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 - 30o) who were skeletally immature (Risser sign of ≤2) were recruited. Patients receiving biplanar X-ray radiographs (EOS™) were divided between a training-cross-validation cohort (328 patients) and independent testing cohort (110 patients). Another 52 patients receiving standard PA spinal X-rays were recruited for cross-platform validation. Following 3D reconstruction, we designated the major curve apex upon PA X-rays as the region of interest (ROI) for machine learning. A self-attentive capsule network was constructed to differentiate between curves manifesting P and NP trajectories. A two-stage transfer learning strategy was introduced to pre-train and fine-tune the model. Model performance (accuracy, sensitivity, specificity) was compared to that of traditional convolutional neural networks (CNNs) and a clinical parameter-based logistic regression model.
Findings: 3D reconstruction identified that apical rotation of the major curve and torsion were significantly different between P and NP curve trajectories. Our predictive model utilizing an ROI centered on the major curve apex achieved an accuracy of 76.6%, a sensitivity of 75.2% and a specificity of 80.2% upon independent testing. Cross-platform performance upon standard standing PA X-rays yielded an accuracy of 77.1%, a sensitivity of 73.5% and a specificity of 81.0%. Errors in prediction occurred when the degree of apical rotation / torsion was discrepant from that of the subsequent curve trajectory but could be rectified by considering serial X-rays. Performance was superior to that of traditional CNNs as well as clinical parameter-based regression models.
Interpretation: This is the first report of automated prediction of AIS curve progression based on radiomics and deep learning, towards directing treatment strategy at first visit. Patients predicted to be at-risk of progression may be counselled to receive early bracing with enforcement of treatment compliance. Over-treatment may be avoided in curves deemed to be non-progressive. Results need to be consolidated in larger sample populations of different ethnicities.
Funding: The Society for the Relief of Disabled Children (SRDC).
Keywords: Adolescent idiopathic scoliosis; curve progression; deep learning; radiomics; scoliosis screening.
© 2021 The Author(s).