深度学习方法使用三维OCT成像来预测地理缩进展
Kenta Yoshida1, Neha Anegondi2, Adam Pely3
1Clinical Pharmacology, Genentech, Inc., South San Francisco, CA, USA.
Translational vision science & technology
|February 6, 2025
概括
使用3D光学连贯断层扫描 (OCT) 图像的深度学习模型有效地预测地理缩 (GA) 病变大小和与年龄相关的黄斑变性 (AMD) 的增长率. 圆形区域和视网膜色素上皮层是准确预测的关键.
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:
- 与年龄相关的黄斑变性 (AMD) 是导致视力丧失的主要原因.
- 地理缩 (GA) 是一种晚期的AMD,其特征是视网膜逐渐退化.
- 准确预测GA进展对于监测疾病和评估治疗非常重要.
研究的目的:
- 评估不同处理方法对3D光学连贯断层扫描 (OCT) 图像的性能.
- 确定这些方法在与深度学习模型一起使用时的有效性,以预测GA损伤面积和生长速度.
- 使用OCT数据确定最佳的图像处理策略,用于GA进展预测.
主要方法:
- 从lampalizumab临床试验中使用的OCT体积,包括1219只眼睛用于开发和442只眼睛用于评估.
- 评估了四种OCT图像处理方法:面对面强度图,SLIVER-net,3D卷积神经网络 (CNN) 和细分衍生图.
- 采用CNN模型预测基线GA病变大小和年增长率,使用经过处理的OCT数据.
主要成果:
- 所有评估的方法在预测GA增长率 (r2 ≈ 0.33 0.35) 中表现相似.
- 基线GA损伤大小预测也相当 (r2 ≈ 0.90.91),而SLIVER-net的表现略低 (r2 = 0.83).
- 圆形区域 (EZ) 和视网膜色素上皮层 (RPE) 层的厚度图是最有信息的;将它们结合起来改善了预测,而添加其他层却没有.
结论:
- 当前的处理方法在预测GA增长率方面实现了可比的,可能停滞不前的性能.
- EZ和RPE视网膜层含有预测GA进展的最重要的信息.
- 3D OCT成像对于预测GA疾病进展具有相当大的实用性.
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