基于前段OCT的角度封闭的深度学习分类.
Jing Shan1, Zhixi Li2, Ping Ma3
1Department of Ophthalmology, University of California, San Francisco, San Francisco, California.
Ophthalmology. Glaucoma
|July 12, 2023
概括
卷积神经网络 (CNN) 模型显示,使用OCT扫描识别初级角关闭疾病 (PACD) 是有前途的. 虽然对于一般的PACD检测是有效的,但区分像初级近视角疑似 (PACS) 和初级近视角玻璃眼 (PACG) 这样的阶段需要进一步细化.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 主要视角闭合疾病 (PACD) 是视力丧失的重要原因之一.
- 准确和有效的诊断对于及时干预至关重要.
- 前段光学连贯性断层扫描 (AS-OCT) 提供了眼睛结构的详细成像.
研究的目的:
- 评估卷积神经网络 (CNN) 模型的性能和通用性,以识别PACD.
- 评估CNNs使用AS-OCT图像区分PACD阶段的能力.
- 确定CNN在高通量查和诊断中的有效性.
主要方法:
- 一项涉及中国和新加坡眼科中心患者的横截面研究.
- 使用AS-OCT图像对三个CNN分类器进行培训和验证.
- 第1分类:控制与PACS与PAC+PACG;第2分类:控制与PACD;第3分类:PACS与PAC+PACG.
- 在独立验证套件和跨国测试套件上进行评估.
主要成果:
- 分类器2表现出强大的概括性,在验证时达到0.96的曲线下的面积 (AUC),在测试组中达到0.95.
- 分类器1在区域上表现良好 (AUC 0.96),但在测试组中概括性降低 (AUC 0.84).
- 第三种分类表现最差,AUC为0.83 (验证) 和0.64 (测试).
结论:
- 通过使用AS-OCT,CNN模型可以有效地将PACD与健康对照区分开来,具有良好的跨队列概括性.
- 在区分初级近视角疑似病 (PACS) 和初级近视角玻璃眼 (PACG) 之间,CNN的性能适度.
- 可能需要进一步的研究来提高PACD分期中的CNN准确性.
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