估计腰椎骨矿物质密度从常规的MRI和X光学与深度学习在脊柱患者
Fabio Galbusera1, Andrea Cina2,3, Dave O'Riordan2
1Department of Teaching, Research and Development, Schulthess Clinic, Lengghalde 2, Zurich, 8008, Switzerland. fabio.galbusera@kws.ch.
概括
机器学习模型可以使用腰部MRI和X射线检测骨质疏松症和骨质疏松症. 这些模型在识别低骨密度方面表现良好,为机会性查提供了潜力.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
- 骨健康研究 骨健康研究
背景情况:
- 骨质疏松症和骨质疏松症是严重的公共卫生问题,通常使用双能量X射线吸收度 (DXA) 进行诊断.
- 传统的成像方法,如MRI和X光学,广泛可用,但通常不用于直接的骨密度评估.
- 开发人工智能驱动的方法可以使用现有的成像数据进行骨密度问题的机会性查.
研究的目的:
- 开发和评估机器学习 (ML) 模型来估计骨矿物质密度 (BMD) 和检测骨质疏松症/骨质疏松症.
- 使用传统的腰部MRI (T1和T2加权) 和平面放射作为成像来源.
- 将临床数据和成像采集参数与ML模型集成,以提高性能.
主要方法:
- 在6个月内编制了429名腰部MRI,放射和DXA患者的回顾性数据库.
- 在373名患者身上训练了ML模型,并在86名患者身上进行了测试.
- 输入包括MRI/放射图像,放射学特征和元数据 (年龄,性别,体型等). 用于估计骨质量和分类T分数 (<-1或<-2.5).
主要成果:
- 最好的ML模型实现了直接BMD估计的0.15-0.16g/cm2的平均绝对误差.
- 对T分数<-1的分类给出了MRI的0.82和X线图的0.80的ROC曲线下的面积.
- 对T分数<-2.5的分类导致MRI的ROC曲线面积为0.80,X线图的ROC曲线面积为0.65.
结论:
- ML模型在检测低骨矿物质密度 (骨质疏松症/骨质疏松症) 方面表现出强大的区分能力.
- 与分类任务相比,直接估计骨矿物质密度值的准确性较为有限.
- 这些基于人工智能的工具,利用常规成像,显示了对追溯数据分析和机会性骨疾病查的希望.
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Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...