3D Inference of the Scoliotic Spine from Depth Maps of the Back

Recent advances combining outer images and deep-learning algorithms (DLA) show promising results in the detection and the characterization of the Adolescent Idiopathic Scoliosis (AIS). However, these methods are providing a limited 2D characterization while scoliosis is defined in 3D. In this study we propose an inference method that takes as input a depthmap of the back of a person and outputs the 3D shape estimation of the thoracolumbar spine. Our DLA method predicts 3D vertebrae positions with an average 3D error of 7.1 mm (std: 4.7 mm). From the predicted 3D positions, scoliosis can be located and estimated with a mean absolute error (MAE) of 5.5 \(^\circ \) (std: 6.2 \(^\circ \) ) in the frontal plane. Moreover, sagittal alignments can be estimated with a MAE of 6.4 \(^\circ \) (std: 5.5 \(^\circ \) ) in kyphosis and 8.3 \(^\circ \) (std: 6.8 \(^\circ \) ) in lordosis. In addition, our non-ionizing approach can detect scoliosis with an accuracy of 89%.