Scoliosis Idiopathic AdolescentScoliRadar-AINCT07593560
Deep Learning for Early Scoliosis Detection Using mmWave Radar Gait Data
An observational study of Scoliosis Idiopathic Adolescent and Scoliosis, run by Gebze Technical University at 1 site in Turkey (Türkiye).
Trial 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. Read more about this study
- Who is it for?
- Ages 2 to 75 yearsHealthy volunteers can take part
- Conditions the study is for
- Scoliosis Idiopathic Adolescent, Scoliosis
- What is being tested?
- mmWave Radar Gait Assessment
- How many people
- 200 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- Jun 2026to Jun 2028
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 2 to 75 years
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
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.
Who may be able to join
None listed.
Who cannot join
- Severe scoliosis requiring urgent surgical intervention that prevents participation in walking tasks
- Refusal to give informed consent or consent
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Ercan Ayaz, Doç Dr. contact
Turkey (Türkiye)1 site
Başakşehir Çam and Sakura City Hospital
Istanbul, Istanbul, 34480
Zehra Bilici, MSc, 05343056166, zbilici@gtu.edu.tr
Ercan Ayaz, Doç. Dr.
About this study
Official title: 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.
Read more
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.
Read the detailed description
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.
- Conditions
- Scoliosis Idiopathic Adolescent, Scoliosis
- Being tested
- mmWave Radar Gait Assessment
- Keywords
- Deep Learning, Artificial Intelligence, Machine Learning, Millimeter Wave Radar, Gait Analysis, Gait, Early Detection
- Medical subject headings
- Scoliosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
mmWave Radar Gait AssessmentDiagnostic 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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Diagnostic Test: mmWave Radar Gait Assessment |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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
At the operating point that maximizes the Youden index on the validation set, the sensitivity (true positive rate) and specificity (true negative rate) of the deep learning model for classifying scoliosis status are calculated on the held-out test set. Both metrics are reported as percentages with 95% confidence intervals.
Measured: Assessed at the end of the data collection period, approximately 18 months after study start
Comparative Diagnostic Performance Across Deep Learning Architectures
The diagnostic accuracy (AUC-ROC) of multiple deep learning architectures, including convolutional neural networks (CNN), recurrent neural networks (RNN/LSTM), and transformer-based models, is compared on the same dataset using cross-validation. The architecture yielding the highest AUC-ROC is identified as the best-performing model.
Measured: Assessed at the end of the data collection period, approximately 18 months after study start
Stratified Diagnostic Performance by Scoliosis Severity (Cobb Angle Category)
The diagnostic accuracy of the deep learning model is evaluated separately for participants with mild scoliosis (Cobb angle 10 to 24 degrees), moderate scoliosis (Cobb angle 25 to 39 degrees), and severe scoliosis (Cobb angle 40 degrees or greater). Sensitivity is reported for each severity category to assess whether the model detects clinically significant curves of different magnitudes.
Measured: Assessed at the end of the data collection period, approximately 18 months after study start
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 May 2026
- Study startJun 2026 (estimated)
- Primary completionDec 2027 (estimated)
- Study completionJun 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Scoliosis Idiopathic AdolescentStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Gebze Technical University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Zehra Bilici, Gebze Technical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07593560
- Sponsor's protocol ID
- GTU-Scoliosis-mmWave-2026
- Acronym
- ScoliRadar-AI
- Status last verified
- May 2026
- First submitted
- 12 May 2026
- Last updated
- 18 May 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data will not be shared. The dataset consists of biometric gait signatures from minors, classified as personal health data under the Turkish Personal Data Protection Law (Law No. 6698, KVKK) and the Regulation on Personal Health Data. Sharing is restricted by national legislation and institutional policy. De-identified aggregated results will be published in peer-reviewed scientific journals.
Study at a glance
From the registry record, last updated by the sponsor on 18 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Deep Learning-Based Approach for Early Scoliosis Detection Using mmWave Radar-Based Gait Data
- ClinicalTrials.gov ID
- NCT07593560
- Acronym / sponsor ID
- ScoliRadar-AI / GTU-Scoliosis-mmWave-2026
- Conditions
- Scoliosis Idiopathic Adolescent, Scoliosis
- Keywords
- Deep Learning, Artificial Intelligence, Machine Learning, Millimeter Wave Radar, Gait Analysis, Gait, Early Detection
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Gebze Technical UniversityUniversity, hospital or other
- Study start
- Jun 2026 (estimated)
- Primary completion
- Dec 2027 (estimated)
- Study completion
- Jun 2028 (estimated)
- First posted
- 18 May 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Pediatric Participants Undergoing Scoliosis Evaluation | – | Diagnostic Test: mmWave Radar Gait AssessmentConsecutive 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. |
Who may qualify
- Ages
- 2 to 75 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- 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.
Key inclusion criteria (0 listed)
Key exclusion criteria (2 listed)
- Severe scoliosis requiring urgent surgical intervention that prevents participation in walking tasks
- Refusal to give informed consent or consent
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| mmWave Radar Gait AssessmentEach 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. | Diagnostic test | Pediatric Participants Undergoing Scoliosis Evaluation |
Primary outcome
Diagnostic Accuracy of the mmWave Radar-Based Deep Learning Model for Scoliosis Detection (AUC-ROC)
Time frame: Assessed at the end of the data collection period, approximately 18 months after study start
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.
3 secondary outcomes
Sensitivity and Specificity of the Deep Learning Model at the Optimal Operating Point
Time frame: Assessed at the end of the data collection period, approximately 18 months after study start
Comparative Diagnostic Performance Across Deep Learning Architectures
Time frame: Assessed at the end of the data collection period, approximately 18 months after study start
Stratified Diagnostic Performance by Scoliosis Severity (Cobb Angle Category)
Time frame: Assessed at the end of the data collection period, approximately 18 months after study start
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 18 May 2026.
- Individual participant dataSharing planned: No. Individual participant data will not be shared. The dataset consists of biometric gait signatures from minors, classified as personal health data under the Turkish Personal Data Protection Law (Law No. 6698, KVKK) and the Regulation on Personal Health Data. Sharing is restricted by national legislation and institutional policy. De-identified aggregated results will be published in peer-reviewed scientific journals.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Ercan Ayaz, Doç Dr. contact
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Scoliosis Idiopathic Adolescent and Scoliosis, run by Gebze Technical University at 1 site in Turkey (Türkiye).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 0 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 18 May 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Turkey (Türkiye). Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Zehra Bilici, MSc (+905343056166, zbilici@gtu.edu.tr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic Accuracy of the mmWave Radar-Based Deep Learning Model for Scoliosis Detection (AUC-ROC) (time frame: Assessed at the end of the data collection period, approximately 18 months after study start). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 18 May 2026.