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使用光学连贯断层扫描预测新血管与年龄相关的黄斑变性患者的视力敏度

Sehwan Moon^1,2, Sungyong Park², Chul Gu Kim³

¹Electronics and Telecommunications Research Institute, Gwangju, Republic of Korea.

Scientific reports

|December 2, 2025

概括

一个人工智能 (AI) 模型从新血管与年龄相关的黄斑变性 (AMD) 患者的光学连贯性断层扫描 (OCT) 图像中中度预测了视力敏度 (VA). 高可信度预测显示出很高的准确性,这表明在区分好视力和差视力的潜在临床价值.

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:

神经血管与年龄相关的黄斑变性 (AMD) 是视力丧失的主要原因.
准确预测视力敏度 (VA) 对于管理AMD治疗至关重要.
光学连贯断层扫描 (OCT) 提供了详细的视网膜成像,但其对VA的预测潜力需要进一步探索.

研究的目的:

评估人工智能 (AI) 模型的可行性,使用OCT图像来预测在以前未接受治疗的新血管AMD患者中VA.
为了评估修改后的VGG16卷积神经网络 (CNN) 对于VA分类的性能.
确定AI驱动的VA预测在AMD管理中的临床实用性.

主要方法:

对240名接受抗VEGF治疗的新血管AMD患者的2400张OCT图像进行了回顾性分析.
数据预处理包括将图像剪切和调整到224x224像素的尺寸.
预训练的VGG16 CNN被调整为五类VA分类 (0.1至≥0.8) 并使用五倍交叉验证进行评估.

主要成果:

人工智能模型的整体性能中等,宏观平均AUC为0.772,准确率为50.3%.
高可信度预测 (29.2%的病例) 显示了显著提高的准确性 (74.1%) 和二进制准确性 (94.2%).
该模型显示,预测视力非常差 (VA <0.1,AUC 0.83) 的歧视最强,大多数错误分类发生在相邻的VA类别之间.

结论:

一个VGG16 CNN模型显示,从新血管AMD中的OCT图像预测VA的可行性中等.
尽管整体分类存在局限性,但高二进制精度表明,在区分良好的视觉结果和差的视觉结果方面,具有潜在的临床价值.
基于AI的OCT分析可能有助于监测AMD患者的治疗反应和指导AMD患者的临床决策.

关键词:

与年龄相关的黄斑变性.人工智能的人工智能是人工智能.光学连贯性断层扫描技术.视力敏度视力敏度视力敏度

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