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自动骨质疏松查系统使用放射学和深度学习从低剂量胸部CT图像

Xiaoyu Tong¹, Shigeng Wang¹, Jingyi Zhang¹

¹Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian 116014, China.

Bioengineering (Basel, Switzerland)

|January 22, 2024

概括

两种使用深度学习 (DL) 和放射学 (Rad) 的新系统可以从低剂量胸部CT扫描中选骨质疏松症. 在骨密度评估方面,DL模型的准确性高于Rad模型.

科学领域:

放射学放射学是一门学科.
医疗成像医学成像
人工智能的人工智能

背景情况:

骨质疏松症查通常需要专门的骨密度扫描.
低剂量胸部CT (LDCT) 扫描,通常由于其他原因进行,包含有关骨密度的信息.
从LDCT开发骨质疏松症评估的自动化方法可以提高查效率.

研究的目的:

开发和评估两种用于骨质疏松症查的自动化系统,使用LDCT图像上的深度学习 (DL) 和放射学 (Rad).
为了比较DL和Rad模型的诊断效率来评估骨矿物质密度 (BMD).

主要方法:

开发了一种用于LDCT图像的自动胸椎结骨 (TVCB) 分段模型.
创建了三类放射学 (Rad) 和深度学习 (DL) 模型来分类骨质疏松症,骨质疏松症和正常骨质量.
在时间验证集上使用接收器操作特征 (ROC) 曲线,决策曲线分析 (DCA) 和准确度指标验证模型性能.

主要成果:

自动化细分模型实现了高性能 (平均DSC为0.96±0.02).
拉德和DL模型都表现出强大的诊断性能,用于BMD评估 (拉德AUC:0.943-0.932;DL AUC:0.983-0.969).
与Rad模型 (73.3%) 相比,DL模型表现出更高的准确性 (81.2%) 并为DCA提供了更大的净收益.

结论:

一个自动化细分框架准确地从LDCT图像中细分了结骨.
DL和Rad模型为自动骨损伤评估提供了可比的诊断性能.
DL模型具有更高的准确性和精度,表明其有可能改善骨质疏松症查.

关键词:

电脑计算的X射线成像骨矿物质密度骨矿物质密度深度学习是一种深度学习.骨质疏松症是一种骨质疏松症.无线电学 (radiomics) 是一种无线电学.断层扫描 (tomography) 是一个非常重要的技术.

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