Convolutional Neural Networks for Automatic Risser Stage Assessment

Purpose: To develop an automatic method for the assessment of the Risser stage using deep learning that could be used in the management panel of adolescent idiopathic scoliosis (AIS). Materials and Methods: In this institutional review board approved-study, a total of 1830 posteroanterior radiographs of patients with AIS (age range, 10-18 years, 70% female) were collected retrospectively and graded manually by six trained readers using the United States Risser staging system. Each radiograph was preprocessed and cropped to include the entire pelvic region. A convolutional neural network was trained to automatically grade conventional radiographs according to the Risser classification. The network was then validated by comparing its accuracy against the interobserver variability of six trained graders from the authors' institution using the Fleiss k statistical measure. Results: Overall agreement between the six observers was fair, with a k coefficient of 0.65 for the experienced graders and agreement of 74.5%. The automatic grading method obtained a k coefficient of 0.72, which is a substantial agreement with the ground truth, and an overall accuracy of 78.0%. Conclusion: The high accuracy of the model presented here compared with human readers suggests that this work may provide a new method for standardization of Risser grading. The model could assist physicians with the task, as well as provide additional insights in the assessment of bone maturity based on radiographs. (C) RSNA, 2020.