An interpretable deep learning model for hallux valgus prediction

This work developed an interpretable deep learning model to automatically annotate landmarks and calculate the hallux valgus angle (HVA) and the intermetatarsal angle (IMA), reducing the time and error of manual calculations by medical experts and improving the efficiency and accuracy of hallux valgus (HV) diagnosis. A total of 2,000 foot X-ray images were manually labeled with 12 landmarks by two surgical specialists as training data for the deep learning model. The important parts of the foot X-ray images centered on the proximal phalanx of the bunion (PH1), the first metatarsal (MT1), and the second metatarsal (MT2) were segmented using the proposed AG-UNet in the study. The SE-DNN network model was used for automatic identification of landmarks and calculation of the HVA angle between PH1 and MT1, and the IMA angle between MT1 and MT2. Finally, the accuracy of the model was assessed using a comparison of two methods, the interpretability of deep learning and manual measurements by a foot and ankle surgeon. In the test set, the average error distance between the 12 landmarks predicted by the model and the manually annotated landmarks ranged from 1.9 mm to 5.6 mm, and the average error of all landmarks was less than 3.1 mm. In addition, for the measurement of HVA and IMA angles, the inter-rater agreement between the proposed model and the experts performed well, and the ICC results were all greater than or equal to 0.9. This work proposed an interpretable deep learning model for hallux valgus prediction, which can automatically identify 12 landmarks and calculate HVA and IMA. Compared with the subjective judgment of medical experts, the model showed significant advantages in reliability and accuracy. The method has been applied in hospitals and achieved significant detection results.