%0杂志文章%I JMIR出版物%V 1% 卡塔尔世界杯8强波胆分析N 1% P e37508% T磁共振图像人工智能辅助诊断前交叉韧带撕裂:算法开发与验证研究%陈阿，杨坤辉，王志宇，马新义，李小利，李a,Oscar光生%+国立杨明交通大学临床医学研究所，台北市利农街二段155号，邮编:11221,886 2 28757391，oscarlee9203@gmail.com %K人工智能%K卷积神经网络%K磁共振成像%K MRI %K深度学习%K前交叉韧带%K运动医学%K机器学习%K韧带%K运动%K诊断%K撕裂%K损伤%K成像%K发育%K验证%K算法%D 2022 %7 26.7.2022 %9原创论文%J J AI %G英文%X背景:前交叉韧带(ACL)损伤在运动中很常见，是严重的膝关节损伤，需要及时诊断。磁共振成像(MRI)是检测前交叉韧带撕裂的一种强大的、非侵入性的工具，需要经过训练才能准确读取。对于前交叉韧带撕裂的诊断，具有不同阅读MR图像经验的临床医生需要不同的信息。人工智能图像处理在前交叉韧带撕裂的诊断中具有广阔的应用前景。目的:本研究寻求使用AI(1)从完整的MR图像中诊断ACL撕裂，(2)从完整的MR图像中识别ACL撕裂诊断，(3)从所选的MR图像中区分完整ACL和撕裂ACL的MR图像。方法:回顾性收集800例ACL撕裂(n=1205)和ACL完整(n=1018)患者的矢状面MR图像，以及200例20-40岁患者的全膝关节MR图像(100例ACL撕裂和100例ACL完整)。一种使用卷积神经网络的人工智能方法被用于为目标建立模型。 The MR images of 200 independent cases (100 torn ACL and 100 intact ACL) were used as the test set for the models. The MR images of 40 randomly selected cases from the test set were used to compare the reading accuracy of ACL tears between the trained model and clinicians with different levels of experience. Results: The first model differentiated between torn-ACL, intact-ACL, and other images from complete MR images with an accuracy of 0.9946, and the sensitivity, specificity, precision, and F1-score were 0.9344, 0.9743, 0.8659, and 0.8980, respectively. The final accuracy for ACL-tear diagnosis was 0.96. The model showed a significantly higher reading accuracy than less experienced clinicians. The second model identified torn-ACL images from complete MR images with a diagnosis of ACL tear with an accuracy of 0.9943, and the sensitivity, specificity, precision, and F1-score were 0.9154, 0.9660, 0.8167, and 0.8632, respectively. The third model differentiated torn- and intact-ACL images with an accuracy of 0.9691, and the sensitivity, specificity, precision, and F1-score were 0.9827, 0.9519, 0.9632, and 0.9728, respectively. Conclusions: This study demonstrates the feasibility of using an AI approach to provide information to clinicians who need different information from MRI to diagnose ACL tears. %R 10.2196/37508 %U https://ai.www.mybigtv.com/2022/1/e37508 %U https://doi.org/10.2196/37508