Deep learning-based fully automatic Risser stage assessment model using abdominal radiographs

Abstract

Background: Artificial intelligence has been increasingly used in medical imaging and has demonstrated expert level performance in image classification tasks. Objective: To develop a fully automatic approach for determining the Risser stage using deep learning on abdominal radiographs. Materials and methods: In this multicenter study, 1,681 supine abdominal radiographs (age range, 9–18 years, 50% female) obtained between January 2019 and April 2022 were collected retrospectively from three medical institutions and graded manually using the United States Risser staging system. A total of 1,577 images from Hospitals 1 and 2 were used for development, and 104 images from Hospital 3 for external validation. From each radiograph, right and left iliac crest patch images were extracted using the pelvic bone segmentation model DeepLabv3 + with the EfficientNet-B0 encoder trained with 90 digitally reconstructed radiographs from pelvic computed tomography scans with a pelvic bone mask. Using these patch images, ConvNeXt-B was trained to grade according to the Risser classification. The model’s performance was evaluated using accuracy, area under the receiver operating characteristic curve (AUROC), and mean absolute error. Results: The fully automatic Risser stage assessment model showed an accuracy of 0.87 and 0.75, mean absolute error of 0.13 and 0.26, and AUROC of 0.99 and 0.95 on internal and external test sets, respectively. Conclusion: We developed a deep learning-based, fully automatic segmentation and classification model for Risser stage assessment using abdominal radiographs. Graphical Abstract: (Figure presented.)