光素血管学泄漏的检测和量化:从手动分级到机器学习的进步
Uday Pratap Singh Parmar1, Atul Arora2, Aniruddha Agarwal3
1Government Medical College and Hospital, Sector 32, Chandigarh, India, 160047.
Survey of ophthalmology
|September 3, 2025
概括
人工智能 (AI) 将光素血管学 (FA) 的解释从主观转变为客观. 人工智能驱动的泄漏量化为视网膜血管疾病提供了标准化指标,改善了患者护理和研究.
科学领域:
- 眼科 眼科
- 医学成像
- 人工智能
背景情况:
- 光素血管学 (FA) 对于诊断视网膜血管疾病至关重要,但依赖于主观解释.
- 目前评估视网膜血管泄漏的方法缺乏标准化和可重复性.
- 人工智能 (AI) 为客观量化提供了一个潜在的解决方案.
研究的目的:
- 审查AI在量化光素血管学泄漏的演变和应用.
- 突出人工智能驱动的客观指标在视网膜血管疾病管理中的好处.
- 探索人工智能在定义"最小残留疾病"等新临床终点方面的潜力.
主要方法:
- 在PubMed,Embase和Scopus的综合文献搜索.
- 包括手动,半自动化和基于人工智能的人类FA泄漏评估的研究.
- 用于细分和测量血管图泄漏的AI技术的分析.
主要成果:
- 人工智能可以开发标准化和可重复的泄漏指数.
- 基于人工智能的量化与疾病严重程度相关,并有助于治疗决策.
- 人工智能有助于患者分层和对治疗反应的敏感监测.
结论:
- 人工智能驱动的FA泄漏量化比主观分级具有显著的进展.
- 由人工智能衍生的客观数字生物标志物增强了视网膜疾病的临床护理和研究.
- 这种方法有望通过精确的疾病评估和监测改善结果.
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