纵向视野的深度学习预测青光眼进展速率并识别快速进展者

中文摘要 AI 辅助

青光眼是不可逆性失明的主要原因，及时识别快速进展者对于预防残疾至关重要。当前实践通过均值偏差（MD）对时间的普通最小二乘回归来估计进展，需要6-10次视野（VF）测试并历时数年才能获得可靠的斜率。我们提出了GLAM（青光眼纵向分析模型），一种深度学习框架，该框架摄入纵向Humphrey 24-2总偏差序列及五个临床特征，利用基于注意力的融合和任意不确定性预测MD和视野指数进展速率。在开放的华盛顿大学Humphrey视野数据集（4,276只患者眼）上，GLAM实现了MD速率平均绝对误差为0.139 dB/年（R²=0.927；相比岭基线降低了73.5%），快速进展者检测的AUC为0.990。仅使用视野的深度学习可以匹配多模态流程，仅利用常规收集的视野检查即可进行进展预后预测。

英文摘要

Glaucoma is the leading cause of irreversible blindness, and timely identification of fast progressors is essential to prevent disability. Current practice estimates progression by ordinary least-squares regression of mean deviation (MD) on time, requiring 6--10 visual field (VF) tests over several years to obtain a reliable slope. We present GLAM (Glaucoma Longitudinal Analysis Model), a deep learning framework that ingests longitudinal Humphrey 24-2 total deviation sequences with five clinical features and predicts MD and visual field index progression rates using attention-based fusion and aleatoric uncertainty. On the open-access University of Washington Humphrey Visual Field dataset (4,276 patient-eyes), GLAM achieved an MD-rate mean absolute error of 0.139 dB yr$^{-1}$ ($R^2 = 0.927$; 73.5% reduction over a ridge baseline) and an AUC of 0.990 for fast-progressor detection. VF-only deep learning can match multimodal pipelines for progression prognostication using routinely collected perimetry alone.

发表机构

• AI-MIQA
• Vision Eye Institute and Hospital（视觉眼科研究所与医院）
• China West Normal University（西华师范大学）

机构由 AI 辅助整理，请以论文原文为准。