Clinical trial NCT05146193: AI-Powered Scoliosis Auto-Analysis System Multicenter Development and Validations
Official title: AI-Powered Scoliosis Auto-Analysis System Multicenter Development and Validations
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Spinal Deformity
Keywords: Adult deformity, scoliosis, spondylolisthesis
Lead sponsor: The University of Hong Kong
Sponsor class: OTHER
Collaborators: name: Peking Union Medical College
name: Peking University Third Hospital
name: Beijing Chao Yang Hospital
name: University of Malaya
name: Nara Medical University
name: Hamamatsu University
name: Ruijin Hospital
name: Huashan Hospital
name: Zhejiang University
name: Ji Shui Tan Hospital
First posted: 2021-12-06
Enrollment: 2500
Sex: ALL
Minimum age (years): 10
Maximum age (years): 80
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: All subjects with spinal deformities

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

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.

Eligibility criteria: Inclusion Criteria:

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

Exclusion Criteria:

* Refusal for imaging, postoperative patients

Primary outcomes:
measure: Cobb angle
timeFrame: 1 year
Secondary outcomes:
measure: Thoracic kyphosis
timeFrame: 1 year
measure: Lumbar lordosis
timeFrame: 1 year
measure: Pelvic tilt
timeFrame: 1 year
measure: Sacral slope
timeFrame: 1 year
measure: Pelvic incidence
timeFrame: 1 year
measure: Maximum thoracic kyphosis
timeFrame: 1 year
measure: Curve severity
timeFrame: 1 year
Other outcomes:
Locations:
city: Hong Kong
country: Hong Kong
facility: Duchess of Kent Children's Hospital
Study officials:
affiliation: Queen Mary Hospital, Duchess of Kent Children's Hospital
name: Jason Pui Yin Cheung, MD, MS
role: PRINCIPAL_INVESTIGATOR
References: