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一个视觉变压器-卷积神经网络框架,用于决策透明的双能量X射线吸收度推,使用胸部低剂量CT

Duen-Pang Kuo¹, Yung-Chieh Chen¹, Sho-Jen Cheng²

¹Department of Medical Imaging, Taipei Medical University Hospital, Taipei, Taiwan; Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, Taiwan; Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.

International journal of medical informatics

|April 5, 2025

概括

这项研究开发了一个AI框架,将视觉变压器和CNN模型结合起来,以分析CT扫描中的骨密度,改善骨质疏松症的检测,并为进一步的DXA扫描提供指导建议.

科学领域:

医疗成像中的人工智能
放射学和医学诊断医学诊断
骨健康评估骨健康评估

背景情况:

低剂量计算机断层扫描 (LDCT) 越来越多地用于肺癌查,为机会性评估骨状况提供了机会.
从LDCT准确评估骨矿物质密度 (BMD) 和骨质疏松症,可以帮助早期检测和预防骨折.
整合人工智能模型可以提高分析医学成像数据的效率和准确性.

研究的目的:

引入一个整体框架,整合视觉变换器 (ViT) 和卷积神经网络 (CNN) 来评估最不发达国家的骨状况.
为双能X射线吸收度 (DXA) 扫描生成可视化和决策透明的建议.
评估框架在区分正常与异常的骨矿物质密度 (BMD) 和区分骨质疏松症与非骨质疏松症方面的表现.

主要方法:

开发了一个整体框架,将ViT和CNN模型结合起来,使用来自321个人的数据,并对186人进行了验证.
评估了三个视野 (FOV) 设置:适合FOV,半FOV和大FOV.
权重和组合模型预测以提高分类准确性,并为透明度生成可视化.

主要成果:

整体框架的表现明显优于单个模型 (p < 0.001).
在测试队列中实现了高精度:86.6%的BMD分类 (AUROC:0.93) 和76.9%的骨质疏松症分化 (AUROC:0.99).
在测试队列中,DXA推的准确性达到87.1%,对于骨质疏松症检测的准确性为100%.

结论:

ViT-CNN框架有效地评估来自LDCT图像的骨状况,特别是在fitFOV和largeFOV设置下.
视觉化增强决策透明度,帮助临床医生做出明智的DXA建议.
该框架通过LDCT分析支持机会性骨质疏松症查.

关键词:

骨矿物质密度骨矿物质密度卷积神经网络是一个卷积神经网络.低剂量的计算机断层扫描.视觉变压器视觉变压器双能量X射线吸收度计.

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