Purpose: To calculate the diagnostic accuracy from the confusion matrix using deep learning (DL) on ultrasound (US) images of Palmer 1B triangular fibrocartilage complex (TFCC) injury.
Methods: Twenty-nine wrists of 15 healthy volunteers (11 men; mean age, 34.9 years ± 9.7) (control group) and 20 wrists of 17 patients (11 men; mean age 41.0 years ± 12.2) with TFCC injury (Palmer type IB) (injury group) were included in the study. The diagnosis of Palmer 1B TFCC injury was made using magnetic resonance imaging, computed tomography arthrography, and intraoperative arthroscopic findings. In total, 2,000 images were provided to each group, 80% of which were randomly selected by AI and used as training data; the remaining data were used as test data. Transfer learning was conducted using a pretrained 3 separate models (GoogLeNet, ResNet50, ResNet101). Model evaluation was performed using a confusion matrix. The area under a receiver operating characteristic curve was also calculated. The occlusion sensitivity was used to visualize the important features.
Results: For the prediction of TFCC injury by the DL model, the best score of accuracy was 0.85 in GoogLeNet, a recall was 1.0 in ResNet50 and ResNet101, and a specificity was 0.78 in GoogLeNet. In predicting the TFCC injury for the test data, the best score of the AUC was 0.97 on ResNet101. Visualization of important features showed that AI predicted the presence of injury by focusing on the morphology of the articular disc.
Conclusions: US images using the DL model predicted Palmer 1B TFCC injury with high accuracy, with the best scores of 0.85 for accuracy on GoogLeNet, 1.00 for sensitivity on ResNet50 and ResNet101, and 0.78 for specificity on GoogLeNet. The use of DL for US imaging of Palmer 1B TFCC injury predicted the injury as well as magnetic resonance imaging and computed tomography arthrography LEVEL OF EVIDENCE: IV; retrospective case series study.
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