Transformer-Based Multilabel Deep Learning Model Is Efficient for Detecting Ankle Lateral and Medial Ligament Injuries on Magnetic Resonance Imaging and Improving Clinicians' Diagnostic Accuracy for Rotational Chronic Ankle Instability.

Abstract

To develop a deep learning (DL) model that can simultaneously detect lateral and medial collateral ligament injuries of the ankle, aiding in the diagnosis of chronic ankle instability (CAI), and assess its impact on clinicians' diagnostic performance. DL models were developed and externally validated on retrospectively collected ankle magnetic resonance imaging (MRI) between April 2016 and March 2022 respectively at 3 centers. Included patients had confirmed diagnoses of CAI through arthroscopy, as well as individuals who had undergone MRI and physical examinations that ruled out ligament injuries. DL models were constructed based on a multilabel paradigm. A transformer-based multilabel DL model (AnkleNet) was developed and compared with 4 convolution neural network (CNN) models. Subsequently, a reader study was conducted to evaluate the impact of model assistance on clinicians when diagnosing challenging cases: identifying rotational CAI (RCAI). Diagnostic performance was assessed using area under the receiver operating characteristic curve (AUC). Our transformer-based model achieved AUCs of 0.910 and 0.892 for detecting lateral and medial collateral ligament injury, respectively, both of which were significantly higher than those of CNN-based models (all P < .001). In terms of further CAI diagnosis, there was a macro-average AUC of 0.870 and a balanced accuracy of 0.805. The reader study indicated that incorporation with our model significantly enhanced the diagnostic accuracy of clinicians (P = .042), particularly junior clinicians, and led to a reduction in diagnostic variability. The code of the model can be accessed at https://github.com/ChiariRay/AnkleNet. Our transformer-based model was able to detect lateral and medial collateral ligament injuries based on MRI and outperformed CNN-based models, demonstrating a promising performance in diagnosing CAI, especially patients with RCAI. Developing such an algorithm can improve the diagnostic performance of clinicians, aiding in identifying patients who would benefit from arthroscopy, such as patients with RCAI.