3D topographic acquisitions to predict spinal curvature in adolescent idiopathic scoliosis : a prospective validation study.
prospective_cohort · Level II
Where this comes from
- Record sourced from PubMed, PMID 41921982.
- Also identified by DOI 10.1302/2633-1462.74.BJO-2026-0029.R1 and PMC identifier 13043243.
- Licence recorded as CC BY-NC-ND.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
Abstract
This study aims to determine the reliability, accuracy, and usability of a new health application that uses AI to estimate major coronal curve magnitude in patients with adolescent idiopathic scoliosis (AIS) from 3D surface topography (ST) captured on a smartphone video scan. This is a prospective validation study. AIS patients, aged ten to 18 years, with coronal curve magnitudes ≤ 45° were recruited at a tertiary care spine clinic. A single trained researcher performed scans twice, six months apart, during participants' routine clinical and radiological assessment. Participants were asked to complete a scan once a month between clinic visits, starting the day of recruitment. Agreement was calculated by comparing scan curve magnitude predictions to the reference standard: a three-foot standing spine radiograph measured by blinded spine clinicians. Inter-rater reliability was assessed by comparing in-clinic to home scan predictions. Measures of diagnostic accuracy to determine the app's ability to screen for coronal deformity > 25° and its ability to detect progression > 5° over a six-month period were determined. Successful compared with failed scans were recorded. Among participants (n = 63), 59 patients (94%) had at least one successful in-clinic scan and 32 patients (51%) had at least one successful home scan. Agreement with the reference standard was moderate for in-clinic scans (intraclass correlation coefficient (ICC) 0.535) and poor for home scans (ICC 0.402). Inter-rater reliability between in-clinic and home scans was poor (ICC 0.168). The app had an accuracy of 70% when discriminating between curve magnitudes ± 25° and detecting curve progression > 5°. A larger proportion of scans failed at-home (30%) compared with in-clinic (16%). Conceptually, the app shows potential as an accessible screening tool for scoliosis. However, the accuracy and reliability suggest it is not yet a reasonable replacement for radiographs and in-person clinical evaluation.