JMIR Publications

ABSTRACT

Background:

Anterior cruciate ligament (ACL) injuries are common in sports and are critical knee injuries that require prompt diagnosis. Magnetic resonance imaging (MRI) is a strong, noninvasive tool for detecting ACL tears, which requires training to read accurately. Clinicians with different experiences in reading MRIs require different information for the diagnosis of ACL tears. Artificial intelligence (AI) image processing could be a promising approach in the diagnosis of ACL tears.

Objective:

This study sought to use artificial intelligence to (1) diagnose ACL tears from complete MRI images, (2) identify torn ACL images from complete MRI images with a diagnosis of ACL tears, and (3) differentiate intact ACL and torn ACL MRI images from selected MRI images.

Methods:

Sagittal MRI images of torn ACL (n = 1205) and intact ACL (n = 1018) from 800 cases and complete knee MRI images of 200 cases (100 torn ACL and 100 intact ACL cases) between 20 and 40 years of age were retrospectively collected. An AI approach using a convolutional neural network was applied to build models for the objective. MRI images of 200 independent cases (100 torn ACL and 100 intact ACL) were used as the test set for the models. MRI 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 model differentiated between torn ACL, intact ACL, and other images from complete MRI 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 96.0%. The model showed a significantly higher reading accuracy than less experienced clinicians. The model identified torn ACL images from complete MRI 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 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 demonstrated the feasibility of using an AI approach to provide information to clinicians who need different information from MRI to diagnose ACL tears.