Yundi Gao, Fen Xiong, Jian Xiong, Zidan Chen, Yucai Lin, Xinjing Xia, Yulan Yang, Guodong Li, Yunwei Hu
{"title":"人工智能在老年性黄斑变性中应用的最新进展。","authors":"Yundi Gao, Fen Xiong, Jian Xiong, Zidan Chen, Yucai Lin, Xinjing Xia, Yulan Yang, Guodong Li, Yunwei Hu","doi":"10.1136/bmjophth-2024-001903","DOIUrl":null,"url":null,"abstract":"<p><p>Recent advancements in ophthalmology have been driven by the incorporation of artificial intelligence (AI), especially in diagnosing, monitoring treatment and predicting outcomes for age-related macular degeneration (AMD). AMD is a leading cause of irreversible vision loss worldwide, and its increasing prevalence among the ageing population presents a significant challenge for managing the disease. AI holds considerable promise in tackling this issue. This paper provides an overview of the latest developments in AI applications for AMD. However, current limitations include insufficient and unbalanced data, lack of interpretability in models, dependence on data quality and limited generality.</p>","PeriodicalId":9286,"journal":{"name":"BMJ Open Ophthalmology","volume":"9 1","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11580293/pdf/","citationCount":"0","resultStr":"{\"title\":\"Recent advances in the application of artificial intelligence in age-related macular degeneration.\",\"authors\":\"Yundi Gao, Fen Xiong, Jian Xiong, Zidan Chen, Yucai Lin, Xinjing Xia, Yulan Yang, Guodong Li, Yunwei Hu\",\"doi\":\"10.1136/bmjophth-2024-001903\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Recent advancements in ophthalmology have been driven by the incorporation of artificial intelligence (AI), especially in diagnosing, monitoring treatment and predicting outcomes for age-related macular degeneration (AMD). AMD is a leading cause of irreversible vision loss worldwide, and its increasing prevalence among the ageing population presents a significant challenge for managing the disease. AI holds considerable promise in tackling this issue. This paper provides an overview of the latest developments in AI applications for AMD. However, current limitations include insufficient and unbalanced data, lack of interpretability in models, dependence on data quality and limited generality.</p>\",\"PeriodicalId\":9286,\"journal\":{\"name\":\"BMJ Open Ophthalmology\",\"volume\":\"9 1\",\"pages\":\"\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2024-11-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11580293/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMJ Open Ophthalmology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1136/bmjophth-2024-001903\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"OPHTHALMOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMJ Open Ophthalmology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1136/bmjophth-2024-001903","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"OPHTHALMOLOGY","Score":null,"Total":0}
Recent advances in the application of artificial intelligence in age-related macular degeneration.
Recent advancements in ophthalmology have been driven by the incorporation of artificial intelligence (AI), especially in diagnosing, monitoring treatment and predicting outcomes for age-related macular degeneration (AMD). AMD is a leading cause of irreversible vision loss worldwide, and its increasing prevalence among the ageing population presents a significant challenge for managing the disease. AI holds considerable promise in tackling this issue. This paper provides an overview of the latest developments in AI applications for AMD. However, current limitations include insufficient and unbalanced data, lack of interpretability in models, dependence on data quality and limited generality.