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通过使用深度学习的基线和纵向结构测量预测视觉场进展

Vahid Mohammadzadeh¹, Sean Wu², Sajad Besharati¹

¹From the Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles (V.M., S.B., E.M., K.E., M.R., A.M., D.Z., J.C., K.N.-M.), Los Angeles, California, USA.

American journal of ophthalmology

|February 14, 2024

概括

深度学习使用视网膜神经纤维层厚度和光盘照片准确预测青光眼的进展. 这种工具可以帮助识别高风险的患者,及时进行干预.

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:

鉴定患有高进展风险的青光眼患者对于临床管理至关重要.
目前的方法往往缺乏早期干预所需的预测能力.
广泛可用的结构数据可能会被用于风险分层.

研究的目的:

开发和验证一种深度学习 (DL) 算法,用于预测青光眼患者视野 (VF) 进展.
测试基线或串行结构测量可以预测VF进展的假设.

主要方法:

一个带有ResNet-152骨干的语神经网络被设计用于预测VF的进展.
该模型使用了3079只眼睛的连续光盘照片 (ODP) 和基线视网膜神经纤维层 (RNFL) 厚度.
使用ROC曲线下的面积 (AUC) 评估模型性能,并使用外部数据集进行测试.

主要成果:

DL模型实现了0.813的AUC,其中包括基线ODP和RNFL厚度.
添加串行ODP提高了预测准确度,AUC达到0.894.
该模型在预测快速眼病进展方面表现出高准确度 (AUC=0.911),并且在外部数据集 (AUC=0.893) 上保持了性能.

结论:

深度学习模型可以使用易于获得的结构数据准确预测绿眼病的视野进展.
开发的DL算法,利用RNFL厚度和串行ODPs,显示出作为临床风险分层工具的潜力.
建议对临床实施进行进一步的验证.

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