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° 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.

KEY POINTS: • Our deep learning algorithm model had high sensitivity and accuracy for scoliosis, which could help radiologists improve their diagnostic efficiency. • Multi-center validation data were used in this study to guarantee the reliability of the research. • Algorithmic model measures 200 times faster than radiologists.