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通过面部照片进行深度学习驱动的外视计在甲状腺眼病中的眼睛疾病

Joonhyeon Park¹, Jin Sook Yoon², Namju Kim³

¹Division of Research & Development, THYROSCOPE Inc., Ulsan, Republic of Korea.

Ophthalmology science

|June 11, 2025

概括

一个新的深度学习系统使用甲状腺眼病 (TED) 患者的面部照片准确地测量了proptosis. 这种易于使用的工具提供了一个可靠的替代传统外眼镜测量.

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:

甲状腺眼病 (TED) 常常导致亡,需要精确的测量.
传统的视外测量方法可能具有挑战性,需要专门的设备.

研究的目的:

开发和评估一种深度学习 (DL) 辅助的系统,用于使用TED患者的面部照片进行质测量.
与标准测量相比,评估这个新系统的准确性和可靠性.

主要方法:

一项回顾性队列研究,涉及来自Severance医院 (SH) 的1108名TED患者和来自首尔国立大学Bundang医院 (SNUBH) 的171名TED患者.
一个双流的ResNet-18神经网络,在面部图像和深度图上进行训练,并根据Hertel外眼测量测量进行验证.
使用平均绝对误差 (MAE),皮尔森相关性,类内相关系数 (ICC) 和接收器操作特征 (ROC) 曲线分析来评估准确性.

主要成果:

该DL系统实现了低MAE (1.27毫米为SH,1.24毫米为SNUBH) 和强烈的相关性 (皮尔森:0.82SH,0.77SNUBH;ICC:0.80SH,0.73SNUBH).
经过ROC分析,在检测质中显示出高精度 (AUC:0.91 SH,0.88 SNUBH).
该系统准确地检测到显著的proptosis变化 (≥2毫米),准确度为74.6%.

结论:

该DL辅助系统提供了一种准确且易于使用的方法,用于TED患者使用面部照片进行外眼测量.
这项技术为传统方法提供了一个有前途的替代方案,有可能在各种临床环境中改善质测量可访问性.

关键词:

深度学习是一种深度学习.双流式ResNet-18 双流式ResNet-18 双流式ResNet-18 双流式ResNet-18 双流式视外镜测量 (exophthalmometry) 是一种视外镜测量技术.产物灭 (Proptosis) 是一种导致产物灭的过程.甲状腺眼睛疾病甲状腺眼病

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