie, Sbaraini, Mariana, Scazufca, Marcia, Schaan, Beatriz D, Schargrodsky, Herman, Scheidt-Nave, Christa, Schipf, Sabine, Schmidt, Amand Floriaan, Schmidt, Börge, 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Uusitalo, Hannu MT, Valdivia, Gonzalo, Valvi, Damaskini, van Dam, Rob M, van den Born, Bert-Jan, Van der Heyden, Johan, Van Minh, Hoang, van Rossem, Lenie, Van Schoor, Natasja M, van Valkengoed, Irene GM, Vanderschueren, Dirk, Vanuzzo, Diego, Varbo, Anette, Vasan, Senthil K, Vega, Tomas, Veidebaum, Toomas, Velasquez-Melendez, Gustavo, Verdot, Charlotte, Veronesi, Giovanni, Verstraeten, Roosmarijn, Victora, Cesar G, Viet, Lucie, Villarroel, Luis, Vioque, Jesus, Virtanen, Jyrki K, Viswanathan, Bharathi, Vollenweider, Peter, Voutilainen, Ari, Vrijheid, Martine, Walton, Janette, Wan Bebakar, Wan Mohamad, Wan Mohamud, Wan Nazaimoon, Wang, Chongjian, Wang, Huijun, Wang, Ningli, Wang, Qian, Wang, Weiqing, Wang, Ya Xing, Wang, Yi-Ren, Wang, Ying-Wei, Wannamethee, S Goya, Webster-Kerr, Karen, Wedderkopp, Niels, Wei, Wenbin, Westbury, Leo D, Whincup, Peter H, Widhalm, Kurt, Widyahening, Indah S, Więcek, Andrzej, Wijemunige, Nilmini, Wilks, Rainford J, Willeit, Karin, Willeit, Peter, Wilsgaard, Tom, Wojtyniak, Bogdan, Wong-McClure, Roy A, Wong, Andrew, Wong, Emily B, Woodward, Mark, Wu, Chao-Chun, Wu, Frederick C, Xu, Haiquan, Xu, Liang, Xu, Yu, Yaacob, Nor Azwany, Yan, Li, Yan, Weili, Yasuharu, Tabara, Yeh, Chao-Yu, Yoosefi, Moein, Yoshihara, Akihiro, You, San-Lin, Yu, Yu-Ling, Yusoff, Ahmad Faudzi, Zainuddin, Ahmad A, Zamani, Farhad, Zambon, Sabina, Zampelas, Antonis, Zargar, Abdul Hamid, Zaw, Ko Ko, Zeljkovic Vrkic, Tajana, Zeng, Yi, Zhang, Bing, Zhang, Lei, Zhang, Luxia, Zhang, Zhen-Yu, Zhao, Ming-Hui, Zhao, Wenhua, Zholdin, Bekbolat, Zimmet, Paul, Zins, Marie, Zitt, Emanuel, Zoghlami, Nada, Zuñiga Cisneros, Julio, and Ezzati, Majid
Published: 2024
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38. Comparative Analysis of Vision Transformers and Conventional Convolutional Neural Networks in Detecting Referable Diabetic Retinopathy

Author: Goh, Jocelyn Hui Lin, Ang, Elroy, Srinivasan, Sahana, Lei, Xiaofeng, Loh, Johnathan, Quek, Ten Cheer, Xue, Cancan, Xu, Xinxing, Liu, Yong, Cheng, Ching-Yu, Rajapakse, Jagath C., and Tham, Yih-Chung
Published: 2024
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39. Value proposition of retinal imaging in Alzheimer's disease screening: A review of eight evolving trends

Author: Chan, Victor T.T., Ran, An Ran, Wagner, Siegfried K., Hui, Herbert Y.H., Hu, Xiaoyan, Ko, Ho, Fekrat, Sharon, Wang, Yaxing, Lee, Cecilia S., Young, Alvin L., Tham, Clement C., Tham, Yih Chung, Keane, Pearse A., Milea, Dan, Chen, Christopher, Wong, Tien Yin, Mok, Vincent C.T., and Cheung, Carol Y.
Published: 2024
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40. Application of a deep-learning marker for morbidity and mortality prediction derived from retinal photographs: a cohort development and validation study

Author: Nusinovici, Simon, Rim, Tyler Hyungtaek, Li, Hengtong, Yu, Marco, Deshmukh, Mihir, Quek, Ten Cheer, Lee, Geunyoung, Chong, Crystal Chun Yuen, Peng, Qingsheng, Xue, Can Can, Zhu, Zhuoting, Chew, Emily Y, Sabanayagam, Charumathi, Wong, Tien-Yin, Tham, Yih-Chung, and Cheng, Ching-Yu
Published: 2024
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41. Contributions of Lipid-Related Metabolites and Complement Proteins to Early and Intermediate Age-Related Macular Degeneration

Author: Nusinovici, Simon, Zhou, Lei, Wang, Xinyue, Tham, Yih Chung, Wang, Xiaomeng, Wong, Tien Yin, Chakravarthy, Usha, and Cheng, Ching-Yu
Published: 2024
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42. Practice Patterns and Challenges in Managing Inherited Retinal Diseases Across Asia-Pacific: A Survey from the APIED Network

Author: Wong, Wendy M., Tham, Yih Chung, Ayton, Lauren N., Britten-Jones, Alexis Ceecee, Edwards, Thomas L., Grigg, John, Simunovic, Matthew P., Chen, Fred K., Jin, Zi-Bing, Shen, Ren-Juan, Sui, Ruifang, Yang, Liping, Zhao, Chen, Chen, Haoyu, Li, Shiying, Ding, Xiaoyan, Bhende, Muna, Raman, Rajiv, Sen, Parveen, Poornachandra, B., Chia, Valen, Manurung, Florence, Sasongko, Muhammad Bayu, Ikeda, Hanako, Fujinami, Kaoru, Woo, Se Joon, Kim, Sang Jin, Bastion, Mae-Lynn Catherine, Kamalden, Ain Tengku, Lott, Penny P., Fong, Kenneth, Shunmugam, Manoharan, Lim, Amelia, Thapa, Raba, Ibañez, B.Manuel Benjamin, Koh, Adrian, Holder, Graham E., Su, Xinyi, Chan, Choi Mun, Fenner, Beau J., Laude, Augustinus, Ngo, Wei Kiong, Chen, Ta-Ching, Wang, Nan-Kai, Kang, Eugene Yu-Chuan, Surawatsatien, Nuntachai, Pisuchpen, Phattrawan, Sujirakul, Tharikarn, Wongchaisuwat, Nida, Apivatthakakul, Atitaya, Kumaramanickavel, Govindasamy, Leroy, Bart, Michaelides, Michel, Pontikos, Nikolas, Cheng, Ching-Yu, Pang, Chi Pui, Chen, Li Jia, and Chan, Hwei Wuen
Published: 2024
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43. Ocular image-based deep learning for predicting refractive error: A systematic review

Author: Yew, Samantha Min Er, Chen, Yibing, Goh, Jocelyn Hui Lin, Chen, David Ziyou, Chun Jin Tan, Marcus, Cheng, Ching-Yu, Teck Chang Koh, Victor, and Tham, Yih Chung
Published: 2024
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44. Integration of smartphone technology and artificial intelligence for advanced ophthalmic care: A systematic review

Author: Jin, Kai, Li, Yingyu, Wu, Hongkang, Tham, Yih Chung, Koh, Victor, Zhao, Yitian, Kawasaki, Ryo, Grzybowski, Andrzej, and Ye, Juan
Published: 2024
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45. Artificial intelligence for diabetes care: current and future prospects

Author: Sheng, Bin, Pushpanathan, Krithi, Guan, Zhouyu, Lim, Quan Hziung, Lim, Zhi Wei, Yew, Samantha Min Er, Goh, Jocelyn Hui Lin, Bee, Yong Mong, Sabanayagam, Charumathi, Sevdalis, Nick, Lim, Cynthia Ciwei, Lim, Chwee Teck, Shaw, Jonathan, Jia, Weiping, Ekinci, Elif Ilhan, Simó, Rafael, Lim, Lee-Ling, Li, Huating, and Tham, Yih-Chung
Published: 2024
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46. Latest developments of generative artificial intelligence and applications in ophthalmology

Author: Feng, Xiaoru, Xu, Kezheng, Luo, Ming-Jie, Chen, Haichao, Yang, Yangfan, He, Qi, Song, Chenxin, Li, Ruiyao, Wu, You, Wang, Haibo, Tham, Yih Chung, Ting, Daniel Shu Wei, Lin, Haotian, Wong, Tien Yin, and Lam, Dennis Shun-chiu
Published: 2024
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47. Is Kidney Function Associated with Age-Related Macular Degeneration?: Findings from the Asian Eye Epidemiology Consortium

Author: Xue, Can Can, Sim, Ralene, Chee, Miao Li, Yu, Marco, Wang, Ya Xing, Rim, Tyler Hyungtaek, Hyung, Park Kyu, Woong, Kang Se, Song, Su Jeong, Nangia, Vinay, Panda-Jonas, Songhomitra, Wang, Ning Li, Hao, Jie, Zhang, Qing, Cao, Kai, Sasaki, Mariko, Harada, Sei, Toru, Takebayashi, Ryo, Kawasaki, Raman, Rajiv, Surya, Janani, Khan, Rehana, Bikbov, Mukharram, Wong, Ian Y., Cheung, Chui Ming Gemmy, Jonas, Jost B., Cheng, Ching-Yu, and Tham, Yih-Chung
Published: 2024
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48. International incidence and temporal trends for rhegmatogenous retinal detachment: A systematic review and meta-analysis

Author: Ge, Jasmine Yaowei, Teo, Zhen Ling, Chee, Miao Li, Tham, Yih-Chung, Rim, Tyler Hyungtaek, Cheng, Ching-Yu, Wong, Tien Yin, Wong, Edmund Yick Mun, Lee, Shu Yen, and Cheung, Ning
Published: 2024
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49. Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults

Author: Phelps, Nowell H, Singleton, Rosie K, Zhou, Bin, Heap, Rachel A, Mishra, Anu, Bennett, James E, Paciorek, Christopher J, Lhoste, Victor PF, Carrillo-Larco, Rodrigo M, Stevens, Gretchen A, Rodriguez-Martinez, Andrea, Bixby, Honor, Bentham, James, Di Cesare, Mariachiara, Danaei, Goodarz, Rayner, Archie W, Barradas-Pires, Ana, Cowan, Melanie J, Savin, Stefan, Riley, Leanne M, Aguilar-Salinas, Carlos A, Baker, Jennifer L, Barkat, Amina, Bhutta, Zulfiqar A, Branca, Francesco, Caixeta, Roberta B, Cuschieri, Sarah, Farzadfar, Farshad, Ganapathy, Shubash, Ikeda, Nayu, Iotova, Violeta, Kengne, Andre P, Khang, Young-Ho, Laxmaiah, Avula, Lin, Hsien-Ho, Ma, Jun, Mbanya, Jean Claude N, Miranda, J Jaime, Pradeepa, Rajendra, Rodríguez-Artalejo, Fernando, Sorić, Maroje, Turley, Maria, Wang, Limin, Webster-Kerr, Karen, Aarestrup, Julie, Abarca-Gómez, Leandra, Abbasi-Kangevari, Mohsen, Abdeen, Ziad A, Abdrakhmanova, Shynar, Abdul Ghaffar, Suhaila, Abdul Rahim, Hanan F, Abdurrahmonova, Zulfiya, Abu-Rmeileh, Niveen M, Abubakar Garba, Jamila, Acosta-Cazares, Benjamin, Adam, Ishag, Adamczyk, Marzena, Adams, Robert J, Adu-Afarwuah, Seth, Aekplakorn, Wichai, Afsana, Kaosar, Afzal, Shoaib, Agbor, Valirie N, Agdeppa, Imelda A, Aghazadeh-Attari, Javad, Ågren, Åsa, Aguenaou, Hassan, Agyemang, Charles, Ahmad, Mohamad Hasnan, Ahmad, Noor Ani, Ahmadi, Ali, Ahmadi, Naser, Ahmadi, Nastaran, Ahmed, Imran, Ahmed, Soheir H, Ahrens, Wolfgang, Aitmurzaeva, Gulmira, Ajlouni, Kamel, Al-Hazzaa, Hazzaa M, Al-Hinai, Halima, Al-Lahou, Badreya, Al-Lawati, Jawad A, Al-Raddadi, Rajaa, Al Asfoor, Deena, Al Hourani, Huda M, Al Qaoud, Nawal M, Alarouj, Monira, AlBuhairan, Fadia, AlDhukair, Shahla, Aldwairji, Maryam A, Alexius, Sylvia, Ali, Mohamed M, Alieva, Anna V, Alkandari, Abdullah, Alkerwi, Ala'a, Alkhatib, Buthaina M, Allin, Kristine, Alomary, Shaker A, Alomirah, Husam F, Alshangiti, Arwa M, Alvarez-Pedrerol, Mar, Aly, Eman, Amarapurkar, Deepak N, Amiano Etxezarreta, Pilar, Amoah, John, Amougou, 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Farhad, Pourshams, Akram, Poustchi, Hossein, Price, Alison J, Price, Jacqueline F, Prista, Antonio, Providencia, Rui, Puder, Jardena J, Pudule, Iveta, Puhakka, Solie, Puiu, Maria, Punab, Margus, Qadir, Muhammed S, Qasrawi, Radwan F, Qiao, Qing, Qorbani, Mostafa, Quintana, Hedley K, Quiroga-Padilla, Pedro J, Quoc Bao, Tran, Rach, Stefan, Radic, Ivana, Radisauskas, Ricardas, Rahimikazerooni, Salar, Rahman, Mahfuzar, Rahman, Mahmudur, Raitakari, Olli, Raj, Manu, Rajabov, Tamerlan, Rakhmatulloev, Sherali, Rakovac, Ivo, Ramachandra Rao, Sudha, Ramachandran, Ambady, Ramadan, Otim PC, Ramires, Virgílio V, Ramirez-Zea, Manuel, Ramke, Jacqueline, Ramos, Elisabete, Ramos, Rafel, Rampal, Lekhraj, Rampal, Sanjay, Ramsay, Sheena E, Rangelova, Lalka S, Rarra, Vayia, Rascon-Pacheco, Ramon A, Rashidi, Mohammad-Mahdi, Rech, Cassiano Ricardo, Redon, Josep, Reganit, Paul Ferdinand M, Regecová, Valéria, Renner, Jane DP, Repasy, Judit A, Reuter, Cézane P, Revilla, Luis, Reynolds, Andrew, Rezaei, Negar, 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Alireza, Safarpour, Ali Reza, Safi, Sare, Safiri, Saeid, Saghi, Mohammad Hossien, Saidi, Olfa, Saieva, Calogero, Sakata, Satoko, Saki, Nader, Šalaj, Sanja, Salanave, Benoit, Salazar Martinez, Eduardo, Salhanova, Akkumis, Salmerón, Diego, Salomaa, Veikko, Salonen, Jukka T, Salvetti, Massimo, Samoutian, Margarita, Sánchez-Abanto, Jose, Sánchez Rodríguez, Inés, Sandjaja, Sans, Susana, Santa-Marina, Loreto, Santacruz, Ethel, Santos, Diana A, Santos, Ina S, Santos, Lèlita C, Santos, Maria Paula, Santos, Osvaldo, Santos, Rute, Santos, Tamara R, Saramies, Jouko L, Sardinha, Luis B, Sarrafzadegan, Nizal, Sathish, Thirunavukkarasu, Saum, Kai-Uwe, Savva, Savvas, Savy, Mathilde, Sawada, Norie, Sbaraini, Mariana, Scazufca, Marcia, Schaan, Beatriz D, Schaffrath Rosario, Angelika, Schargrodsky, Herman, Schienkiewitz, Anja, Schindler, Karin, Schipf, Sabine, Schmidt, Börge, Schmidt, Carsten O, Schmidt, Ida Maria, Schneider, Andrea, Schnohr, Peter, Schöttker, Ben, Schramm, Sara, Schramm, Stine, Schröder, Helmut, Schultsz, Constance, Schultz, Gry, Schulze, Matthias B, Schutte, Aletta E, Sebert, Sylvain, Sedaghattalab, Moslem, Selamat, Rusidah, Sember, Vedrana, Sen, Abhijit, Senbanjo, Idowu O, Sepanlou, Sadaf G, Sequera, Guillermo, Serra-Majem, Luis, Servais, Jennifer, Ševčíková, Ľudmila, Sewpaul, Ronel, Shalnova, Svetlana, Shamah-Levy, Teresa, Shamshirgaran, Seyed Morteza, Shanthirani, Coimbatore Subramaniam, Sharafkhah, Maryam, Sharma, Sanjib K, Sharman, Almaz, Shaw, Jonathan E, Shayanrad, Amaneh, Shayesteh, Ali Akbar, Shengelia, Lela, Shi, Zumin, Shibuya, Kenji, Shimizu-Furusawa, Hana, Shimony, Tal, Shiri, Rahman, Shrestha, Namuna, Si-Ramlee, Khairil, Siani, Alfonso, Siantar, Rosalynn, Sibai, Abla M, Sidossis, Labros S, Silitrari, Natalia, Silva, Antonio M, Silva, Caroline Ramos de Moura, Silva, Diego Augusto Santos, Silva, Kelly Samara, Sim, Xueling, Simon, Mary, Simons, Judith, Simons, Leon A, Sjöberg, Agneta, Sjöström, Michael, Skoblina, Elena V, Skoblina, Natalia A, 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Maciej, Topbas, Murat, Topór-Mądry, Roman, Torheim, Liv Elin, Tornaritis, Michael J, Torrent, Maties, Torres-Collado, Laura, Toselli, Stefania, Touloumi, Giota, Traissac, Pierre, Tran, Thi Tuyet-Hanh, Tremblay, Mark S, Triantafyllou, Areti, Trichopoulos, Dimitrios, Trichopoulou, Antonia, Trinh, Oanh TH, Trivedi, Atul, Tshepo, Lechaba, Tsigga, Maria, Tsintavis, Panagiotis, Tsugane, Shoichiro, Tuitele, John, Tuliakova, Azaliia M, Tulloch-Reid, Marshall K, Tullu, Fikru, Tuomainen, Tomi-Pekka, Tuomilehto, Jaakko, Twig, Gilad, Tynelius, Per, Tzala, Evangelia, Tzotzas, Themistoklis, Tzourio, Christophe, Udoji, Nwannedimma, Ueda, Peter, Ugel, Eunice, Ukoli, Flora AM, Ulmer, Hanno, Unal, Belgin, Usupova, Zhamyila, Uusitalo, Hannu MT, Uysal, Nalan, Vaitkeviciute, Justina, Valdivia, Gonzalo, Vale, Susana, Valvi, Damaskini, van Dam, Rob M, van den Born, Bert-Jan, Van der Heyden, Johan, van der Schouw, Yvonne T, Van Herck, Koen, Van Lippevelde, Wendy, Van Minh, Hoang, Van Schoor, Natasja M, van 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Wang, Ya Xing, Wang, Ying-Wei, Wannamethee, S Goya, Wareham, Nicholas, Wartha, Olivia, Weber, Adelheid, Wedderkopp, Niels, Weghuber, Daniel, Wei, Wenbin, Weres, Aneta, Werner, Bo, Westbury, Leo D, Whincup, Peter H, Wichstrøm, Lars, Wickramasinghe, Kremlin, Widhalm, Kurt, Widyahening, Indah S, Więcek, Andrzej, Wild, Philipp S, Wilks, Rainford J, Willeit, Johann, Willeit, Peter, Williams, Julianne, Wilsgaard, Tom, Wirth, James P, Wojtyniak, Bogdan, Woldeyohannes, Meseret, Wolf, Kathrin, Wong-McClure, Roy A, Wong, Andrew, Wong, Emily B, Wong, Jyh Eiin, Wong, Tien Yin, Woo, Jean, Woodward, Mark, Wu, Frederick C, Wu, Hon-Yen, Wu, Jianfeng, Wu, Li Juan, Wu, Shouling, Wyszyńska, Justyna, Xu, Haiquan, Xu, Liang, Yaacob, Nor Azwany, Yamborisut, Uruwan, Yan, Li, Yan, Weili, Yang, Ling, Yang, Xiaoguang, Yang, Yang, Yardim, Nazan, Yasuharu, Tabara, Yépez García, Martha, Yiallouros, Panayiotis K, Yngve, Agneta, Yoosefi, Moein, Yoshihara, Akihiro, Yotov, Yoto, You, Qi Sheng, You, San-Lin, Younger-Coleman, Novie O, Yu, Yu-Ling, Yu, Yunjiang, Yusof, Safiah Md, Yusoff, Ahmad Faudzi, Zaccagni, Luciana, Zafiropulos, Vassilis, Zainuddin, Ahmad A, Zakavi, Seyed Rasoul, Zamani, Farhad, Zambon, Sabina, Zampelas, Antonis, Zamrazilová, Hana, Zapata, Maria Elisa, Zargar, Abdul Hamid, Zaw, Ko Ko, Zayed, Ayman A, Zdrojewski, Tomasz, Żegleń, Magdalena, Zejglicova, Kristyna, Zeljkovic Vrkic, Tajana, Zeng, Yi, Zentai, Andrea, Zhang, Bing, Zhang, Luxia, Zhang, Zhen-Yu, Zhao, Dong, Zhao, Ming-Hui, Zhao, Wenhua, Zhecheva, Yanitsa V, Zhen, Shiqi, Zheng, Wei, Zheng, Yingfeng, Zholdin, Bekbolat, Zhou, Maigeng, Zhu, Dan, Zimmet, Paul, Zins, Marie, Zitt, Emanuel, Zocalo, Yanina, Zoghlami, Nada, Zuñiga Cisneros, Julio, Zuziak, Monika, and Ezzati, Majid
Published: 2024
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50. Large language models for diabetes care: Potentials and prospects

Author: Sheng, Bin, Guan, Zhouyu, Lim, Lee-Ling, Jiang, Zehua, Mathioudakis, Nestoras, Li, Jiajia, Liu, Ruhan, Bao, Yuqian, Bee, Yong Mong, Wang, Ya-Xing, Zheng, Yingfeng, Tan, Gavin Siew Wei, Ji, Hongwei, Car, Josip, Wang, Haibo, Klonoff, David C., Li, Huating, Tham, Yih-Chung, Wong, Tien Yin, and Jia, Weiping
Published: 2024
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