Spinal Deformity
Conditions
Keywords
Adult deformity, scoliosis, spondylolisthesis
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.
Interventions
OTHERNude back photo
Nude back photo at baseline and at follow-ups for each patient and visual severity and curve type classifications
Sponsors
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
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
10 Years to 80 Years
Inclusion criteria
* Idiopathic scoliosis, adult deformity (spondylolisthesis, idiopathic kyphosis, kyphoscoliosis, lordoscoliosis)
Exclusion criteria
* Refusal for imaging, postoperative patients
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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
| Measure | Time frame | Description |
|---|---|---|
| Thoracic kyphosis | 1 year | Thoracic kyphosis T5-12: angle between upper endplate of T5 to lower endplate of T12 in the lateral radiograph |
| Lumbar lordosis | 1 year | Lumbar lordosis L1-S1: angle between upper endplate of L1 to top of S1 in the lateral radiograph |
| Pelvic tilt | 1 year | Pelvic tilt angle measurement in degrees in the lateral radiograph |
| Sacral slope | 1 year | Sacral slope angle measurement in degrees in the lateral radiograph |
| Pelvic incidence | 1 year | Pelvic incidence angle measurement in degrees in the lateral radiograph |
| 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 |
| Curve severity | 1 year | Severity classifications: normal-mild; moderate and severe |
Countries
Hong Kong
Contacts
CONTACTJason Pui Yin Cheung, MD, MS
CONTACTTeng Zhang, PhD
PRINCIPAL_INVESTIGATORJason Pui Yin Cheung, MD, MS
Queen Mary Hospital, Duchess of Kent Children's Hospital
Outcome results
None listed