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基于深度学习的诊断关节骨关节炎使用全身骨扫描

Yeon-Hee Lee^1,2, Hee-Sung Kim³, Seonggwang Jeon³

¹Department of Orofacial Pain and Oral Medicine, Kyung Hee University Dental Hospital, KyungHee University Medical Center, Kyung Hee University School of Dentistry, #613 Hoegi-dong, Dongdaemun-gu, Seoul 02447, Korea.

|January 7, 2026

概括

深度学习模型使用向骨扫描准确诊断关节骨关节炎 (TMJ-OA). 系统性骨关节炎的特征显示TMJ-OA的预测价值有限.

科学领域:

整形外科整形外科整形外科
放射学放射学是一门学科.
人工智能的人工智能

背景情况:

关节骨关节炎 (TMJ-OA) 导致疼痛和功能限制.
关节OA和全身性骨关节炎 (OA) 之间的联系尚不清楚.

研究的目的:

开发深度学习模型,使用骨光学对TMJ-OA进行自动诊断.
评估系统性OA特征作为TMJ-OA的预测因素.

主要方法:

对1943名患者 (3,886名TMJ) 的分析,使用基于VGG16的三种卷积神经网络 (CNN) 模型.
头部和部成像与全身扫描 (不包括头部和部) 的评估.

主要成果:

在VGG16-Lite模型中,从头部和部骨头扫描中获得TMJ-OA的高诊断准确度 (AUC>0.90).
全身扫描显示TMJ-OA的适度预测值 (AUC~0.65),表明系统影响有限.

结论:

轻量级的深度学习模型和有针对性的骨扫描提供了强大的TMJ-OA检测.
需要进一步的研究来探索TMJ-OA的系统性关联.

关键词:

生物信息学是一种生物信息学.整形外科整形外科整形外科

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