对于OVCF患者的脊柱折断的全自动深度学习模型:一个多中心研究
Xuetao Zhu1, Dejian Liu1, Lian Liu2
1Department of Orthopaedic Surgery, Qilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong University, Jinan, P. R. China.
Orthopaedic surgery
|July 2, 2024
概括
使用计算机断层扫描 (CT) 和骨周转标记器的新人工智能 (AI) 模型预测了脊柱折裂风险. 3D-Full模型整合了临床数据和深度学习,以提高识别高风险患者的准确性.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 整形外科 整形外科 整形外科
背景情况:
- 目前用于预测脊髓折断的人工智能 (AI) 模型缺乏骨质疏松症和骨质质量的具体指标.
- 现有的方法依赖于骨矿物质密度,X射线和常规实验室测试,这些都有局限性.
研究的目的:
- 开发一种新的脊柱折断预测模型,使用骨周转标记物和计算机断层扫描 (CT) 成像.
- 为了识别患有脊柱折断风险较高的患者.
主要方法:
- 从383名患有骨质疏松性脊椎压缩骨折 (OVCF) 患者的CT图像和临床数据的回顾性收集.
- 使用U-net进行自动兴趣区域 (ROI) 分段和Densenet 121-3D进行预测建模.
- 开发了一个T-NIPT预测模型,并使用ROC曲线,校准曲线和决策曲线分析 (DCA) 评估诊断性能.
主要成果:
- 与仅使用临床特征或3D_RoiOnly的模型相比,3D-Full模型表现出优异的校准性能和预测准确性.
- T_P1NT被确定为折断的独立风险因素.
- 与临床数据相结合的3D全方位模型显示,在预测折断方面有显著的改善 (p < 0.05).
结论:
- T_P1NT是脊柱折裂的重要独立风险因素.
- 开发的3D全人工智能模型在预测脊柱折裂高风险人群方面优于现有的方法和临床医生.
- 该模型的自动细分和检测功能支持现实世界的临床应用.
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