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
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.
