卷积神经网络在棒球肘计算机辅助诊断中的应用

Kenta Sasaki, Daisuke Fujita, Kenta Takatsuji, Yoshihiro Kotoura, Tsuyoshi Sukenari, M. Minami, Yusuke Kobayashi, Yoshikazu Kida, Kenji Takahashi, Syoji Kobashi
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引用次数: 0

摘要

棒球肘是由于在棒球运动中反复投球而引起的肘部运动障碍。剥离性骨软骨炎(OCD)是棒球肘最常见的一种形式,是肘关节肱骨小头的一种疾病,早期发现OCD很重要。本研究旨在建立一个从肘部超声图像检测强迫症的模型。该模型基于VGG16。通过67名强迫症被试和91名正常被试对该方法进行了验证。结果表明,该模型的准确率为88.5%,精密度为87.9%,召回率为97.0%,F1得分为0.910,AUC为0.971。
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Detection of osteochondritis dissecans using convolutional neural networks for computer-aided diagnosis of baseball elbow
Baseball elbow is a kinetic disorder of the elbow caused by repetitive pitching in baseball. Osteochondritis dissecans (OCD), one of the most common forms of baseball elbow, is a disorder of the humeral capitellum of the elbow, and early detection of OCD is important. This study aims to create a model to detect OCD from ultrasound images of the elbow. The model is based on VGG16. The proposed method was validated by using 67 OCD subjects and 91 normal subjects. The results showed that the model achieved an accuracy of 88.5%, a precision of 87.9%, a recall of 97.0%, an F1 score of 0.910, and an AUC of 0.971.
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