医学
关节积液
渗出
滑膜炎
骨关节炎
膝关节
算法
临床实习
放射科
人工智能
机器学习
物理疗法
关节炎
磁共振成像
病理
外科
内科学
计算机科学
替代医学
作者
Banafshe Felfeliyan,Stephanie Wichuk,Abhilash Rakkunedeth Hareendranathan,R. Lambert,Walter P. Maksymowych,Jacob L. Jaremko
标识
DOI:10.1016/j.semarthrit.2024.152420
摘要
To begin evaluating deep learning (DL)-automated quantification of knee joint effusion-synovitis via the OMERACT filter. A DL algorithm previously trained on Osteoarthritis Initiative (OAI) knee MRI automatically quantified effusion volume in MRI of 53 OAI subjects, which were also scored semi-quantitatively via KIMRISS and MOAKS by 2-6 readers. DL-measured knee effusion correlated significantly with experts’ assessments (Kendall's tau 0.34-0.43) The close correlation of automated DL knee joint effusion quantification to KIMRISS manual semi-quantitative scoring demonstrated its criterion validity. Further assessments of discrimination and truth vs. clinical outcomes are still needed to fully satisfy OMERACT filter requirements.
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