多模态深度学习分类器用于初级开放角度眼光大眼的诊断,使用广场光神经头部立方体扫描在具有和没有高近视的眼睛中
Christopher Bowd1, Akram Belghith1, Jasmin Rezapour1,2
1Viterbi Family Department of Ophthalmology, Shiley Eye Institute, Hamilton Glaucoma Center.
Journal of glaucoma
|July 31, 2023
概括
一个新的深度学习模型结合了光学连贯断层扫描 (OCT) 纹理和视网膜神经纤维层 (RNFL) 厚度,提高了青光眼诊断的准确性,特别是在高近视病例中.
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:
- 青光眼的诊断可能是具有挑战性的,特别是在高近视的眼睛.
- 准确检测青光眼对于预防视力丧失至关重要.
研究的目的:
- 评估一种多式深度学习 (DL) 分类器,用于诊断初级开角玻璃眼 (POAG).
- 用OCT视觉神经头部立方体扫描来评估分类器在高近视和没有高近视的眼睛中的准确性.
主要方法:
- 开发了一种多式DL分类器,集成VGG-16模型用于基于纹理的面部图像,RNFL厚度图和共聚焦扫描激光眼镜 (cSLO) 图像.
- 该研究包括POAG和健康的眼睛,有或没有轴高近视 (轴长>26毫米).
- 模型性能使用年龄,轴长和磁盘面积调整到接收器操作曲线 (AUC) 下的面积进行了比较.
主要成果:
- 多式联网DL模型实现了0.91的AUC,明显优于单个模型 (纹理:0.83,RNFL:0.84,cSLO:0.68).
- 在高度近视的眼睛中,多模式DL模型的精度 (0.89) 优于单模式模型 (纹理:0.83,RNFL:0.85,cSLO:0.69).
结论:
- 综合了基于OCT的RNFL厚度和纹理信息的多式DL模型,证明了对玻璃眼的卓越诊断能力.
- 这种方法在区分健康眼睛和POAG眼睛方面特别有前途,特别是在高轴近视的情况下.
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