Department of Minimally Invasive Surgery and Sports Medicine, The 2Nd Affiliated Hospital of Harbin Medical University, Harbin, 150001, China.
Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China.
BMC Med Imaging. 2023 Sep 11;23(1):120. doi: 10.1186/s12880-023-01091-6.
To develop a fully automated CNN detection system based on magnetic resonance imaging (MRI) for ACL injury, and to explore the feasibility of CNN for ACL injury detection on MRI images.
Including 313 patients aged 16 - 65 years old, the raw data are 368 pieces with injured ACL and 100 pieces with intact ACL. By adding flipping, rotation, scaling and other methods to expand the data, the final data set is 630 pieces including 355 pieces of injured ACL and 275 pieces of intact ACL. Using the proposed CNN model with two attention mechanism modules, data sets are trained and tested with fivefold cross-validation.
The performance is evaluated using accuracy, precision, sensitivity, specificity and F1 score of our proposed CNN model, with results of 0.8063, 0.7741, 0.9268, 0.6509 and 0.8436. The average accuracy in the fivefold cross-validation is 0.8064. For our model, the average area under curves (AUC) for detecting injured ACL has results of 0.8886.
We propose an effective and automatic CNN model to detect ACL injury from MRI of human knees. This model can effectively help clinicians diagnose ACL injury, improving diagnostic efficiency and reducing misdiagnosis and missed diagnosis.
开发一种基于磁共振成像（MRI）的完全自动化卷积神经网络（CNN）检测系统，用于 ACL 损伤，并探讨 CNN 在 MRI 图像上检测 ACL 损伤的可行性。
纳入 313 例 16-65 岁患者，原始数据为 368 份 ACL 损伤和 100 份 ACL 完整的 MRI。通过添加翻转、旋转、缩放等方法对数据进行扩充，最终数据集为 630 份，包括 355 份 ACL 损伤和 275 份 ACL 完整的 MRI。使用具有两个注意力机制模块的建议 CNN 模型，对数据集进行五重交叉验证训练和测试。
使用我们提出的 CNN 模型的准确性、精确性、敏感性、特异性和 F1 评分来评估性能，结果分别为 0.8063、0.7741、0.9268、0.6509 和 0.8436。五重交叉验证的平均准确率为 0.8064。对于我们的模型，检测 ACL 损伤的平均曲线下面积（AUC）为 0.8886。
我们提出了一种有效的自动 CNN 模型，用于从人类膝关节的 MRI 中检测 ACL 损伤。该模型可以有效帮助临床医生诊断 ACL 损伤，提高诊断效率，减少误诊和漏诊。
