Automated detection of anterior cruciate ligament tears using a deep convolutional neural network

Background The development of computer-assisted technologies to diagnose anterior cruciate ligament (ACL) injury by analyzing knee magnetic resonance images (MRI) would be beneficial, and convolutional neural network (CNN)-based deep learning approaches may offer a solution. This study aimed to evaluate the accuracy of a CNN system in diagnosing ACL ruptures by a single slice from a knee MRI and to compare the results with that of experienced human readers. Methods One hundred sagittal MR images from patients with and without ACL injuries, confirmed by arthroscopy, were cropped and used for the CNN training. The final decision by the CNN for intact or torn ACL was based on the probability of ACL tear on a single MRI slice. Twelve board-certified physicians reviewed the same images used by CNN. Results The sensitivity, specificity, accuracy, positive predictive value and negative predictive value of the CNN classification was 91.0%, 86.0%, 88.5%, 87.0%, and 91.0%, respectively. The overall values of the physicians' readings were similar, but the specificity was lower than the CNN classification for some of the physicians, thus resulting in lower accuracy for the human readers. Conclusions The trained CNN automatically detected the ACL tears with acceptable accuracy comparable to that of human readers.

Automated summary

This study evaluated the accuracy of a convolutional neural network (CNN) system in diagnosing anterior cruciate ligament (ACL) ruptures through analyzing knee magnetic resonance images (MRI). The results showed that the trained CNN had a comparable accuracy to that of experienced human readers.