AI-Powered Scoliosis Auto-Analysis System Multicenter Development and Validations

• Sponsor: The University of Hong Kong (Other)
• Overall Status: Recruiting
• CT.gov ID: NCT05146193
• Collaborator: Peking Union Medical College (Other), Peking University Third Hospital (Other), Beijing Chao Yang Hospital (Other), University of Malaya (Other), Nara Medical University (Other), Hamamatsu University (Other), Ruijin Hospital (Other), Huashan Hospital (Other), Zhejiang University (Other), Ji Shui Tan Hospital (Other)
• Enrollment: 2,500
• Location: 1
• Anticipated Duration (Months): 33.1
• Patients Per Site Per Month: 75.6

Study Details

Study Description

Brief Summary

The investigators aim to use artificial intelligence (AI) to help clinicians in diagnosing and assessing spinal deformities.

Condition or Disease	Intervention/Treatment	Phase
Spinal Deformity	Other: Nude back photo

Detailed Description

Background Spinal deformity is a prevalent spinal disorder in both paediatric and adult populations. The spine alignment need to be quantitively assessed for further treatment planning. However, the current practice requires spine surgeons to manually place landmarks of endplates and key vertebrae. The process is laborious and prone to inter- and intra-rater variance. Thus, the investigators have developed an AI-powered spine alignment assessment system (AlignProCARE) to facilitate clinicians in fast, accurate and consistent analytical results.

The investigators aim to test and improve the performance of the spine alignment auto-analysis in all patients with spinal deformities in multiple centers including Malaysia, China, and Japan

Objectives:

1. prospectively test the alignment assessment of patients' spinal deformities with whole spine X-rays (both PA and lateral) and nude back image with the assessment via AlignProCARE.

2. Collect 500 labeled deformity radiographs and nude back images in both PA and lateral views per center. 150 patients need to be followed up with radiographs and nude back photos collected (all parameters measured again).

3. Use transfer learning to update the current AlignProCARE for scoliosis analysis to form AlignProCARE+.

4 Qualitatively analyse the AlignProCARE+ using an independent dataset.

Study Design

• Study Type: Observational
• Anticipated Enrollment: 2500 participants
• Observational Model: Cohort
• Time Perspective: Prospective
• Official Title: AI-Powered Scoliosis Auto-Analysis System Multicenter Development and Validations
• Actual Study Start Date: May 1, 2022
• Anticipated Primary Completion Date: Feb 1, 2025
• Anticipated Study Completion Date: Feb 1, 2025

Arms and Interventions

Arm	Intervention/Treatment
All subjects are diagnosed of having a spinal deformity Routine care of patients with spinal deformities	Other: Nude back photo; Nude back photo at baseline and at follow-ups for each patient and visual severity and curve type classifications

Outcome Measures

Primary Outcome Measures

1. Cobb angle [1 year]

   Coronal Cobb angle of the spinal deformity. The most tilted end vertebrae away from the apex will be used for measurement of the Cobb angle.The anteroposterior radiograph is used to assess

Secondary Outcome Measures

1. Thoracic kyphosis [1 year]

   Thoracic kyphosis T5-12: angle between upper endplate of T5 to lower endplate of T12 in the lateral radiograph

2. Lumbar lordosis [1 year]

   Lumbar lordosis L1-S1: angle between upper endplate of L1 to top of S1 in the lateral radiograph

3. Pelvic tilt [1 year]

   Pelvic tilt angle measurement in degrees in the lateral radiograph

4. Sacral slope [1 year]

   Sacral slope angle measurement in degrees in the lateral radiograph

5. Pelvic incidence [1 year]

   Pelvic incidence angle measurement in degrees in the lateral radiograph

6. Maximum thoracic kyphosis [1 year]

   Maximum thoracic kyphosis: angle measurement from the upper endplate of most tilted upper end vertebra to the lower endplate of the lower end vertebra of the thoracic spine in the sagittal plane radiograph

7. Curve severity [1 year]

   Severity classifications: normal-mild; moderate and severe

Eligibility Criteria

Criteria

• Ages Eligible for Study: 10 Years to 80 Years
• Sexes Eligible for Study: All

Inclusion Criteria:

• Idiopathic scoliosis, adult deformity (spondylolisthesis, idiopathic kyphosis, kyphoscoliosis, lordoscoliosis)

Exclusion Criteria:

• Refusal for imaging, postoperative patients

Contacts and Locations

Locations

	Site	City	State	Country	Postal Code
1	Duchess of Kent Children's Hospital	Hong Kong		Hong Kong

Sponsors and Collaborators

• The University of Hong Kong
• Peking Union Medical College
• Peking University Third Hospital
• Beijing Chao Yang Hospital
• University of Malaya
• Nara Medical University
• Hamamatsu University
• Ruijin Hospital
• Huashan Hospital
• Zhejiang University
• Ji Shui Tan Hospital

Investigators

• Principal Investigator: Jason Pui Yin Cheung, MD, MS, Queen Mary Hospital, Duchess of Kent Children's Hospital

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.

• Responsible Party: Dr. Jason Pui Yin Cheung, Clinical Associate Professor, The University of Hong Kong
• ClinicalTrials.gov Identifier: NCT05146193
• Other Study ID Numbers: AI_Scoliosis
• First Posted: Dec 6, 2021
• Last Update Posted: Jun 3, 2022
• Last Verified: May 1, 2022

• Individual Participant Data (IPD) Sharing Statement: No
• Plan to Share IPD: No
• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: No
• Keywords provided by Dr. Jason Pui Yin Cheung, Clinical Associate Professor, The University of Hong Kong: Adult deformity, scoliosis, spondylolisthesis
• Additional relevant MeSH terms: Scoliosis; Congenital Abnormalities

Study Results

No Results Posted as of Jun 3, 2022