Transformer-based Multi-label Deep Learning Model is Efficient for Detecting Ankle Lateral and Medial Ligament Injuries on MRI and Improving Clinicians' Diagnostic Accuracy for Rotational Chronic Ankle Instability.

ABSTRACT

PURPOSE: 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. METHODS: DL models were developed and external validated on retrospectively collected ankle MRIs between April 2016 and March 2022 respectively at three centers. Included patients were 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 multi-label paradigm. A transformer-based multi-label DL model (AnkleNet) was developed and compared with four 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). RESULTS: Our transformer-based model achieved AUC of 0.910 and 0.892 for detecting lateral and medial collateral ligament injury, respectively, both of which was significantly higher than that of CNN-based models (all P < 0.001). In terms of further CAI diagnosis, it exhibited 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 = 0.042), 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. CONCLUSION: 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 RCAI patients.