Deep Learning for Early Scoliosis Detection Using mmWave Radar Gait Data

Background:

Adolescent Idiopathic Scoliosis (AIS) is the most common form of spinal deformity in children, affecting approximately 2-4% of adolescents worldwide. Early detection is critical because mild curves can often be managed conservatively (bracing, targeted physiotherapy), whereas advanced curves frequently require surgical correction. Current screening primarily relies on physical examination (forward bend test, scoliometer) supplemented by radiographic confirmation. These methods have known limitations: physical examination has variable sensitivity and inter-observer reliability, while repeated radiographic follow-up exposes pediatric patients to cumulative ionizing radiation. Camera-based motion analysis systems have been proposed as alternatives but raise significant privacy concerns in pediatric populations.

Rationale:

Millimeter-wave (mmWave) radar is a non-ionizing, contactless sensing technology that captures fine-grained motion signatures without producing identifiable visual images. Recent advances in deep learning have demonstrated promising results in interpreting radar-derived gait signals for biomechanical analysis. The investigators hypothesize that subtle biomechanical asymmetries associated with early scoliosis can be detected from mmWave radar gait recordings using appropriately trained deep learning models, providing a privacy-preserving and radiation-free screening modality.

Primary Objective:

To develop and evaluate the diagnostic accuracy of a deep learning model that classifies pediatric participants as having scoliosis or not based on mmWave radar gait data, measured by sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC).

Secondary Objectives:

• To compare diagnostic performance across multiple deep learning architectures (including convolutional neural networks, recurrent neural networks, and transformer-based models).
• To evaluate model performance stratified by age, sex, and curve severity (Cobb angle category).
• To assess test-retest reliability of radar-derived gait features.

Study Design:

This is a single-center, prospective, observational diagnostic accuracy study. Pediatric participants undergoing routine scoliosis evaluation at the participating center are invited to take part. Each participant performs a standardized walking task along a defined path in front of a mmWave radar sensor. Radar recordings are processed and analyzed using deep learning models. Model outputs are compared against the reference standard.

Reference Standard:

Scoliosis status is established by a pediatric orthopedic specialist based on clinical examination supplemented by Cobb angle measurement from existing standard-of-care radiographic data. No additional radiographic imaging is performed for the purpose of this study.

Data Handling and Privacy:

All radar recordings and clinical data are de-identified at the point of collection and stored on institutional servers in compliance with the Turkish Personal Data Protection Law (Law No. 6698, KVKK) and the Regulation on Personal Health Data. Data access is restricted to authorized study personnel. No identifiable visual images are recorded by the mmWave radar sensor.

Participation Criteria:

• Being between 2 and 75 years of age at the time of registration
• Having applied to the pediatric orthopedics outpatient clinic for an assessment of suspected or known scoliosis
• Being able to walk independently for at least 7 meters without assistive devices
• Written informed consent from a parent or legal guardian
• Written informed consent from the participant

Exclusion criteria

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