AI-Based Foot X-Ray Reading in Real-World: Evaluating the Accuracy of Assistive Decisions for Diagnosing Foot & Ankle Disorders

Abstract

Category: Midfoot/Forefoot; Hindfoot Introduction/Purpose: Evaluation of plain radiographs is of fundamental importance in the diagnosis and treatment planning of foot and ankle disorders. These assessments are essential, but can be difficult for the following reasons: First, many angles must be measured using various reference lines. Second, there may be inter-observer differences in the measurements. Consistent and standardized measurement methods are required to increase the efficiency and accuracy of radiographic readings. Several recent studies have reported good results using deep learning (DL) models for medical image analysis. It is expected that efficiency and accuracy can be increased through radiographic readings using DL. This study aims to develop a DL model that recognizes the bones of the foot and ankle in plain radiographs and automatically measures angles frequently used for diagnosis. Methods: The DL model was developed using two architectures: segmenting objects by locations (SOLO) for bone segmentation and Global Universal U-Net (GU2Net) for reference line detection. The angles measured were hallux valgus angle (HVA) and 1st- 2nd intermetatarsal angle (IMA) on anterior-posterior (AP) X-ray; calcaneal pitch angle (CPA) and lateral Meary's angle (LMA) on lateral X-ray. Only data obtained from external test set were used for statistical analysis to evaluate the model's performance. The test set used a total of 558 images collected from January 2022 to September 2022. Angles measured by the DL model were compared with angles measured manually by human researchers. The intraclass correlation coefficient (ICC) was used to evaluate the accuracy of angle measurements. The error between the angle measured by the DL model and the angle measured by human researchers was evaluated using root mean square error (RMSE) and mean absolute difference (MAE). Results: As shown in the attached table, the ICCs of HVA were 0.957 (right) and 0.933 (left). The ICCs of IMA were 0.881 (right) and 0.902 (left). The ICCs of LMA were 0.895 (right) and 0.937 (left). The ICCs of CPA were 0.918 (right) and 0.868 (left). All ICCs were greater than 0.8, indicating excellent correlations between angles measured with the DL model and angles measured manually (ground truth). For RMSE and MAE, the left IMA was the smallest (1.50 and 1.16, respectively), and the right LMA was the largest (4.50 and 2.47, respectively). Conclusion: The DL model in our study was able to perform various angle measurements in both AP and lateral X-rays with considerable accuracy. Through additional model training and development, it is expected that it will be useful for analyzing various foot and ankle disorders such as hallux valgus, flatfoot, pes cavus and other alignment deformities.