A multi-stage ensemble network system to diagnose adolescent idiopathic scoliosis

Objectives To develop a deep learning algorithm to automatically evaluate and diagnose scoliosis on full spinal X-ray images. Methods This retrospective study collected full spinal X-ray images (anteroposterior) from four hospital databases from January 1, 2018, to March 31, 2021. The data were divided into training and validation sets. Full spinal X-ray images for external validation were independently collected at one hospital from April 1, 2021, to June 30, 2021. Model effectiveness was validated with a public dataset. Statistical software R was used to analyze the accuracy and sensitivity of the model curvature and anatomical balance parameters and assess interrater consistency. Results This study included 788 and 185 training and test datasets, respectively. The accuracy and recall of the algorithm model for the Cobb angle, apical vertebrae (AV), upper vertebrae, and lower vertebrae were 89.36%, 85.71%, 77.2%, and 80.24% and 97.35%, 93.38%, 84.11%, and 87.42%, respectively. The symmetric mean absolute percentage error at the Cobb angle was 5.99%, and the automatic measurement time was 1.7 s. The mean absolute error values of the Cobb angle and the distances between the center sacral vertical line and AV and C7 plumb line were 1.07 degrees and 1.12 and 1.38 mm, respectively. Statistical analysis confirmed that the Cobb angle results were in good agreement with the gold standard (interclass coefficients of 0.996, 0.978, and 0.825; p < 0.001). Conclusion Our deep learning algorithm model had high sensitivity and accuracy for scoliosis, which could help radiologists improve their diagnostic efficiency.

Automated summary

This study developed a deep learning algorithm to automatically evaluate and diagnose scoliosis on full spinal X-ray images. The model achieved high sensitivity and accuracy for parameters such as the Cobb angle, apical vertebrae, and demonstrated potential to improve diagnostic efficiency for radiologists.