基于机器学习的预测需要外科治疗远半径骨折的必要性

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Jongmin Lim¹, Sehun Chang¹, Kwangsu Kim¹

¹Department of Computer Science and Engineering, Sungkyunkwan University College of Computing and Informatics, Suwon, South Korea.

Journal of orthopaedic surgery and research

|April 26, 2025

概括

整合临床数据和X射线的机器学习模型准确地预测了远半径骨折 (DRF) 的外科手术需求. 这种工具有助于临床医生做出治疗决策,可能改善患者的治疗结果.

科学领域:

整形外科手术整形外科手术
医学成像医学成像
机器学习在医疗保健中的应用

背景情况:

距离半径骨折 (DRFs) 治疗决策涉及复杂的因素.
使用定量和定性工具来指导DRF处理.
为DRF管理开发先进的决策支持工具至关重要.

研究的目的:

开发一种机器学习 (ML) 模型,用于预测DRF患者手术治疗的必要性.
将普通放射成像数据与临床信息相结合,以提高预测准确度.
提高DRF处理规划的客观性和效率.

主要方法:

一个ML模型使用来自1,139名DRF患者的放射和临床数据进行训练.
该模型使用U-Net来提取图像特征,并使用多层感知器来获取临床数据.
使用梯度加权类激活映射 (Grad-CAM) 和夏普利添加式扩展 (SHAP) 实现了可解释性.

主要成果:

集成的ML模型在预测外科手术需要方面实现了高精度 (92.98%),灵敏度 (93.28%) 和特异性 (92.55%).
格拉德-CAM突出了影响预测的关键放射性区域 (放射性关节,放射性转化).
SHAP分析确定了女性性别和并发性骨折是手术的重要预测因素.

结论:

开发的ML模型在协助DRFs的外科治疗评估方面显示出前景.
准确预测手术的必要性可以提高治疗成功率和临床效率.
这种方法为指导骨科创伤护理临床决策提供了有价值的工具.

关键词:

临床数据整合临床数据整合骨折管理骨折管理这是Grad-CAM.这就是 SHAP SHAP 的意思.手术决策的制定过程这就是U-Net.

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