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

The University of Hong Kong (HKU) logo

The University of Hong Kong (HKU)

Status

Enrolling

Conditions

Spinal Deformity

Treatments

Other: Nude back photo

Study type

Observational

Funder types

Other

Identifiers

NCT05146193
AI_Scoliosis

Details and patient eligibility

About

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

Full 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.

Enrollment

2,500 estimated patients

Sex

All

Ages

10 to 80 years old

Volunteers

No Healthy Volunteers

Inclusion criteria

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

Exclusion criteria

  • Refusal for imaging, postoperative patients

Trial design

2,500 participants in 1 patient group

All subjects are diagnosed of having a spinal deformity
Description:
Routine care of patients with spinal deformities
Treatment:
Other: Nude back photo

Trial contacts and locations

1

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Central trial contact

Teng Zhang, PhD; Jason Pui Yin Cheung, MD, MS

Data sourced from clinicaltrials.gov

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