DINO-EYE:自主监督学习,用于识别初级开放角光眼中不同光盘表型的特征
Lourdes Grassi1,2, Zhe Fei3, Esteban Morales1
1Glaucoma Division, Ophthalmology, Jules Stein Eye Institute, University of California Los Angeles (UCLA), Los Angeles, CA, 90095, USA.
Scientific reports
|January 10, 2026
概括
一个新的自主监督学习 (SSL) 模型,DINO-EYE,使用 fundus 图像准确地分类初级开放角光眼 (POAG) 的光盘表型. 它还揭示了模式,有助于青光眼的诊断和个性化的患者护理.
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:
- 初级开放角眼 (POAG) 诊断依赖于从光盘照片 (ODP) 中识别光盘表型.
- 准确地分类这些表型对于有效的绿眼病管理和治疗规划至关重要.
- 现有的方法可能缺乏全面分析所需的精度或解释性.
研究的目的:
- 开发一种自主监督学习 (SSL) 模型,用于在POAG中对光盘表型进行分类.
- 使用光盘照片 (ODP) 探索新的表型模式.
- 通过先进的人工智能分析来增强青光眼的诊断和患者护理.
主要方法:
- 收集了POAG患者的850张ODP图像,增加到10,493张图像.
- 使用DINO视觉变压器用于SSL提取2048维特征.
- 采用随机森林和XGBoost进行分类,UMAP用于可视化,注意力地图用于解释性.
主要成果:
- DINO-EYE模型在表型分类中实现了91%的准确性 (92.1%在合并类似的表型后).
- 无监督的聚类确定了不同的分组,特别是集中性稀释和广泛的周周膜缩 (PPA).
- 与RETFound SSL模型相比,该模型表现出卓越的性能和可解释性.
结论:
- DINO-EYE有效地从 fundus 图像中提取临床上有意义的特征,用于准确的 POAG 光盘表型分类.
- 该模型在性能和可解释性方面超越了现有的SSL方法,为临床决策支持提供了潜力.
- 这种人工智能方法有望推进个性化的青光眼护理规划和患者管理.
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