RESUMO
Background: Adolescent idiopathic scoliosis (AIS) is the most common spinal disorder in children, characterized by insidious onset and rapid progression, which can lead to severe consequences if not detected in a timely manner. Currently, the diagnosis of AIS primarily relies on X-ray imaging. However, due to limitations in healthcare access and concerns over radiation exposure, this diagnostic method cannot be widely adopted. Therefore, we have developed and validated a screening system using deep learning technology, capable of generating virtual X-ray images (VXI) from two-dimensional Red Green Blue (2D-RGB) images captured by a smartphone or camera to assist spine surgeons in the rapid, accurate, and non-invasive assessment of AIS. Methods: We included 2397 patients with AIS and 48 potential patients with AIS who visited four medical institutions in mainland China from June 11th 2014 to November 28th 2023. Participants data included standing full-spine X-ray images captured by radiology technicians and 2D-RGB images taken by spine surgeons using a camera. We developed a deep learning model based on conditional generative adversarial networks (cGAN) called Swin-pix2pix to generate VXI on retrospective training (n = 1842) and validation (n = 100) dataset, then validated the performance of VXI in quantifying the curve type and severity of AIS on retrospective internal (n = 100), external (n = 135), and prospective test datasets (n = 268). The prospective test dataset included 268 participants treated in Nanjing, China, from April 19th, 2023, to November 28th, 2023, comprising 220 patients with AIS and 48 potential patients with AIS. Their data underwent strict quality control to ensure optimal data quality and consistency. Findings: Our Swin-pix2pix model generated realistic VXI, with the mean absolute error (MAE) for predicting the main and secondary Cobb angles of AIS significantly lower than other baseline cGAN models, at 3.2° and 3.1° on prospective test dataset. The diagnostic accuracy for scoliosis severity grading exceeded that of two spine surgery experts, with accuracy of 0.93 (95% CI [0.91, 0.95]) in main curve and 0.89 (95% CI [0.87, 0.91]) in secondary curve. For main curve position and curve classification, the predictive accuracy of the Swin-pix2pix model also surpassed that of the baseline cGAN models, with accuracy of 0.93 (95% CI [0.90, 0.95]) for thoracic curve and 0.97 (95% CI [0.96, 0.98]), achieving satisfactory results on three external datasets as well. Interpretation: Our developed Swin-pix2pix model holds promise for using a single photo taken with a smartphone or camera to rapidly assess AIS curve type and severity without radiation, enabling large-scale screening. However, limited data quality and quantity, a homogeneous participant population, and rotational errors during imaging may affect the applicability and accuracy of the system, requiring further improvement in the future. Funding: National Key R&D Program of China, Natural Science Foundation of Jiangsu Province, China Postdoctoral Science Foundation, Nanjing Medical Science and Technology Development Foundation, Jiangsu Provincial Key Research and Development Program, and Jiangsu Provincial Medical Innovation Centre of Orthopedic Surgery.
RESUMO
The genetic architecture of adolescent idiopathic scoliosis (AIS) remains poorly understood. Here we present the result of a 4-stage genome-wide association study composed of 5,953 AIS patients and 8,137 controls. Overall, we identified three novel susceptible loci including rs7593846 at 2p14 near MEIS1 (Pcombined = 1.19 × 10-13, OR = 1.21, 95% CI = 1.10-1.32), rs7633294 at 3p14.1 near MAGI1 (Pcombined = 1.85 × 10-12, OR = 1.20, 95% CI = 1.09-1.32), and rs9810566 at 3q26.2 near TNIK (Pcombined = 1.14 × 10-11, OR = 1.19, 95% CI = 1.08-1.32). We also confirmed a recently reported region associated with AIS at 20p11.22 (Pcombined = 1.61 × 10-15, OR = 1.22, 95% CI = 1.12-1.34). Furthermore, we observed significantly asymmetric expression of Wnt/beta-catenin pathway in the bilateral paraspinal muscle of AIS patients, including beta-catenin, TNIK, and LBX1. This is the first study that unveils the potential role of Wnt/beta-catenin pathway in the development of AIS, and our findings may shed new light on the etiopathogenesis of AIS.