A Deep Learning-Based Approach for Early Scoliosis Detection Using mmWave Radar-Based Gait Data

Scoliosis is a sideways curvature of the spine that often develops during childhood and adolescence. When detected early, scoliosis can be managed effectively with non-invasive approaches such as bracing and physiotherapy, while late detection frequently leads to surgical intervention. Current screening methods rely on physical examination and X-ray imaging, which exposes children to ionizing radiation and may miss early-stage cases.

This observational study investigates whether millimeter-wave (mmWave) radar, combined with deep learning (a type of artificial intelligence), can detect early signs of scoliosis by analyzing how a child walks. The radar sensor records subtle movement patterns during walking without using cameras and without producing any identifiable images, fully preserving the participant's privacy. No ionizing radiation is involved.

Pediatric participants attending the orthopedic clinic for routine scoliosis evaluation are invited to walk a short distance in front of a mmWave radar sensor. The collected gait recordings are then analyzed using deep learning models, and the results are compared with the participant's standard clinical scoliosis assessment performed by a pediatric orthopedic specialist. The diagnostic performance of the deep learning model is evaluated using sensitivity, specificity, and overall accuracy.

If the approach proves accurate, it could offer a radiation-free, privacy-preserving, and low-cost alternative for early scoliosis screening in schools, primary healthcare centers, and pediatric orthopedic clinics, ultimately supporting earlier diagnosis and reducing the long-term clinical burden of untreated scoliosis.