Background:Research innovations inocular disease screening,diagnosis,and management have been boosted by deep learning(DL)in the last decade.To assess historical research trends and current advances,we conducted an ar...Background:Research innovations inocular disease screening,diagnosis,and management have been boosted by deep learning(DL)in the last decade.To assess historical research trends and current advances,we conducted an artificial intelligence(AI)-human hybrid analysis of publications on DL in ophthalmology.Methods:All DL-related articles in ophthalmology,which were published between 2012 and 2022 from Web of Science,were included.500 high-impact articles annotated with key research information were used to fine-tune a large language models(LLM)for reviewing medical literature and extracting information.After verifying the LLM's accuracy in extracting diseases and imaging modalities,we analyzed trend of DL in ophthalmology with 2535 articles.Results:Researchers using LLM for literature analysis were 70%(P=0.0001)faster than those who did not,while achieving comparable accuracy(97%versus 98%,P=0.7681).The field of DL in ophthalmology has grown 116%annually,paralleling trends of the broader DL domain.The publications focused mainly on diabetic retinopathy(P=0.0003),glaucoma(P=0.0011),and age-related macular diseases(P=0.0001)using retinal fundus photographs(FP,P=0.0015)and optical coherence tomography(OCT,P=0.0001).DL studies utilizing multimodal images have been growing,with FP and OCT combined being the most frequent.Among the 500 high-impact articles,laboratory studies constituted the majority at 65.3%.Notably,a discernible decline in model accuracy was observed when categorizing by study design,notwithstanding its statistical insignificance.Furthermore,43 publicly available ocular image datasets were summarized.Conclusion:This study has characterized the landscape of publications on DL in ophthalmology,by identifying the trends and breakthroughs among research topics and the fast-growing areas.This study provides an efficient framework for combined AI-human analysis to comprehensively assess the current status and future trends in the field.展开更多
Blindness prevention has been an important national policy in China.Previous strategies,such as deploying experienced cataract surgeons to rural areas and assisting in building local ophthalmology centers,had successf...Blindness prevention has been an important national policy in China.Previous strategies,such as deploying experienced cataract surgeons to rural areas and assisting in building local ophthalmology centers,had successfully decreased the prevalence of visual impairment and blindness.However,new challenges arise with the aging population and the shift of the disease spectrum towards age-related eye diseases and myopia.With the constant technological boom,digital healthcare innovations in ophthalmology could immensely enhance screening and diagnosing capabilities.Artificial intelligence(AI)and telemedicine have been proven valuable in clinical ophthalmology settings.Moreover,the integration of cutting-edge communication technology and AI in mobile clinics and remote surgeries is on the horizon,potentially revolutionizing blindness prevention and ophthalmic healthcare.The future of blindness prevention in China is poised to undergo significant transformation,driven by emerging challenges and new opportunities.展开更多
基金supported by the National Natural Science Foundation of China(82000946)Guangdong Natural Science Funds for Distinguished Young Scholar(2023B1515020100)+1 种基金the Natural Science Foundation of Guangdong Province(2021A1515012238)the Science and Technology Program of Guangzhou(202201020522 and 202201020337).
文摘Background:Research innovations inocular disease screening,diagnosis,and management have been boosted by deep learning(DL)in the last decade.To assess historical research trends and current advances,we conducted an artificial intelligence(AI)-human hybrid analysis of publications on DL in ophthalmology.Methods:All DL-related articles in ophthalmology,which were published between 2012 and 2022 from Web of Science,were included.500 high-impact articles annotated with key research information were used to fine-tune a large language models(LLM)for reviewing medical literature and extracting information.After verifying the LLM's accuracy in extracting diseases and imaging modalities,we analyzed trend of DL in ophthalmology with 2535 articles.Results:Researchers using LLM for literature analysis were 70%(P=0.0001)faster than those who did not,while achieving comparable accuracy(97%versus 98%,P=0.7681).The field of DL in ophthalmology has grown 116%annually,paralleling trends of the broader DL domain.The publications focused mainly on diabetic retinopathy(P=0.0003),glaucoma(P=0.0011),and age-related macular diseases(P=0.0001)using retinal fundus photographs(FP,P=0.0015)and optical coherence tomography(OCT,P=0.0001).DL studies utilizing multimodal images have been growing,with FP and OCT combined being the most frequent.Among the 500 high-impact articles,laboratory studies constituted the majority at 65.3%.Notably,a discernible decline in model accuracy was observed when categorizing by study design,notwithstanding its statistical insignificance.Furthermore,43 publicly available ocular image datasets were summarized.Conclusion:This study has characterized the landscape of publications on DL in ophthalmology,by identifying the trends and breakthroughs among research topics and the fast-growing areas.This study provides an efficient framework for combined AI-human analysis to comprehensively assess the current status and future trends in the field.
基金supported by the Science and Technology Program of Guangzhou(202201020337)the Science and Technology Planning Projects of Guangdong Province(2021B1111610006)+4 种基金the Science and Technology Program of Guangzhou(2024B03J1233)the National Natural Science Foundation of China(82171035)the High-level Science and Technology Journals Projects of Guangdong Province(2021B1212010003)the National Natural Science Foundation of China(82201237)the China Postdoctoral Science Foundation(2023T160751).
文摘Blindness prevention has been an important national policy in China.Previous strategies,such as deploying experienced cataract surgeons to rural areas and assisting in building local ophthalmology centers,had successfully decreased the prevalence of visual impairment and blindness.However,new challenges arise with the aging population and the shift of the disease spectrum towards age-related eye diseases and myopia.With the constant technological boom,digital healthcare innovations in ophthalmology could immensely enhance screening and diagnosing capabilities.Artificial intelligence(AI)and telemedicine have been proven valuable in clinical ophthalmology settings.Moreover,the integration of cutting-edge communication technology and AI in mobile clinics and remote surgeries is on the horizon,potentially revolutionizing blindness prevention and ophthalmic healthcare.The future of blindness prevention in China is poised to undergo significant transformation,driven by emerging challenges and new opportunities.
