以前段OCT为基础的算法在针对初级角关闭疾病的机会性查中的性能
Dapeng Mou1, Jin Wang1,2, Yue Wang1,2
1Beijing Tongren Eye Center, Beijing Key Laboratory of Ophthalmology and Visual Science, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Heliyon
|April 10, 2024
概括
深度学习算法在使用前段光学连贯断层扫描 (ASOCT) 参数进行初级角关闭疾病 (PACD) 机会性查方面表现出色. 这项研究表明了开发PACD查工具的有希望的方法.
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:
- 主要角关闭性疾病 (PACD) 是全球不可逆转失明的重要原因.
- 早期检测和查对于预防与PACD相关的视力损失至关重要.
- 目前的选方法可能在可访问性和效率方面存在局限性.
研究的目的:
- 评估深度学习算法的诊断性能,用于PACD的机会性查.
- 评估来自ASOCT的组合前段参数对PACD检测的有用性.
- 探索人工智能驱动工具在提高PACD查效率方面的潜力.
主要方法:
- 一项基于医院的观察性横截面研究招募了199名患者 (109名PACD,90名对照).
- 前段光学连贯断层扫描 (ASOCT) 用于在明暗条件下评估前腔,角度,虹膜和镜片参数.
- 逆向后勤回归和接收器运行特征 (ROC) 曲线分析被用于开发和评估预测模型.
主要成果:
- 最终的预测模型包括在光线条件下测量的角度开口距离,前腔区域和虹膜曲率.
- 在ROC曲线下的面积 (AUC) 达到0.968,表明出色的诊断性能.
- 该模型在PACD检测方面获得了91.74%的灵敏度和91.11%的特异性.
结论:
- 基于ASOCT的深度学习算法在机会性PACD查方面表现出色的诊断性能.
- 这些发现支持开发用于PACD查的角度闭合概率评分系统.
- 人工智能驱动的工具对改善PACD的早期检测和管理有显著的希望.
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