基于深度学习的呼吸肌肉细分作为呼吸功能评估的潜在成像生物标志物
Insung Choi1,2, Juwhan Choi3, Hwan Seok Yong2
1Department of Integrative Medicine, Major in Digital Healthcare, Yonsei University College of Medicine, Seoul, Republic of Korea.
PloS one
|July 26, 2024
概括
本研究介绍了一种人工智能模型,用于从CT扫描中对呼吸道肌肉进行细分和分类. 人工智能识别的呼吸肌体积与肺功能有很强的相关性,这表明它是呼吸系统健康的新生物标志物.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 肺部病理学 肺部病理学
背景情况:
- 呼吸系统疾病是全球死亡的主要原因之一.
- 呼吸道肌肉的定量分析对于评估呼吸系统状态和患者生活质量至关重要.
研究的目的:
- 开发一种自动化的人工智能 (AI) 方法,从计算机断层扫描 (CT) 图像中对三种类型的呼吸道肌肉进行细分和分类.
- 为了将细分的呼吸肌体积和密度与肺功能测试 (PFT) 参数相关联.
主要方法:
- 利用了大约600,000张来自3,200个人的胸部CT图像的大数据集.
- 采用了注意力U-Net架构,以详细和专注地对肌肉组织和特定呼吸道肌肉进行细分.
- 从AI细分的口罩中计算肌肉体积和密度,并与PFT参数进行相关性分析.
主要成果:
- 在细分模型 (0.9823为肌肉组织,0.9688为呼吸肌肉) 和分类 (0.9900一般化子得分) 中获得了高子得分.
- 在分类胸部 (0.9793),直立脊柱 (0.9975) 和肋间肌肉 (0.9839) 中表现出高的F1分数.
- 发现呼吸肌体积和PFT参数之间存在强烈的相关性.
结论:
- 由人工智能模型精确量化的呼吸肌体积显示出作为呼吸功能新型生物标志物的潜力.
- 虽然肌肉密度的相关性较弱,但它可能在未来的医学研究中具有重要意义.
- 开发的AI方法为CT成像中呼吸道肌肉分析提供了一种自动化和准确的方法.
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