Application of deep learning upon spinal radiographs to predict progression in adolescent idiopathic scoliosis at first clinic visit.

ABSTRACT:

Background

Prediction of curve progression risk in adolescent idiopathic scoliosis (AIS) remains elusive. Prior studies have revealed the potential for three-dimensional (3D) morphological parameters to prognosticate progression, but these require specialized biplanar imaging equipment and labor-intensive software reconstruction. This study aimed to formulate a deep learning model with standing posteroanterior (PA) X-rays at first clinic visit to differentiate between progressive (P) and non-progressive (NP) curves.

Methods

For this retrospective cohort study, we identified patients presenting with AIS between October 2015 to April 2020 at our tertiary referral centre. Patients with mild curvatures (11 - 30^o) who were skeletally immature (Risser sign of ≤2) were r

SUBMITTER: Wang H 

PROVIDER: S-EPMC8639418 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature