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没有细分的OCT基于体积的深度学习模型改善了点向视觉场灵敏度估计

Zhiqi Chen¹, Eitan Shemuelian², Gadi Wollstein^2,3,4

¹Department of Electrical and Computer Engineering, NYU Tandon School of Engineering, Brooklyn, NY, USA.

Translational vision science & technology

|June 29, 2023

概括

使用3D光学连贯断层扫描 (OCT) 卷的新型深度学习模型准确地估计视野 (VF) 功能,优于2D模型并减少患者的测试时间.

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:

光学连贯断层扫描 (OCT) 中的结构变化与视野 (VF) 功能变化相关.
传统的海外国家和地区分析依赖于取决于细分的厚度图,这些厚度图可以通过地板效应来限制.

研究的目的:

开发一个深度学习 (DL) 模型,从3D OCT卷中直接估计VF功能性能.
在评估结构功能关系时克服了依赖于细分的OCT测量和地板效应的局限性.

主要方法:

通过使用3D OCT卷来训练DL模型来预测VF功能性能.
这种3D模型与在依赖细分的OCT厚度图上训练的2D模型进行了比较.
一个梯度损失函数被纳入,以利用VF空间信息.

主要成果:

与2D模型相比,3D DL模型表现出更高的性能,平均绝对误差 (MAE) 显著降低,Pearson相关系数更高.
3D模型对测试数据的地板效应的影响较小,表现优于先前的研究.
梯度损失提高了对低灵敏度视野值的估计准确度.

结论:

开发的3D DL模型为视野中的结构功能关系提供了更准确的定量评估.
这种方法促进了视觉现场测试替代品的衍生,可能减少测试时间并改善临床判断.

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