Supporting data for "Incorporating Artificial Intelligence and Clinical Informatics for Curve Progression Risk Evaluation in Adolescent Idiopathic Scoliosis to Facilitate Population Screening"

Adolescent idiopathic scoliosis (AIS) is a complex three-dimensional spinal deformity affecting 2-5% of the general population. AIS is diagnosed when Cobb angles exceed 10°, and deterioration of curve magnitude during puberty occurs in two-thirds of patients. Whilst curves <25° are considered mild, those of 25°-45° are of moderate severity, and those >45° are severe and indicated for surgical correction. A student screening system for AIS has been adopted in Hong Kong since the 1990's. Patients are commonly diagnosed via such screening in their early teens when the curvature is mild, yet it remains uncertain which curves will continue to deteriorate upon the remaining period of growth. Prediction of curve progression risk in 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. In addition, patient demographics, vertebral morphology, skeletal maturity, and bone quality represent individual risk factors for progression but have yet to be integrated towards accurate prognostication. The objective of this study was to integrate composite clinical information into deep learning model to accurately predict AIS curves at-risk of progression.

The dataset contains 710 AIS patients receiving regular clinical follow-up in 3-6-month intervals at the Duchess of Kent Children's Hospital (DKCH), enabling labelling of major curve trajectories from first clinic presentation until skeletal maturity. Additional inclusion criteria were (1) diagnosis of AIS, (2) Cobb angle between 11° and 30° upon standing posteroanterior X-rays at first visit, (3) DRU grading ≤ R9U8 to demonstrate growth potential, and (4) regular follow-up concluding at skeletal maturity (R11U9) or upon receiving surgery. This cohort was utilised to develop the spinal X-ray radiomics modules and identified from amongst scoliosis clinic attendees between January 2016 and September 2021, of which more than 90% were referrals from the two-tiered Hong Kong school-aged screening program. Only the major curve with largest Cobb angle was considered for patients with more than one curvature. Curve progression was defined by an increase ≥ 6° between first visit and skeletal maturity, as well as a Cobb angle ≥ 25° at skeletal maturity. Non-progression (NP) was defined by < 6° of curvature increase, or a Cobb angle < 25° at skeletal maturity. Patients with non-progressive curves according to these definitions but received brace treatment were also excluded.

In preparation for automated hand X-ray analysis, an experienced orthopedic researcher used online labeling tool Roboflow to label regions of interest (ROIs) corresponding to (1) the 2nd to 4th metacarpals, (2) distal radial physis, and (3) distal ulnar physis. Pixel-level segmentation labels of the second metacarpus and the corresponding intramedullary mid diaphysis were subsequently labelled. Skeletal maturity indices (DRU and Sanders staging) from both the hand X-ray cohort as well as curve progression cohort were labelled by two experienced orthopedic researchers.

Features contained within the major curve apex of PA spinal radiographs predict curve progression due to their capacity to convey rotation and torsion. On the other hand, whole spine X-rays facilitate assessment of global spinal imbalance as a risk factor for curve progression. Thus, we extracted a regional spinal X-ray ROI (300 × 200-pixel fixed window) centered upon the apical vertebrae/disc of the major curve, together with at least two adjacent vertebras above and below with lateral rib articulations. We also extracted a global spinal X-ray ROI (300 × 300-pixel fixed window) covering T1 to the sacrum together with clavicles, ribs, and pelvis. All ROI images were saved as single channel grayscale image in JPG formatting.