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science Bulletin 2024年第5期69卷 583-588页

作者：盛斌管洲榆 Lee-Ling Lim 江泽铧 Nestoras Mathioudakis 李佳佳刘茹涵包玉倩 Yong Mong Bee 王亚星郑颖丰 Gavin Siew Wei Tan 纪宏伟 Josip Car 王海波 David C.Klonoff 李华婷覃宇宗黄天荫贾伟平 Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and EngineeringSchool of ElectronicInformationand Electrical EngineeringShanghai Jiao Tong UniversityDepartment of Endocrinology and MetabolismShanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for DiabetesShanghai 200240China MOE Key Laboratory of Artificial Intelligence Artificial Intelligence InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Medicine Faculty of MedicineUniversity of MalayaKuala Lumpur 50603Malaysia Department of Medicine and Therapeutics The Chinese University of Hong KongHong KongChina Asia Diabetes Foundation Hong KongChina Tsinghua Medicine of Tsinghua University Beijing 100084China School of Clinical Medicine Beijing Tsinghua Changgung HospitalBeijing 102218China Division of Endocrinology Diabetes&MetabolismJohns Hopkins University School of MedicineBaltimoreMD 21211USA Department of Endocrinology Singapore General HospitalSingapore 169608Singapore Beijing Institute of Ophthalmology Beijing Tongren HospitalCapital University of Medical ScienceBeijing Ophthalmology and Visual Sciences Key LaboratoryBeijing 100730China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangzhou 510060China Singapore Eye Research Institute Singapore National Eye CentreSingapore 168751Singapore Department of Cardiology the Affiliated Hospital of Qingdao UniversityQingdao 266011China Centre for Population Health Sciences Lee Kong Chian School of MedicineNanyang Technological University SingaporeSingapore 639798Singapore Department of Primary Care and Public Health School of Public HealthImperial College LondonLondon SW72BUUK Clinical Trial Unit Research Centre of Big Data and Artificial Intelligence in Medicine

The increasing prevalence of diabetes has become a global public health concern in the 21st *** 2021,it was estimated that 537 million people had diabetes,and this number is projected to reach 643 million by 2030,and 783 million by 2045[1].Such a huge burden of diabetes brings great challenges in its prevention and management,including early diagnosis,timely interventions,and regular monitoring of risk factor control and complications *** self-care support and patient empowerment can enhance clinical and psychobehavioural outcomes[2],although these require additional resources including manpower,infrastructure(hard and technology),and *** emergence of digital health technologies(DHTs),especially artificial intelligence(AI),may help address these obstacles and alleviate the burden of diabetes[3].Large language models(LLMs),a generative AI that can accept image and text inputs and produce text outputs,have shown promise in various aspects of medical care.

关键词： prevention diagnosis finance

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

arXiv

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arXiv 2024年

作者： 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. Somfai, Gabor Márk Department of Ophthalmology Stadtspital Zürich Zurich Switzerland Spross Research Institute Zurich Switzerland Gutblick Research Switzerland Digital Medicine Unit Balgrist University Hospital Zurich Switzerland NIHR Biomedical Research Centre Moorfields Eye Hospital NHS Foundation Trust London United Kingdom Institute of Ophthalmology University College London London United Kingdom Department of Medical Physics and Biomedical Engineering University College London London United Kingdom Centre for Innovation and Precision Eye Health National University of Singapore Singapore Department of Ophthalmology National University of Singapore Singapore Singapore Eye Research Institute Singapore National Eye Centre Singapore Ophthalmology & Visual Sciences Academic Clinical Program Duke-NUS Medical School Singapore iScreen 2 Prevent LLC MiamiFL United States Bascom Palmer Eye Institute University of Miami FL United States Department of Ophthalmology University of Heidelberg Heidelberg Germany Department of Ophthalmology Semmelweis University Budapest Hungary

Background: Integrating deep learning into medical imaging is poised to greatly advance diagnostic methods, but it faces challenges with generalizability, which can foster distrust in artificial intelligence and exacerbate ethnic biases. Foundation models, based on self-supervised learning, address these issues, improve data efficiency and reduce ethnic biases. Natural domain foundation models show promise for medical imaging, but systematic research evaluating domain adaptation of these models, especially using self-supervised learning and parameter-efficient fine-tuning, remains underexplored. Additionally, little research addresses the issue of catastrophic forgetting observed during fine-tuning of foundation models. Methods: 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 from multiple datasets were employed for model development and subsequent fine-tuning for diabetic retinopathy staging and glaucoma detection. We introduced block expansion as a novel method for domain adaptation in retinal imaging and assessed the models for catastrophic forgetting. Models were benchmarked to RETFound, a state-of-the-art foundation model in ophthalmology. Findings: 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. Interpretation: The 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 and high data efficiency without sacrificing previously acquired capabilities. Our findings suggest t

关键词： Medical imaging

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Generative Data Augmentation for Diabetic Retinopathy Classification

Generative Data Augmentation for Diabetic Retinopathy Classi...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Gilbert Lim Pranav Thombre Mong Li Lee Wynne Hsu Ophthalmology and Visual Science Academic Clinical Program Duke-NUS Medical School School of Computer Science Carnegie Mellon University School of Computing National University of Singapore

ISBN: (数字)9781728192284

ISBN: (纸本)9781728185361

A fundamental factor limiting the effectiveness of classification algorithms, especially in the medical imaging domain, has been an insufficient quantity of relevant class-specific data. In particular, positive examples of disease conditions tend to be rare, and represent a common bottleneck in improving model performance. In this paper, we introduce GAN-based generative data augmentation methods with dynamic input sampling, and compare their performance against an image feature transfer technique, towards improving the performance of real-world diabetic retinopathy classification tasks. Results suggest that generative data augmentation has the potential to significantly improve classification performance over the baseline.

关键词： Training Retinopathy Tools Diabetes Task analysis Gallium nitride Biomedical imaging

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用于糖尿病培训的大型语言模型:一项前瞻性研究（英文）

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science Bulletin 2025年第6期 934-942页

作者：李灏萱江泽铧管洲榆包玉倩刘月星胡婷婷李佳佳刘茹涵吴量程棣纪宏伟王勇王亚星 Carol Y.Cheung 郑颖丰王继红李震吴卫兵 Cynthia Ciwei Lim Yong Mong Bee Hong Chang Tan Elif I.Ekinci David C.Klonoff Justin B.Echouffo-Tcheugui Nestoras Mathioudakis Leonor Corsino Rafael Simó Charumathi Sabanayagam Gavin Siew Wei Tan 程景煜黄天荫李华婷蔡淳毛丽娟 Lee-Ling Lim 覃宇宗盛斌贾伟平 Shanghai University of Sport School of Clinical Medicine Beijing Tsinghua Changgung Hospital Tsinghua Medicine Tsinghua University Beijing Visual Science and Translational Eye Research Institute(BERI) Tsinghua Medicine Tsinghua University Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes MoE Key Lab of Artificial Intelligence Artificial Intelligence Institute Shanghai Jiao Tong University Department of Cardiology the Affiliated Hospital of Qingdao University CEMS NCMIS HCMS MADIS Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematics University of Chinese Academy of Sciences Chinese Academy of Sciences Key Laboratory of Systems Health Science of Zhejiang Province School of Life Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Beijing Institute of Ophthalmology Beijing Tongren Hospital Capital Medical University Beijing Ophthalmology and Visual Sciences Key Laboratory Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangdong Provincial Clinical Research Center for Ocular Diseases Department of Renal Medicine Singapore General Hospital Sing Health-Duke Academic Medical Centre Department of Endocrinology Singapore General Hospital Department of Endocrinology Austin Health Department of Medicine The University of Melbourne(Austin Health) Australian Centre for Accelerating Diabetes Innovations(ACADI) The Univ

Diabetes poses a considerable global health challenge, with varying levels of diabetes knowledge among healthcare professionals, highlighting the importance of diabetes training. Large Language Models(LLMs)provide new insights into diabetes training, but their performance in diabetes-related queries remains uncertain, especially outside the English language like Chinese. We first evaluated the performance of ten LLMs: Chat GPT-3.5, Chat GPT-4.0, Google Bard, Lla MA-7B, Lla MA2-7B, Baidu ERNIE Bot, Ali Tongyi Qianwen, Med GPT, Huatuo GPT, and Chinese Lla MA2-7B on diabetes-related queries, based on the Chinese National Certificate Examination for Primary Diabetes Care in China(NCE-CPDC) and the English Specialty Certificate Examination in Endocrinology and Diabetes of Membership of the Royal College of Physicians of the United Kingdom. Second, we assessed the training of primary care physicians(PCPs) without and with the assistance of Chat GPT-4.0 in the NCE-CPDC examination to ascertain the reliability of LLMs as medical assistants. We found that Chat GPT-4.0 outperformed other LLMs in the English examination, achieving a passing accuracy of 62.50%, which was significantly higher than that of Google Bard, Lla MA-7B, and Lla MA2-7B. For the NCE-CPFC examination, Chat GPT-4.0, Ali Tongyi Qianwen, Baidu ERNIE Bot, Google Bard, Med GPT, and Chat GPT-3.5 successfully passed, whereas Lla MA2-7B, Huatuo GPT,Chinese LLa MA2-7B, and Lla MA-7B failed. Chat GPT-4.0(84.82%) surpassed all PCPs and assisted most PCPs in the NCE-CPDC examination(improving by 1%–6.13%). In summary, LLMs demonstrated outstanding competence for diabetes-related questions in both the Chinese and English language, and hold great potential to assist future diabetes training for physicians globally.

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Diabetic Retinopathy Assessment through Multitask Learning Approach on Heterogeneous Fundus Image Datasets

Ophthalmology Science

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ophthalmology science 2025年第5期5卷

作者： Hongkang Wu Kai Jin Yiyang Jing Wenyue Shen Yih Chung Tham Xiangji Pan Victor Koh Andrzej Grzybowski Juan Ye Zhejiang Provincial Key Laboratory of Ophthalmology Zhejiang Provincial Clinical Research Center for Eye Diseases Zhejiang Provincial Engineering Institute on Eye Diseases Eye Center of Second Affiliated Hospital School of Medicine Zhejiang University Hangzhou China College of Control Science and Engineering Zhejiang University Hangzhou China Centre for Innovation and Precision Eye Health Department of Ophthalmology National University of Singapore Singapore Ophthalmology and Visual Science Academic Clinical Program Singapore Eye Research Institute Singapore National Eye Centre Duke-NUS Medical School Singapore Institute for Research in Ophthalmology Foundation for Ophthalmology Development Poznan Poland

Objective To develop and validate an artificial intelligence (AI)-based system, Diabetic Retinopathy Analysis Model Assistant (DRAMA), for diagnosing diabetic retinopathy (DR) across multisource heterogeneous datasets and aimed at improving the diagnostic accuracy and efficiency. Design This was a cross-sectional study conducted at Zhejiang University Eye Hospital and approved by the ethics committee. Subjects The study included 1500 retinal images from 957 participants aged 18 to 83 years. The dataset was divided into 3 subdatasets: color fundus photography, ultra-widefield imaging, and portable fundus camera. Images were annotated by 3 experienced ophthalmologists. Methods The AI system was built using EfficientNet-B2, pretrained on the ImageNet dataset. It performed 11 multilabel tasks, including image type identification, quality assessment, lesion detection, and diabetic macular edema (DME) detection. The model used LabelSmoothingCrossEntropy and AdamP optimizer to enhance robustness and convergence. The system's performance was evaluated using metrics such as accuracy, sensitivity, specificity, and area under the curve (AUC). External validation was conducted using datasets from different clinical centers. Main Outcome Measures The primary outcomes measured were the accuracy, sensitivity, specificity, and AUC of the AI system in diagnosing DR. Results After excluding 218 poor-quality images, DRAMA demonstrated high diagnostic accuracy, with EfficientNet-B2 achieving 87.02% accuracy in quality assessment and 91.60% accuracy in lesion detection. Area under the curves were >0.95 for most tasks, with 0.93 for grading and DME detection. External validation showed slightly lower accuracy in some tasks but outperformed in identifying hemorrhages and DME. Diabetic Retinopathy Analysis Model Assistant diagnosed the entire test set in 86 ms, significantly faster than the 90 to 100 minutes required by humans. Conclusions Diabetic Retinopathy Analysis Model Assistant, an

关键词： Diabetic retinopathy Artificial intelligence Deep learning Multitask learning Multisource heterogeneous dataset AI artificial intelligence AUC area under the curve CFP color fundus photography DME diabetic macular edema DR diabetic retinopathy DRAMA Diabetic retinopathy analysis model assistant EX hard exudate HE hemorrhage MA microaneurysms NPDR nonproliferative diabetic retinopathy NV neovascularization PC portable fundus camera PDR proliferative diabetic retinopathy SE soft exudate UWF ultra-widefield imaging ZUEH Zhejiang University Eye Hospital

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Multimodal imaging of the mouse eye using visible light photoacoustic ophthalmoscopy and near-infrared-II optical coherence tomography

arXiv

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arXiv 2023年

作者： Haindl, Richard Bellemo, Valentina Rajendran, Praveenbalaji Tan, Bingyao Liu, Mengyang Zhou, Qifa Leitgeb, Rainer A. Drexler, Wolfgang Schmetterer, Leopold Pramanik, Manojit School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University 62 Nanyang Drive Singapore Singapore Centre for Medical Physics and Biomedical Engineering Medical University of Vienna Währinger Gürtel 18-20/4L Vienna Austria Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Program Nanyang Technological University Singapore Singapore Department of Radiation Oncology Stanford University StanfordCA94305 United States University of Southern California Department of Biomedical Engineering Los AngelesCA90033 United States Ophthalmology and Visual Sciences Academic Clinical Program Duke-NUS Medical School National University of Singapore Singapore Department of Clinical Pharmacology Medical University Vienna Vienna Austria Institute of Molecular and Clinical Ophthalmology Basel Switzerland Department of Electrical and Computer Engineering Iowa State University AmesIA50011 United States

Non-invasive imaging plays a crucial role in diagnosing and studying eye diseases. However, existing photoacoustic ophthalmoscopy (PAOM) techniques in mice have limitations due to handling restrictions, suboptimal optical properties, limited availability of light sources and permissible light fluence at the retina. This study introduces an innovative approach that utilizes Rose Bengal, a contrast agent, to enhance PAOM contrast. This enables visualization of deeper structures like the choroidal microvasculature and sclera in the mouse eye using visible light. The integration of near-infrared-II optical coherence tomography (NIR-II OCT) provides additional tissue contrast and insights into potential NIR-II PAOM capabilities. To optimize imaging, we developed a cost-effective 3D printable mouse eye phantom and a fully 3D printable tip/tilt mouse platform. This solution elevates PAOM to a user-friendly technology, which can be used to address pressing research questions concerning several ocular diseases such as myopia, glaucoma and/or age-related macular degeneration in the future. © 2023, CC BY.

关键词： Optical tomography

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Assessment of Early Fuchs Endothelial Corneal Dystrophy and CTG Trinucleotide Expansion Positivity using Scheimpflug Imaging

Ophthalmology Science

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ophthalmology science 2025年

作者： Stanley S.J. Poh Kai Yuan Tey Gary S.L. Peh Dawn J.H. Neo Hla Myint Htoon Evan K.L. Lee Yu Qiang Soh V Vinod Mootha Jodhbir S. Mehta Singapore National Eye Centre Singapore Ophthalmology and Visual Sciences Academic Clinical Program Duke-NUS Singapore Tissue Engineering and Cell Therapy Research Group Singapore Eye Research Institute Singapore Singapore Eye Research Institute Singapore Woodlands Health Department of Ophthalmology Singapore Department of Ophthalmology University of Texas Southwestern Medical Center Dallas Texas United States of America

Purpose To evaluate changes in corneal densitometry and optical aberrations using Scheimpflug imaging in early Fuchs Endothelial Corneal Dystrophy (FECD) and to assess their association with CTG trinucleotide repeat expansion. Design Retrospective cross-sectional study. Subjects FECD eyes diagnosed between 2018 and 2022 were included. Control eyes were recruited from healthy individuals undergoing routine cataract surgery. Methods All eyes underwent Scheimpflug imaging. Subclinical edema was defined by the presence of at least two of the following features (i) loss of parallel isopachs, (ii) displacement of the thinnest point, (iii) focal posterior depression. A subset of FECD subjects was genotyped for CTG repeat expansion. Main outcome measures Corneal densitometry, higher order aberrations (HOA) and pachymetry were quantified. Densitometry was measured across three layers (anterior 120μm, central, and posterior 60μm) and within four concentric annuli. HOA was analyzed separately for the anterior and posterior cornea. Results 108 FECD cases and 59 controls were included. Eyes with subclinical edema exhibited significantly higher densitometry and HOA across all corneal layers and zones compared to those without edema (all p<0.01). Subclinical edema was independently associated with increased posterior corneal HOA (β=15.068, 95% CI: 7.546 to 22.590) and elevated densitometry in the central 0–2 mm annulus across all layers. Among genotyped eyes, 22 (44.0%) were positive for CTG expansion, which was associated with lower densitometry in the anterior (β=-4.59, 95% CI: -7.28 to -1.92), central (β=-1.76, 95% CI: -2.79 to -0.74), and posterior (β=-3.05, 95% CI -5.67 to -0.44) layers of the central 0-2mm annulus. Conclusion Subclinical edema in FECD is associated with increased corneal densitometry and HOA, indicating early structural and optical changes. In contrast, the presence of CTG expansion was correlated with reduced central corneal densitometry, suggesting a poten

关键词： Scheimpflug Imaging Densitometry Optical Aberrations Fuchs Endothelial Corneal Dystrophy Genetics FECD Fuchs Endothelial Corneal Dystrophy HOA higher order aberrations GSU grayscale unit AUC area under curve

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

arXiv

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arXiv 2025年

作者： 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 Tham, Yih-Chung Department of Ophthalmology Yong Loo Lin School of Medicine National University of Singapore Singapore Centre for Innovation and Precision Eye Health Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore Singapore Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Ophthalmology and Visual Science Academic Clinical Program Duke-NUS Medical School Singapore Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Singapore Duke-NUS Medical School Singapore Singapore Precision Health Research Singapore Singapore Beijing Tsinghua Changgung Hospital Tsinghua Medicine Tsinghua University Beijing China Tsinghua Medicine Tsinghua University Beijing China Rothschild Foundation Hospital Institut Français de Myopie Paris France New York Eye and Ear Infirmary of Mount Sinai Icahn School of Medicine at Mount Sinai New York United States Suraj Eye Institute Nagpur India Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Centre for Medical Image Computing University College London London United Kingdom NIHR Biomedical Research Centre Moorfields Eye Hospital NHS Foundation Trust London United Kingdom Department of Medical Physics and Biomedical Engineering University College London London United Kingdom Institute of Ophthalmology University College London London United Kingdom

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 Copyright © 2025, The Authors. All rights reserved.

关键词： ophthalmology

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The Use of Large Language Models and Their Association With Enhanced Impact in Biomedical Research and Beyond

MedComm - Future Medicine

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MedComm - Future Medicine 2025年第2期4卷

作者： Cheng, Huzi Shi, Wen Sheng, Bin Lee, Aaron Y. Car, Josip Chaudhary, Varun Atanasov, Atanas G. Liu, Nan Qiu, Yue Chen, Qingyu Wong, Tien Yin Tham, Yih-Chung Zheng, Ying-Feng Department of Psychological and Brain Sciences Indiana University Bloomington IN United States Department of Ophthalmology the First Affiliated Hospital of Guangzhou Medical University Guangzhou China Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology University of Washington School of Medicine Seattle WA United States Centre for Population Health Sciences Lee Kong Chian School of Medicine Nanyang Technological University Singapore Singapore Department of Primary Care and Public Health School of Public Health Imperial College London London United Kingdom Department Health Research Methods Evidence and Impact McMaster University Hamilton ON Canada Department of Surgery McMaster University Hamilton ON Canada Ludwig Boltzmann Institute Digital Health and Patient Safety Medical University of Vienna Vienna Austria Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences Jastrzebiec Poland Centre for Quantitative Medicine Duke-NUS Medical School Singapore Singapore Tsinghua Medicine Tsinghua University Beijing China Yale School of Medicine Yale University United States Singapore Eye Research Institute Singapore National Eye Centre Singapore School of Clinical Medicine Beijing Tsinghua Changgung Hospital Beijing China Ophthalmology and Visual Sciences Academic Clinical Program Duke-NUS Medical School Singapore Centre for Innovation and Precision Eye Health and Department of Ophthalmology Yong Loo Lin School of Medicine National University of Singapore Singapore State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China

The release of ChatGPT in 2022 has catalyzed the adoption of large language models (LLMs) across diverse writing domains, including academic writing. However, this technological shift has raised critical questions regarding the prevalence of LLM usage in academic writing and its potential influence on the quality and impact of research articles. Here, we address these questions by analyzing preprint articles from arXiv, bioRxiv, and medRxiv between 2022 and 2024, employing a novel LLM usage detection tool. Our study reveals that LLMs have been widely adopted in biomedical and other types of academic writing since late 2022. Notably, we noticed that LLM usage is linked to an enhanced impact of research articles after examining their correlation, as measured by citation numbers. Furthermore, we observe that LLMs influence specific content types in academic writing, including hypothesis formulation, conclusion summarization, description of phenomena, and suggestions for future work. Collectively, our findings underscore the potential benefits of LLMs in scientific communication, suggesting that they may not only streamline the writing process but also enhance the dissemination and impact of research findings across disciplines. © 2025 The Author(s). MedComm - Future Medicine published by John Wiley & Sons Australia, Ltd on behalf of Sichuan International Medical Exchange & Promotion Association (SCIMEA).

关键词： academic writing chatGPT large language models LLM

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Spectral analysis of Electroretinography to differentiate autism spectrum disorder and attention deficit hyperactivity disorder.

Spectral analysis of Electroretinography to differentiate au...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Sultan Mohammad Manjur Md-Billal Hossain Paul A. Constable Dorothy A. Thompson Fernando Marmolejo-Ramos Irene O Lee Hugo F. Posada Quintero the Department of Biomedical Engineering University of Connecticut Storrs CT USA the Caring Futures Institute College of Nursing and Health Sciences Flinders University Adelaide SA Australia the UCL Great Ormond Street Institute of Child Health University College London London UK Clinical and Academic Department of Ophthalmology The Tony Kriss Visual Electrophysiology Unit Great Ormond Street Hospital for Children NHS Trust London UK the University of South Australia Online Adelaide Australia the Behavioral and Brain Sciences Unit Population Policy and Practice Program UCL Great Ormond Street Institute of Child Health University College London London UK

autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are both neurodevelopmental conditions that produce social interaction and executive functioning challenges but require very different therapeutic strategies. For that reason, early and accurate differentiation is important. However, their heterogeneity and overlap in symptoms make ASD and ADHD difficult to differentiate. The current diagnostic procedure to detect and distinguish ASD and ADHD is lengthy as it involves a comprehensive medical, developmental, and behavioral assessment. A more accessible and faster screening tool is needed to avoid delays in treatment. There is evidence that some retinal responses captured by the electroretinogram (ERG) are reduced in ASD subjects compared to neurotypicals whereas an opposite trend has been reported in ADHD, making ERG a promising tool for differentiating ASD and ADHD. However, previous ERG analyses based on amplitude and timing of ERG waves have exhibited limited success in differentiating ASD and ADHD. Recently, it has been found that time-varying spectral analysis of ERG allows for more accurate ASD detection compared to time-domain analysis. In this study, we evaluated the feasibility of differentiation of ASD and ADHD using features obtained by decomposing ERG using variable frequency complex demodulation (VFCDM). We used VFCDM features to train machine learning models and evaluated them using a subject independent validation approach. We achieved a maximum accuracy of 84% (87% sensitivity, 79% specificity), outperforming previous studies using ERG. Features from higher frequencies were found to be more important than features from lower *** Relevance—This study establishes high frequency ERG information as a potential biomarker to differentiate ASD and ADHD.

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