[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100639441":3},{"organization":4,"armGroups":7,"interventions":14,"overallOfficials":10,"centralContacts":20,"locations":28,"responsibleParty":45,"collaborators":10,"id":49,"slug":50,"hasResults":51,"nctId":52,"briefTitle":53,"officialTitle":54,"acronym":55,"eligibilityCriteria":56,"healthyVolunteers":57,"sex":58,"minAge":59,"maxAge":60,"enrollmentInfo":61,"targetDuration":10,"studyType":64,"phases":10,"briefSummary":65,"conditions":66,"keywords":69,"overallStatus":77,"whyStopped":10,"lastUpdateSubmitDate":78,"lastUpdatePostDateStruct":79,"startDateStruct":82,"completionDateStruct":84,"leadSponsor":86,"locationsCount":87},{"fullName":5,"class":6},"Gebze Technical University","OTHER",[8],{"label":9,"type":10,"description":11,"interventionNames":12},"Pediatric Participants Undergoing Scoliosis Evaluation",null,"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.",[13],"Diagnostic Test: mmWave Radar Gait Assessment",[15],{"type":16,"name":17,"description":18,"armGroupLabels":19,"otherNames":10},"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.",[9],[21,26],{"name":22,"role":23,"phone":24,"phoneExt":10,"email":25},"Zehra Bilici, MSc","CONTACT","+905343056166","zbilici@gtu.edu.tr",{"name":27,"role":23,"phone":10,"phoneExt":10,"email":10},"Ercan Ayaz, Doç Dr.",[29],{"facility":30,"status":10,"city":31,"state":31,"zip":32,"country":33,"countryCode":10,"cosmosGeoPoint":34,"geoPoint":39,"contacts":40},"Başakşehir Çam and Sakura City Hospital","Istanbul","34480","Turkey (Türkiye)",{"type":35,"coordinates":36},"Point",[37,38],28.94966,41.01384,{"lat":38,"lon":37},[41,43],{"name":22,"role":23,"phone":42,"phoneExt":10,"email":25},"05343056166",{"name":44,"role":23,"phone":10,"phoneExt":10,"email":10},"Ercan Ayaz, Doç. Dr.",{"type":46,"investigatorFullName":47,"investigatorTitle":48,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"PRINCIPAL_INVESTIGATOR","Zehra Bilici","Research Assistant \u002F PhD Candidate","100639441","deep-learning-for-early-scoliosis-detection-using-mmwave-radar-gait-data-100639441",false,"NCT07593560","Deep Learning for Early Scoliosis Detection Using mmWave Radar Gait Data","A Deep Learning-Based Approach for Early Scoliosis Detection Using mmWave Radar-Based Gait Data","ScoliRadar-AI","Participation Criteria:\n\n* Being between 2 and 75 years of age at the time of registration\n* Having applied to the pediatric orthopedics outpatient clinic for an assessment of suspected or known scoliosis\n* Being able to walk independently for at least 7 meters without assistive devices\n* Written informed consent from a parent or legal guardian\n* Written informed consent from the participant\n\nExclusion Criteria:\n\n* Severe scoliosis requiring urgent surgical intervention that prevents participation in walking tasks\n* Refusal to give informed consent or consent",true,"ALL","2 Years","75 Years",{"count":62,"type":63},200,"ESTIMATED","OBSERVATIONAL","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.\n\nThis 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.\n\nPediatric 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.\n\nIf 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.",[67,68],"Scoliosis Idiopathic Adolescent","Scoliosis",[70,71,72,73,74,75,76],"Deep Learning","Artificial Intelligence","Machine Learning","Millimeter Wave Radar","Gait Analysis","Gait","Early Detection","NOT_YET_RECRUITING","2026-05-12",{"date":80,"type":81},"2026-05-18","ACTUAL",{"date":83,"type":63},"2026-06",{"date":85,"type":63},"2028-06",{"name":5,"class":6},1]