Radiograph-comparable Image Synthesis for Spine Alignment Analysis Using Deep Learning with Prospective Clinical Validation

Background: Adolescent idiopathic scoliosis (AIS) is the most common type of spinal disorder affecting children. Clinical screening and diagnosis require physical and radiographic examinations, which are either subjective or increase radiation exposure. We therefore developed and validated a radiation-free portable system and device utilising light-based depth sensing and deep learning technologies to analyse AIS by landmark detection and image synthesis.

Methods: Consecutive patients with AIS attending two local scoliosis clinics in Hong Kong between October 9, 2019, and May 21, 2022, were recruited. Patients were excluded if they had psychological and/or systematic neural disorders that could influence the compliance of the study and/or the mobility of the patients. For each participant, a Red Green Blue-Depth (RGBD) image of the nude back was collected using our in-house radiation-free device. Manually labelled landmarks and alignment parameters by our spine surgeons were considered as the ground truth (GT). Images from training and internal validation cohorts (n = 1936) were used to develop the deep learning models. The model was then prospectively validated on another cohort (n = 302) which was collected in Hong Kong and had the same demographic properties as the training cohort. We evaluated the prediction accuracy of the model on nude back landmark detection as well as the performance on radiograph-comparable image (RCI) synthesis. The obtained RCIs contain sufficient anatomical information that can quantify disease severities and curve types.

Findings: Our model had a consistently high accuracy in predicting the nude back anatomical landmarks with a less than 4-pixel error regarding the mean Euclidian and Manhattan distance. The synthesized RCI for AIS severity classification achieved a sensitivity and negative predictive value of over 0.909 and 0.933, and the performance for curve type classification was 0.974 and 0.908, with spine specialists' manual assessment results on real radiographs as GT. The estimated Cobb angle from synthesized RCIs had a strong correlation with the GT angles (R = 0.984, p < 0.001).

Interpretation: The radiation-free medical device powered by depth sensing and deep learning techniques can provide instantaneous and harmless spine alignment analysis which has the potential for integration into routine screening for adolescents.

Funding: Innovation and Technology Fund (MRP/038/20X), Health Services Research Fund (HMRF) 08192266.