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深度学习24-2视觉场图的估计视觉神经头部光学连贯性断层扫描血管造影

Golnoush Mahmoudinezhad¹, Sasan Moghimi¹, Liyang Ru²

¹Department of Ophthalmology, Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family.

Journal of glaucoma

|September 9, 2025

概括

人工智能使用OCTA图像准确估计视野地图. 这种深度学习方法可能会减少对频繁视觉现场测试的需求.

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:

视野 (VF) 测试对于诊断和监测玻璃眼和其他视神经头部 (ONH) 疾病至关重要.
光学连贯断层扫描血管学 (OCTA) 提供了ONH的详细的微血管信息.
当前的VF测试可能耗时,可能无法有效捕捉微妙的变化.

研究的目的:

开发和评估深度学习 (DL) 模型,用于估计来自OCTA视觉神经头部 (ONH) 面部图像的24-2视野 (VF) 地图.
将DL模型的性能与传统的线性回归 (LR) 方法进行比较.

主要方法:

在994名参与者的3148个VF OCTA图像对上训练DL模型,使用辐射周毛细血管 (RPC),表面和胆道ONH血管密度 (VD) 层.
估计的24-2平均偏差 (MD),模式标准偏差 (PSD),总偏差 (TD) 和模式偏差 (PD) 值.
使用平均绝对误差 (MAE) 和皮尔森相关系数 (R) 评估模型准确性.

主要成果:

在所有测试的ONH层中,DL模型在估计VF值方面明显优于LR模型 (P <0.001).
使用RPC,DL获得了0.79的R和1.77dB的MAE,用于MD估计.
使用联合ONH层的DL模型显示MD和TD估计略有改善,而不是单个层.

结论:

对OCTA图像应用的深度学习模型在估计24-2视野地图时表现出高准确度.
利用OCTA通过DL提供的ONH微血管信息,有可能减少VF测试的频率.
这种人工智能驱动的方法可能提供一种更有效的监测视神经健康的方法.

关键词:

24-2-2是什么意思? 24-2是什么意思?人工智能的人工智能是人工智能.深度学习是一种深度学习.玻璃眼 glaucoma 玻璃眼玻璃眼玻璃眼玻璃眼光学连贯性断层扫描血管图谱视野视野视野视野视野

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