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.
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.
CONCLUSIONS: We developed a deep learning-based, fully automatic segmentation and classification model for Risser stage assessment using abdominal radiographs.
OBJECTIVE: To develop a fully automatic approach for determining the Risser stage using deep learning on abdominal radiographs.
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.
CONCLUSIONS: We developed a deep learning-based, fully automatic segmentation and classification model for Risser stage assessment using abdominal radiographs.
摘要：
背景：人工智能已越来越多地用于医学成像，并在图像分类任务中表现出专家级的性能。
目的：开发一种使用腹部X光片深度学习确定Risser分期的全自动方法。
方法：在这项多中心研究中，1,681仰卧腹部X光片（年龄范围，9-18岁,2019年1月至2022年4月之间获得的50％女性）从三个医疗机构进行回顾性收集，并使用美国Risser分期系统进行手动分级。共有来自医院1和2的1,577张图像用于开发，和104张来自医院3的图像进行外部验证。从每张射线照片来看，使用骨盆骨分割模型DeepLabv3+和EfficientNet-B0编码器,利用90张数字化重建的X线照片,利用骨盆骨面罩进行骨盆计算机断层扫描,提取右和左髂脊斑块图像.使用这些补丁图像，ConvNeXt-B被训练为根据Risser分类进行分级。使用准确性评估了模型的性能，接收器工作特性曲线下面积（AUROC），和平均绝对误差。
结果：全自动Risser阶段评估模型在内部和外部测试集上显示出0.87和0.75的准确性，0.13和0.26的平均绝对误差以及0.99和0.95的AUROC，分别。
结论：我们开发了基于深度学习的，使用腹部X光片进行Risser分期评估的全自动分割和分类模型。
目的：开发一种使用腹部X光片深度学习确定Risser分期的全自动方法。
方法：在这项多中心研究中，1,681仰卧腹部X光片（年龄范围，9-18岁,2019年1月至2022年4月之间获得的50％女性）从三个医疗机构进行回顾性收集，并使用美国Risser分期系统进行手动分级。共有来自医院1和2的1,577张图像用于开发，和104张来自医院3的图像进行外部验证。从每张射线照片来看，使用骨盆骨分割模型DeepLabv3+和EfficientNet-B0编码器,利用90张数字化重建的X线照片,利用骨盆骨面罩进行骨盆计算机断层扫描,提取右和左髂脊斑块图像.使用这些补丁图像，ConvNeXt-B被训练为根据Risser分类进行分级。使用准确性评估了模型的性能，接收器工作特性曲线下面积（AUROC），和平均绝对误差。
结果：全自动Risser阶段评估模型在内部和外部测试集上显示出0.87和0.75的准确性，0.13和0.26的平均绝对误差以及0.99和0.95的AUROC，分别。
结论：我们开发了基于深度学习的，使用腹部X光片进行Risser分期评估的全自动分割和分类模型。
