Fully Automatic Deep Learning Model for Spine Refracture in Patients with OVCF : A Multi‐Center Study

作者：Xuetao Zhu, Dejian Liu, Liu Lian, Jingxuan Guo, Zedi Li, Yixiang Zhao, Tianhao Wu, Kaiwen Liu, Xinyu Liu, Xin Pan, Qi Lei, Yuanqiang Zhang, Lei Cheng, Bin Chen · 发表于：Orthopaedic Surgery · 年份：2024 · DOI：10.1111/os.14155 · 被引用次数：10 · 研究领域：Bone health and osteoporosis research、Medical Imaging and Analysis、Spinal Fractures and Fixation Techniques

BACKGROUND: The reaserch of artificial intelligence (AI) model for predicting spinal refracture is limited to bone mineral density, X-ray and some conventional laboratory indicators, which has its own limitations. Besides, it lacks specific indicators related to osteoporosis and imaging factors that can better reflect bone quality, such as computed tomography (CT). OBJECTIVE: To construct a novel predicting model based on bone turn-over markers and CT to identify patients who were more inclined to suffer spine refracture. METHODS: CT images and clinical information of 383 patients (training set = 240 cases of osteoporotic vertebral compression fractures (OVCF), validation set = 63, test set = 80) were retrospectively collected from January 2015 to October 2022 at three medical centers. The U-net model was adopted to automatically segment ROI. Three-dimensional (3D) cropping of all spine regions was used to achieve the final ROI regions including 3D_Full and 3D_RoiOnly. We used the Densenet 121-3D model to model the cropped region and simultaneously build a T-NIPT prediction model. Diagnostics of deep learning models were assessed by constructing ROC curves. We generated calibration curves to assess the calibration performance. Additionally, decision curve analysis (DCA) was used to assess the clinical utility of the predictive models. RESULTS: The performance of the test model is comparable to its performance on the training set (dice coefficients of 0.798, an mIOU of 0.755, ...