使用人工智能驱动的框架量化3D脚对齐:一个试点研究
Roel Huysentruyt1,2,3, Emmanuel Audenaert4,5, Ide Van den Borre4,6,5
1BioCAT, Department of Human Structure and Repair, Ghent University, Corneel Heymanslaan 10, 9000, Ghent, Belgium. roel.huysentruyt@ugent.be.
Skeletal radiology
|September 27, 2025
概括
本研究引入了一个人工智能框架,用于使用负载CT扫描进行自动化脚对齐评估. 人工智能实现了准确的地标检测,改善了足部形的诊断和治疗计划.
科学领域:
- 整形医学和医学成像学
- 医疗保健中的人工智能
- 生物机械分析 生物机械分析
背景情况:
- 准确的脚和脚对齐评估对于诊断形和指导治疗至关重要.
- 传统的2D射线图在捕捉脚和脚结构的3D复杂性方面存在局限性.
- 承重CT (WBCT) 在生理负载下提供3D视图,但手动地标识别是耗时和可变的.
研究的目的:
- 介绍一种新的AI框架,用于使用WBCT图像上的深度学习里程碑检测自动化脚和脚对齐评估.
- 消除在WBCT分析中需要手动细分和代网格注册的需要.
- 评估AI框架在临床相关的脚形患者群体中的准确性.
主要方法:
- 开发和训练3D U-Net模型,使用热图预测直接从WBCT图像预测22个解剖学地标.
- 利用了74名骨科患者的数据集,这些患者患有像腔和状骨等疾病.
- 通过五重交叉验证对地标和角度进行评估的平均绝对误差.
主要成果:
- 标志识别的平均绝对距离误差在1.00毫米至1.88毫米之间.
- 来自AI地标的自动化临床测量显示,后脚角为0.91°,博勒角为2.90°之间的平均绝对误差.
- 人工智能方法实现的准确度与手动评分器之间的变化性相美.
结论:
- 基于热图的人工智能方法使得自动足脚和脚对齐评估从WBCT成像高精度.
- 这种人工智能驱动的方法为评估脚和脚形态提供了一个潜在的有价值的工具.
- 建议使用更大的数据集进行进一步验证,以确认临床适用性.
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