Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.
Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
PLoS One. 2019 Feb 28;14(2):e0212741. doi: 10.1371/journal.pone.0212741. eCollection 2019.
The lifetime prevalence of shoulder pain is nearly 70% and is mostly attributable to subacromial disorders. A rotator cuff tear is the most severe form of subacromial disorders, and most occur in the supraspinatus. For clinical examination, shoulder ultrasound is recommended to detect supraspinatus tears. In this study, a computer-aided tear classification (CTC) system was developed to identify supraspinatus tears in ultrasound examinations and reduce inter-operator variability. The observed cases included 89 ultrasound images of supraspinatus tendinopathy and 102 of supraspinatus tear from 136 patients. For each case, intensity and texture features were extracted from the entire lesion and combined in a binary logistic regression classifier for lesion classification. The proposed CTC system achieved an accuracy rate of 92% (176/191) and an area under receiver operating characteristic curve (Az) of 0.9694. Based on its diagnostic performance, the CTC system has promise for clinical use.
肩痛的终身患病率接近 70%，主要归因于肩峰下疾病。肩袖撕裂是肩峰下疾病最严重的形式，大多数发生在冈上肌。对于临床检查，推荐使用肩部超声来检测冈上肌撕裂。在这项研究中，开发了一种计算机辅助撕裂分类（CTC）系统，用于识别超声检查中的冈上肌撕裂并减少操作者间的变异性。观察病例包括 136 名患者的 89 张冈上肌肌腱病和 102 张冈上肌撕裂的超声图像。对于每个病例，从整个病变中提取强度和纹理特征，并组合在二进制逻辑回归分类器中进行病变分类。所提出的 CTC 系统的准确率为 92%（176/191），受试者工作特征曲线下面积（Az）为 0.9694。基于其诊断性能，CTC 系统有望在临床上使用。
