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通过光盘照片或OCT扫描进行深度学习
Abhilash Katuru1, In Young Chung2, Iyad Majid1
1Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA.
Ophthalmology science
|September 2, 2025
概括
使用光连贯断层扫描 (OCT) 视网膜神经纤维层厚度 (RNFLT) 地图的深度学习 (DL) 模型比光盘照片 (DP) 模型更准确地检测到青光眼. 人口群体的表现是一致的, 尽管差异突显出需要公平的数据集.
科学领域:
- 眼科 眼科
- 医学的人工智能
- 医学成像分析
背景情况:
- 青光眼的诊断依赖于检测功能视野 (VF) 的损害.
- 深度学习 (DL) 模型显示了自动化眼检测的前景.
- 使用不同成像方式的DL模型对诊断准确性至关重要.
研究的目的:
- 使用OCT RNFLT地图与磁盘照片 (DPs) 来检测青光眼的DL模型的准确性.
- 评估这些DL模型在不同的人口群体 (种族,性别,种族) 的诊断性能.
主要方法:
- 以2011年至2022年的OCT和DP数据集进行回顾性队列研究.
- 在OCT RNFLT地图和DP上训练的DL模型的开发,用于基于VF损伤的青光眼检测.
- 包括16,936个高质量的OCT扫描和可靠的VF测试.
主要成果:
- 与基于DP的模型相比,基于OCT的DL模型实现了0. 90的曲线下面面积 (AUC) 显著增加 (AUC = 0. 86,P < 0. 005).
- 跨种族,性别和种族人口群体的超越性表现一致.
- 这两种模型都显示了不同人口群体的精度差异,表明了潜在的偏见.
结论:
- 与基于DP的模型相比,使用OCT RNFLT地图的DL模型显示出更高的青光眼检测准确度.
- RNFLT测量的客观和定量性质可能有助于提高性能.
- 观察到的人口差异强调了在开发公平的人工智能诊断工具时对多样化和公平的数据集的关键需求.
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