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

Last updated: March 24, 2025
Sponsor: The University of Hong Kong
Overall Status: Active - Recruiting

Phase

N/A

Condition

Holoprosencephaly

Spinal Stenosis

Birth Defects

Treatment

Nude back photo

Clinical Study ID

NCT05146193
AI_Scoliosis
  • Ages 10-80
  • All Genders

Study Summary

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

Eligibility Criteria

Inclusion

Inclusion Criteria:

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

Exclusion

Exclusion Criteria:

  • Refusal for imaging, postoperative patients

Study Design

Total Participants: 2500
Treatment Group(s): 1
Primary Treatment: Nude back photo
Phase:
Study Start date:
May 01, 2022
Estimated Completion Date:
February 01, 2030

Study 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.

Connect with a study center

  • Duchess of Kent Children's Hospital

    Hong Kong,
    Hong Kong

    Active - Recruiting

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