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

Autor: Xuetao Zhu, Dejian Liu, Lian Liu, Jingxuan Guo, Zedi Li, Yixiang Zhao, Tianhao Wu, Kaiwen Liu, Xinyu Liu, Xin Pan, Lei Qi, Yuanqiang Zhang, Lei Cheng, Bin Chen
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Orthopaedic Surgery, Vol 16, Iss 8, Pp 2052-2065 (2024)
Druh dokumentu: article
ISSN: 1757-7861
1757-7853
DOI: 10.1111/os.14155
Popis: 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, an SA of 0.767, and an OS of 0.017). Univariable and multivariable analysis indicate that T_P1NT was an independent risk factor for refracture. The performance of predicting refractures in different ROI regions showed that 3D_Full model exhibits the highest calibration performance, with a Hosmer–Lemeshow goodness‐of‐fit (HL) test statistic exceeding 0.05. The analysis of the training and test sets showed that the 3D_Full model, which integrates clinical and deep learning results, demonstrated superior performance with significant improvement (p‐value
Databáze: Directory of Open Access Journals
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