人类专家分类与nAMD,DME和BRVOVO中流体体积的自动定量
Felix Goldbach1, Bianca S Gerendas1, Oliver Leingang1,2
1Department of Ophthalmology and Optometry, Medical University of Vienna, Waehringer Guertel 18-20, Vienna, 1090, Austria.
Scientific reports
|August 19, 2025
概括
一个自动化的深度学习算法在OCT扫描中准确地检测 retinal fluid (IRF) 和 subretinal fluid (SRF),与视网膜疾病的专家人类分级器相匹配. 这项技术为治疗决策和监测提供了精确的体积数据.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 精确检测和量化视网膜内液 (IRF) 和子视网膜液 (SRF) 对于管理视网膜疾病至关重要,如新血管与年龄相关的黄斑变性 (nAMD),糖尿病黄斑 (DME) 和分支视网膜静脉封闭 (BRVO).
- 目前的评分依赖于专业的人类评分员,这可能是主观的,耗时的.
研究的目的:
- 为了比较自动深度学习算法的性能与认证的人类分级器在识别和量化IRF和SRF在光学连贯断层扫描 (OCT) 扫描中的性能.
- 评估该算法在为各种视网膜液相关疾病提供精确体积信息方面的实用性.
主要方法:
- 我们对来自维也纳阅读中心 (VRC) 影像数据库的多中心临床试验数据进行了后期分析.
- 使用自动化的深度学习算法 (RetInSight) 来计算来自6898个OCT扫描的IRF和SRF卷.
- 算法衍生的液体体积与经验丰富的VRC人类分级器分级的液体存在量进行了比较.
主要成果:
- 深度学习算法与人类专家对不同疾病的评分有很高的一致性.
- 在nAMD,DME和BRVO中检测IRF的曲线下面面积 (AUC) 值分别为0.94,0.92和0.98.
- 在nAMD,DME和BRVO中检测SRF的AUC值分别为0.89,0.95和0.92,表明表现强.
结论:
- 自动化的深度学习算法显示出与人类专家对IRF和SRF检测的分级高度一致.
- 该算法提供了精确的体积定量,在临床实践中为标准化治疗决策支持和疾病监测提供了宝贵的工具.
- 自动流体量化代表了在专家层面管理视网膜流体相关疾病的可行进展.
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