使用人工智能进行肺通风MRI的疾病分类
Alexander M Matheson1, Abdullah S Bdaiwi1, Matthew M Willmering2
1Center for Pulmonary Imaging Research, Division of Pulmonary Medicine, Cincinnati, Ohio (A.M.M., A.S.B., M.M.W., E.B.H., L.L.W., Z.I.C., J.C.W.).
Academic radiology
|July 5, 2025
概括
人工智能现在只能使用子MRI图像来对肺部疾病进行分类,其表现优于人类医生. 这一突破为诊断和了解肺部疾病提供了新的途径.
科学领域:
- 肺部成像扫描 肺部成像扫描
- 医学中的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 超极化129Xenon MRI通过通风缺陷百分比 (VDP) 来量化肺透气.
- 单靠VDP无法区分各种肺部疾病.
- 之前的研究在异MRI中发现了事性疾病特异性模式,但这些并未被系统地分析.
研究的目的:
- 调查使用卷积神经网络 (CNN) 的人工智能 (AI) 是否可以根据异MRI中的空间模式来分类肺部疾病.
- 为了确定人工智能能否在气通风图像中识别疾病特异性模式.
主要方法:
- 使用了来自262名参与者的XenonMRI数据,涉及6种疾病 (控制,喘,支气管炎消灭综合征,支气管肺功能障碍,囊性纤维化,LAM).
- 包括VGG-16在内的卷积神经网络被训练来根据图像模式来分类疾病.
- 使用准确度,回忆,精度和AUC评估网络性能;Grad-CAM可视化分类驱动器.
主要成果:
- 性能最高的VGG-16网络实现了56%的top-1和78%的top-2精度.
- 人工智能模型在较大的患者队列 (例如对照组,囊性纤维化,LAM) 上显示出更高的准确性.
- 人工智能网络的表现优于人类观察员,达到61%的top-1准确度,而人类在特定测试组中的准确率为40%.
结论:
- 一个AI工具被开发出来,能够仅使用异通风MRI图像来对肺部疾病进行分类.
- 与人类解释相比,人工智能工具表现出更高的性能,表明图像中固有的疾病特定信息.
- 人工智能分析的Xenon MRI具有挑战性临床病例和疾病表型的潜力.
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