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Deep Learning for Early Scoliosis Detection Using mmWave Radar Gait Data

NCT07593560 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 200

Last updated 2026-05-18

No results posted yet for this study

Summary

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.

Conditions

  • Scoliosis Idiopathic Adolescent
  • Scoliosis

Interventions

DIAGNOSTIC_TEST

mmWave Radar Gait Assessment

Each participant performs a standardized walking task along a defined path in front of a millimeter-wave (mmWave) radar sensor. The radar continuously records the participant's gait micro-Doppler signatures during the walk. The mmWave radar device is contactless, non-ionizing, and does not capture identifiable visual images, fully preserving participant privacy. The recorded gait signals are subsequently processed and analyzed using deep learning models (including convolutional and transformer-based architectures) trained to classify scoliosis status. The full radar-based assessment takes approximately 5 to 10 minutes per participant. The standard clinical and radiographic scoliosis evaluation performed as part of routine care serves as the reference standard.

Sponsors & Collaborators

  • Gebze Technical University

    lead OTHER

Eligibility

Min Age
2 Years
Max Age
75 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2026-06-30
Primary Completion
2027-12-31
Completion
2028-06-30

Countries

  • Turkey (Türkiye)

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07593560 on ClinicalTrials.gov