用人工智能对白内障进行分类的进展,使用前段光学连贯断层扫描图像与自我监督的视力变换器视力变换器
Shumpei Takinami1,2, Yuta Ueno1, Haruhiro Mori3
1Faculty of Medicine, Department of Ophthalmology, University of Tsukuba, 2-1-1 Amakubo, Tsukuba, Ibaraki, 305-0005, Japan.
Japanese journal of ophthalmology
|January 27, 2026
概括
自主监督的视觉变压器 (ViT) 通过前段光学连贯断层扫描 (AS-OCT) 图像有效地分类白内障严重程度. 这种人工智能方法显示了改善眼科诊断的前景.
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:
- 白内障分类对于治疗计划至关重要.
- 前段光学连贯断层扫描 (AS-OCT) 提供了眼睛前段的详细成像.
- 开发准确的AI模型用于白内障分级可以提高诊断效率.
研究的目的:
- 评价一个自我监督的学习视觉变换器 (ViT) 用于分类白内障严重程度.
- 用AS-OCT图像评估模型在核,皮层和后部亚囊白内障方面的性能.
主要方法:
- 训练并比较了五种人工智能模型,包括ResNet18,ViT和自主监督ViT (SS-ViT).
- 使用了1693张AS-OCT图像,其中1023张使用镜头不透明度分类系统III进行分类.
- 对不同白内障类型的多个分类任务 (2,3 和 6 类) 进行评估模型,用精度回忆曲线下的区域 (AUPRC) 测量性能.
主要成果:
- 自主监督ViT (SS-ViT) 在3类核白内障 (0.939),6类核白内障 (0.788),2类皮质白内障 (0.774) 和2类后部亚囊白内障 (0.506) 分类中获得了最高的AUPRC.
- 对于2级核白内障分类,ResNet18,ViT和SS-ViT都实现了近乎完美的AUPRC 0.999.
结论:
- 一个自主监督的ViT模型显示出高的有效性从AS-OCT图像对白内障严重程度的分类.
- 这种人工智能方法显示出客观和高效的白内障分级的巨大潜力.
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