Deep Learning for Early Scoliosis Detection Using mmWave Radar Gait Data

NCT07593560 | Not Yet Recruiting

• 1 other identifier: GTU-Scoliosis-mmWave-2026
• Study Type: observational
• Target: 200
• Locations: 1 country
• Sites: 1

Brief 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; Scoliosis Idiopathic Adolescent

Keywords

Deep Learning; Artificial Intelligence; Machine Learning; Millimeter Wave Radar; Gait Analysis; Gait; Early Detection

Outcome Measures

Primary Outcomes (1)

• Diagnostic Accuracy of the mmWave Radar-Based Deep Learning Model for Scoliosis Detection (AUC-ROC)

  The diagnostic performance of the mmWave radar-based deep learning classification model is assessed by the area under the receiver operating characteristic curve (AUC-ROC). The AUC-ROC is computed by comparing the model's predicted probability of scoliosis for each participant against the reference standard (clinical examination combined with Cobb angle measurement from standard-of-care radiographic imaging) on a held-out test set. The AUC-ROC is reported as a single numeric value between 0 and 1, with 95% confidence intervals.

  Assessed at the end of the data collection period, approximately 18 months after study start

Secondary Outcomes (3)

• Sensitivity and Specificity of the Deep Learning Model at the Optimal Operating Point

  Assessed at the end of the data collection period, approximately 18 months after study start

• Comparative Diagnostic Performance Across Deep Learning Architectures

  Assessed at the end of the data collection period, approximately 18 months after study start

• Stratified Diagnostic Performance by Scoliosis Severity (Cobb Angle Category)

  Assessed at the end of the data collection period, approximately 18 months after study start

Study Arms (1)

Pediatric Participants Undergoing Scoliosis Evaluation

Consecutive pediatric participants attending the orthopedic outpatient clinic for routine scoliosis evaluation. The cohort includes participants across the full spectrum of clinical assessment outcomes (both scoliosis confirmed and scoliosis ruled out) to enable evaluation of the diagnostic accuracy of the mmWave radar-based deep learning model against the standard-of-care clinical and radiographic reference assessment.

Diagnostic Test: mmWave Radar Gait Assessment

Interventions

mmWave Radar Gait Assessment DIAGNOSTIC_TEST

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.

Pediatric Participants Undergoing Scoliosis Evaluation

Eligibility Criteria

• Age: 2 Years - 75 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Pediatric and adolescent participants attending the pediatric orthopedic outpatient clinic at Başakşehir Çam and Sakura City Hospital for routine scoliosis evaluation. Consecutive eligible patients are invited to participate, including both those with confirmed scoliosis and those in whom scoliosis is ruled out following clinical and radiographic assessment.

You may not qualify if:

• Severe scoliosis requiring urgent surgical intervention that prevents participation in walking tasks
• Refusal to give informed consent or consent

Sponsors & Collaborators

• Gebze Technical University: lead

Study Sites (1)

Başakşehir Çam and Sakura City Hospital

Istanbul, Istanbul, 34480, Turkey (Türkiye)

Location

MeSH Terms

Conditions

Scoliosis

Condition Hierarchy (Ancestors)

Spinal Curvatures; Spinal Diseases; Bone Diseases; Musculoskeletal Diseases

Central Study Contacts

Zehra Bilici, MSc

CONTACT

+905343056166; zbilici@gtu.edu.tr

Ercan Ayaz, Doç Dr.

CONTACT

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Research Assistant / PhD Candidate

Study Record Dates

• First Submitted: May 12, 2026
• First Posted: May 18, 2026
• Study Start (Estimated): June 1, 2026
• Primary Completion (Estimated): December 1, 2027
• Study Completion (Estimated): June 1, 2028
• Last Updated: May 18, 2026

Record last verified: 2026-05

Data Sharing

• IPD Sharing: Will not share

Locations

TU (1)