人工智能在老年性黄斑变性中的应用:促进诊断、预后和治疗。

IF 5.9 2区 医学 Q1 OPHTHALMOLOGY
Euna Lee, David Hunt, Yavuz Cakir, David Kuo, Ziqi Zhou, Miroslav Pajic, Majda Hadziahmetovic
{"title":"人工智能在老年性黄斑变性中的应用:促进诊断、预后和治疗。","authors":"Euna Lee, David Hunt, Yavuz Cakir, David Kuo, Ziqi Zhou, Miroslav Pajic, Majda Hadziahmetovic","doi":"10.1016/j.survophthal.2025.09.007","DOIUrl":null,"url":null,"abstract":"<p><p>Age-related macular degeneration (AMD) is a leading cause of irreversible vision loss in older adults. While anti-vascular endothelial growth factor (anti-VEGF) therapy and novel treatments for geographic atrophy have improved management, timely diagnosis and personalized intervention remain a challenge. Artificial intelligence (AI), such as machine learning and deep learning models, shows promise in AMD diagnosis, classification, and treatment planning. This review summarizes AI's recent advancements, highlights its clinical utility, and addresses key limitations for wider real-world implementation in AMD. We conducted systematic search of PubMed from its conception up to August 1, 2024. Studies utilizing AI-based algorithms for AMD management were identified and categorized into early detection/classification and prediction of disease progression/treatment response. Data extraction focused on AI model performance, imaging modalities, and clinical applicability. Of 193 records screened, 47 studies were included, in which 19 studies focused on early detection/classification and 28 on prediction of disease progression/treatment response. AI models demonstrated high accuracy in AMD classification and progression prediction, including in real-world settings. Prediction models for treatment response, particularly anti-VEGF therapy, could provide recommendations on optimizing injection timelines. Recent studies have also begun tackling previous challenges, such as algorithmic biases, limited generalizability, and AI's \"black-box\" nature. AI-based models offer significant potential to transform AMD care through timely detection and personalized treatment; however, clinical integration depends on improving model interpretability and validating tools across diverse populations. As AI continues to evolve, ongoing research is needed to refine AI models and support their translation into evidence-based, real-world applicability in AMD.</p>","PeriodicalId":22102,"journal":{"name":"Survey of ophthalmology","volume":" ","pages":""},"PeriodicalIF":5.9000,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence in age-related macular degeneration: Advancing diagnosis, prognosis, and treatment.\",\"authors\":\"Euna Lee, David Hunt, Yavuz Cakir, David Kuo, Ziqi Zhou, Miroslav Pajic, Majda Hadziahmetovic\",\"doi\":\"10.1016/j.survophthal.2025.09.007\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Age-related macular degeneration (AMD) is a leading cause of irreversible vision loss in older adults. While anti-vascular endothelial growth factor (anti-VEGF) therapy and novel treatments for geographic atrophy have improved management, timely diagnosis and personalized intervention remain a challenge. Artificial intelligence (AI), such as machine learning and deep learning models, shows promise in AMD diagnosis, classification, and treatment planning. This review summarizes AI's recent advancements, highlights its clinical utility, and addresses key limitations for wider real-world implementation in AMD. We conducted systematic search of PubMed from its conception up to August 1, 2024. Studies utilizing AI-based algorithms for AMD management were identified and categorized into early detection/classification and prediction of disease progression/treatment response. Data extraction focused on AI model performance, imaging modalities, and clinical applicability. Of 193 records screened, 47 studies were included, in which 19 studies focused on early detection/classification and 28 on prediction of disease progression/treatment response. AI models demonstrated high accuracy in AMD classification and progression prediction, including in real-world settings. Prediction models for treatment response, particularly anti-VEGF therapy, could provide recommendations on optimizing injection timelines. Recent studies have also begun tackling previous challenges, such as algorithmic biases, limited generalizability, and AI's \\\"black-box\\\" nature. AI-based models offer significant potential to transform AMD care through timely detection and personalized treatment; however, clinical integration depends on improving model interpretability and validating tools across diverse populations. As AI continues to evolve, ongoing research is needed to refine AI models and support their translation into evidence-based, real-world applicability in AMD.</p>\",\"PeriodicalId\":22102,\"journal\":{\"name\":\"Survey of ophthalmology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2025-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Survey of ophthalmology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.survophthal.2025.09.007\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"OPHTHALMOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Survey of ophthalmology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.survophthal.2025.09.007","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPHTHALMOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

年龄相关性黄斑变性(AMD)是老年人不可逆视力丧失的主要原因。虽然抗血管内皮生长因子治疗和新疗法改善了地理萎缩的管理,但及时诊断和个性化干预仍然是一个挑战。人工智能(AI),如机器学习和深度学习模型,在AMD的诊断、分类和治疗计划中显示出前景。这篇综述总结了人工智能的最新进展,强调了它的临床应用,并解决了在AMD中更广泛的现实世界实施的关键限制。我们对PubMed从创立到2024年8月1日进行了系统检索。利用基于人工智能的算法进行AMD管理的研究被确定并分类为早期发现/分类和疾病进展/治疗反应预测。数据提取侧重于AI模型性能、成像方式和临床适用性。在筛选的193项记录中,纳入了47项研究,其中19项研究侧重于早期发现/分类,28项研究侧重于疾病进展/治疗反应的预测。人工智能模型在AMD分类和进展预测方面表现出很高的准确性,包括在现实环境中。治疗反应的预测模型,特别是抗vegf治疗,可以为优化注射时间提供建议。最近的研究也开始解决以前的挑战,比如算法偏见、有限的泛化性以及人工智能的“黑箱”性质。基于人工智能的模型通过及时检测和个性化治疗,为改变AMD的护理提供了巨大的潜力;然而,临床整合依赖于提高模型的可解释性和跨不同人群的验证工具。随着人工智能的不断发展,需要持续的研究来完善人工智能模型,并支持它们在AMD中转化为基于证据的、现实世界的适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial intelligence in age-related macular degeneration: Advancing diagnosis, prognosis, and treatment.

Age-related macular degeneration (AMD) is a leading cause of irreversible vision loss in older adults. While anti-vascular endothelial growth factor (anti-VEGF) therapy and novel treatments for geographic atrophy have improved management, timely diagnosis and personalized intervention remain a challenge. Artificial intelligence (AI), such as machine learning and deep learning models, shows promise in AMD diagnosis, classification, and treatment planning. This review summarizes AI's recent advancements, highlights its clinical utility, and addresses key limitations for wider real-world implementation in AMD. We conducted systematic search of PubMed from its conception up to August 1, 2024. Studies utilizing AI-based algorithms for AMD management were identified and categorized into early detection/classification and prediction of disease progression/treatment response. Data extraction focused on AI model performance, imaging modalities, and clinical applicability. Of 193 records screened, 47 studies were included, in which 19 studies focused on early detection/classification and 28 on prediction of disease progression/treatment response. AI models demonstrated high accuracy in AMD classification and progression prediction, including in real-world settings. Prediction models for treatment response, particularly anti-VEGF therapy, could provide recommendations on optimizing injection timelines. Recent studies have also begun tackling previous challenges, such as algorithmic biases, limited generalizability, and AI's "black-box" nature. AI-based models offer significant potential to transform AMD care through timely detection and personalized treatment; however, clinical integration depends on improving model interpretability and validating tools across diverse populations. As AI continues to evolve, ongoing research is needed to refine AI models and support their translation into evidence-based, real-world applicability in AMD.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Survey of ophthalmology
Survey of ophthalmology 医学-眼科学
CiteScore
10.30
自引率
2.00%
发文量
138
审稿时长
14.8 weeks
期刊介绍: Survey of Ophthalmology is a clinically oriented review journal designed to keep ophthalmologists up to date. Comprehensive major review articles, written by experts and stringently refereed, integrate the literature on subjects selected for their clinical importance. Survey also includes feature articles, section reviews, book reviews, and abstracts.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信