Background:The advent of mobile health(mHealth)applications has fundamentally transformed the healthcare landscape,particularly within the field of ophthalmology,by providing unprecedented opportunities for remote dia...Background:The advent of mobile health(mHealth)applications has fundamentally transformed the healthcare landscape,particularly within the field of ophthalmology,by providing unprecedented opportunities for remote diagnosis,monitoring,and treatment.Ocular surface diseases,including dry eye disease(DED),are the most common eye diseases that can be detected by mHealth applications.However,most remote artificial intelligence(AI)systems for ocular surface disease detection are predominantly based on self-reported data collected through interviews,which lack the rigor of clinical evidence.These constraints underscore the need to develop robust,evidence-based AI frameworks that incorporate objective health indicators to improve the reliability and clinical utility of remote health applications.Methods:Two novel deep learning(DL)models,YoloTR and YoloMBTR,were developed to detect key ocular surface indicators(OSIs),including tear meniscus height(TMH),non-invasive Keratograph break-up time(NIKBUT),ocular redness,lipid layer,and trichiasis.Additionally,back propagation neural networks(BPNN)and universal network for image segmentation(U-Net)were employed for image classification and segmentation of meibomian gland images to predict Demodex mite infections.These models were trained on a large dataset from high-resolution devices,including Keratograph 5M and various mobile platforms(Huawei,Apple,and Xiaomi).Results:The proposed DL models of YoloMBTR and YoloTR outperformed baseline you only look once(YOLO)models(Yolov5n,Yolov6n,and Yolov8n)across multiple performance metrics,including test average precision(AP),validation AP,and overall accuracy.These two models also exhibit superior performance compared to machine plug-in models in KG5M when benchmarked against the gold standard.Using Python's Matplotlib for visualization and SPSS for statistical analysis,this study introduces an innovative proof-of-concept framework leveraging quantitative AI analysis to address critical challenges in ophthalmology.By integrating advanced DL models,the framework offers a robust approach for detecting and quantifying OSIs with a high degree of precision.This methodological advancement bridges the gap between AI-driven diagnostics and clinical ophthalmology by translating complex ocular data into actionable insights.Conclusions:Integrating AI with clinical laboratory data holds significant potential for advancing mobile eye health(MeHealth),particularly in detecting OSIs.This study aims to explore this integration,focusing on improving diagnostic accuracy and accessibility.This study demonstrates the potential of AI-driven tools in ophthalmic diagnostics,paving the way for reliable,evidence-based solutions in remote patient monitoring and continuous care.The results contribute to the foundation of AI-powered health systems that can extend beyond ophthalmology,improving healthcare accessibility and patient outcomes across various domains.展开更多
This study explores the potential of Artificial Intelligence(AI)in early screening and prognosis of Dry Eye Disease(DED),aiming to enhance the accuracy of therapeutic approaches for eye-care practitioners.Despite the ...This study explores the potential of Artificial Intelligence(AI)in early screening and prognosis of Dry Eye Disease(DED),aiming to enhance the accuracy of therapeutic approaches for eye-care practitioners.Despite the promising opportunities,challenges such as diverse diagnostic evidence,complex etiology,and interdisciplinary knowledge integration impede the interpretability,reliability,and applicability of AI-based DED detection methods.The research conducts a comprehensive review of datasets,diagnostic evidence,and standards,as well as advanced algorithms in AI-based DED detection over the past five years.The DED diagnostic methods are categorized into three groups based on their relationship with AI techniques:(1)those with ground truth and/or comparable standards,(2)potential AI-based methods with significant advantages,and(3)supplementary methods for AI-based DED detection.The study proposes suggested DED detection standards,the combination of multiple diagnostic evidence,and future research directions to guide further investigations.Ultimately,the research contributes to the advancement of ophthalmic disease detection by providing insights into knowledge foundations,advanced methods,challenges,and potential future perspectives,emphasizing the significant role of AI in both academic and practical aspects of ophthalmology.展开更多
基金funded by the National Natural Science Foundation of China(Grant/Award Numbers:U22A2041,62372047)Shenzhen Key Laboratory of Intelligent Bioinformatics(Grant/Award Number:ZDSYS20220422103800001)+1 种基金Shenzhen Science and Technology Program(Grant/Award Number:KQTD20200820113106007)the Characteristic Innovation Project of Ordinary Universities in Guangdong Province(Grant/Award Number:2024KTSCX226).
文摘Background:The advent of mobile health(mHealth)applications has fundamentally transformed the healthcare landscape,particularly within the field of ophthalmology,by providing unprecedented opportunities for remote diagnosis,monitoring,and treatment.Ocular surface diseases,including dry eye disease(DED),are the most common eye diseases that can be detected by mHealth applications.However,most remote artificial intelligence(AI)systems for ocular surface disease detection are predominantly based on self-reported data collected through interviews,which lack the rigor of clinical evidence.These constraints underscore the need to develop robust,evidence-based AI frameworks that incorporate objective health indicators to improve the reliability and clinical utility of remote health applications.Methods:Two novel deep learning(DL)models,YoloTR and YoloMBTR,were developed to detect key ocular surface indicators(OSIs),including tear meniscus height(TMH),non-invasive Keratograph break-up time(NIKBUT),ocular redness,lipid layer,and trichiasis.Additionally,back propagation neural networks(BPNN)and universal network for image segmentation(U-Net)were employed for image classification and segmentation of meibomian gland images to predict Demodex mite infections.These models were trained on a large dataset from high-resolution devices,including Keratograph 5M and various mobile platforms(Huawei,Apple,and Xiaomi).Results:The proposed DL models of YoloMBTR and YoloTR outperformed baseline you only look once(YOLO)models(Yolov5n,Yolov6n,and Yolov8n)across multiple performance metrics,including test average precision(AP),validation AP,and overall accuracy.These two models also exhibit superior performance compared to machine plug-in models in KG5M when benchmarked against the gold standard.Using Python's Matplotlib for visualization and SPSS for statistical analysis,this study introduces an innovative proof-of-concept framework leveraging quantitative AI analysis to address critical challenges in ophthalmology.By integrating advanced DL models,the framework offers a robust approach for detecting and quantifying OSIs with a high degree of precision.This methodological advancement bridges the gap between AI-driven diagnostics and clinical ophthalmology by translating complex ocular data into actionable insights.Conclusions:Integrating AI with clinical laboratory data holds significant potential for advancing mobile eye health(MeHealth),particularly in detecting OSIs.This study aims to explore this integration,focusing on improving diagnostic accuracy and accessibility.This study demonstrates the potential of AI-driven tools in ophthalmic diagnostics,paving the way for reliable,evidence-based solutions in remote patient monitoring and continuous care.The results contribute to the foundation of AI-powered health systems that can extend beyond ophthalmology,improving healthcare accessibility and patient outcomes across various domains.
基金funded by the National Natural Science Foundation of China Natural(Nos.U22A2041,82071915,and 62372047)the Shenzhen Key Laboratory of Intelligent Bioinformatics(No.ZDSYS20220422103800001)+5 种基金the Shenzhen Science and Technology Program(No.KQTD20200820113106007)the Guangdong Basic and Applied Basic Research Foundation(No.2022A1515220015)the Zhuhai Technology and Research Foundation(Nos.ZH22036201210034PWC,2220004000131,and 2220004002412)the Project of Humanities and Social Science of MOE(Ministry of Education in China)(No.22YJCZH213)the Science and Technology Research Program of Chongqing Municipal Education Commission(Nos.KJZD-K202203601,KJQN0202203605,and KJQN202203607)the Natural Science Foundation of Chongqing China(No.cstc2021jcyj-msxmX1108).
文摘This study explores the potential of Artificial Intelligence(AI)in early screening and prognosis of Dry Eye Disease(DED),aiming to enhance the accuracy of therapeutic approaches for eye-care practitioners.Despite the promising opportunities,challenges such as diverse diagnostic evidence,complex etiology,and interdisciplinary knowledge integration impede the interpretability,reliability,and applicability of AI-based DED detection methods.The research conducts a comprehensive review of datasets,diagnostic evidence,and standards,as well as advanced algorithms in AI-based DED detection over the past five years.The DED diagnostic methods are categorized into three groups based on their relationship with AI techniques:(1)those with ground truth and/or comparable standards,(2)potential AI-based methods with significant advantages,and(3)supplementary methods for AI-based DED detection.The study proposes suggested DED detection standards,the combination of multiple diagnostic evidence,and future research directions to guide further investigations.Ultimately,the research contributes to the advancement of ophthalmic disease detection by providing insights into knowledge foundations,advanced methods,challenges,and potential future perspectives,emphasizing the significant role of AI in both academic and practical aspects of ophthalmology.
