The Use of AI For Hallux Valgus Assessment via Mobile Phone-Based 3D Camera Scan

Abstract

Hallux valgus (HV) is a common foot deformity. Traditional detection methods include clinical examination and radiographic imaging, which, although reliable, often remain inaccessible to many due to existing care barriers. This study introduces an innovative approach to computer vision analysis and phone camera-based 3D scanning technology to detect and assess HV severity. We evaluated the accuracy of this method against routine clinical examination as the currently accepted assessment standard. Our study included 120 participants, resulting in 240 foot scans, with a diverse demographic representation. The computer vision algorithm utilized a surrogate angle, automatically derived from the 3D scans, to identify the severity of HV, and its correlation with traditional radiographic measurements for HV. Our findings reveal that computer vision-based detections offer high accuracy, with an Area Under the Curve (AUC) score of 0.947, presenting a promising alternative to conventional methods. This technology offers promise for increasing access to HV detection, potentially aiding in earlier diagnosis as well as non-operative treatment options that may ultimately reduce the need for surgical intervention. Its ease of use and application in telemedicine contexts has the potential, moreover, to significantly benefit patients in remote or underserved areas and expand capacity to promote similar care improvement in other areas of musculoskeletal disease.