Detection and quantification of fluorescein angiography leakage: From manual grading to advances in machine learning.

IF 5.9 2区 医学 Q1 OPHTHALMOLOGY
Uday Pratap Singh Parmar, Atul Arora, Aniruddha Agarwal, Sapna Gangaputra, Rupesh Agrawal, Vishali Gupta
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引用次数: 0

Abstract

Fluorescein angiography (FA) has long been a cornerstone for evaluating retinal vascular leakage in diseases like uveitis, diabetic retinopathy, and macular degeneration, but its interpretation relies on subjective grading that can vary between clinicians. With the emergence of artificial intelligence (AI), there is a push to transform this qualitative assessment into objective, quantifiable metrics. We conducted a comprehensive literature search using PubMed, Embase, and Scopus, combining keywords and MeSH terms related to fluorescein angiography leakage, artificial intelligence, and retinal vascular diseases. Studies were included if they assessed FA leakage using manual, semi-automated, or AI-based methods and were peer-reviewed, published in English, and focused on human subjects. Our review charts the evolution from manual grading to modern machine learning techniques that segment and measure leakage using various angiograms. These AI-based approaches enable standardized, reproducible leakage indices that correlate with disease severity, inform treatment decisions, stratify high-risk patients, and facilitate sensitive monitoring of therapeutic response. We also introduce the concept of "minimal residual disease" in this context. By moving from coarse, subjective estimations to precise digital biomarkers, AI-driven FA leakage quantification promises to improve clinical care and research endpoints in retinal disease.

荧光素血管造影渗漏的检测和定量:从人工分级到机器学习的进展。
荧光素血管造影(FA)长期以来一直是评估葡萄膜炎、糖尿病视网膜病变和黄斑变性等疾病视网膜血管渗漏的基础,但其解释依赖于主观评分,临床医生之间可能存在差异。随着人工智能(AI)的出现,人们正在推动将这种定性评估转变为客观的、可量化的指标。我们使用PubMed、Embase和Scopus进行了全面的文献检索,结合荧光素血管造影渗漏、人工智能和视网膜血管疾病相关的关键词和MeSH术语。如果研究使用手动、半自动或基于人工智能的方法评估FA泄漏,并经过同行评审,以英文发表,并以人类受试者为重点,则纳入研究。我们的回顾描绘了从人工分级到现代机器学习技术的演变,这些技术使用各种血管造影来分割和测量泄漏。这些基于人工智能的方法实现了与疾病严重程度相关的标准化、可重复的泄漏指数,为治疗决策提供信息,对高危患者进行分层,并促进对治疗反应的敏感监测。在这种情况下,我们还引入了“最小残留病”的概念。通过从粗糙的主观估计到精确的数字生物标志物,人工智能驱动的FA泄漏量化有望改善视网膜疾病的临床护理和研究终点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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.
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