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人工智能量化整个手部增强的突,在动态对比增强MRI的风湿性关节炎

Yijun Mao¹, Kiko Imahori¹, Wanxuan Fang¹

¹Graduate School of Health Sciences, Hokkaido University, Sapporo, Hokkaido, Japan.

Journal of magnetic resonance imaging : JMRI

|May 29, 2024

概括

一个人工智能 (AI) 模型有效地使用动态对比增强MRI量化类风湿性关节炎 (RA) 中的炎症. 最佳的结果需要至少15个阶段,减少扫描时间,同时准确评估疾病活动.

科学领域:

放射学放射学是一门学科.
人工智能的人工智能
医疗成像医学成像

背景情况:

在使用动态对比增强 (DCE) 核磁共振 (MRI) 来自动和有效量化类风湿性关节炎 (RA) 患者的炎症方面存在挑战.
目前的方法可能缺乏有效的疾病监测所需的精度和速度.

研究的目的:

开发和验证一种自动化的人工智能 (AI) 方法,以量化整个手中的RA疾病活动.
优化动态MRI协议,特别是相数,以排除动脉像素并提高量化准确性.

主要方法:

一项回顾性研究利用35名RA患者的DCE-MRI数据.
一个人工智能模型使用DCE-MRI进行了27个阶段的训练,并对具有不同阶段数量的图像进行了测试.
使用诸如曲线下的面积 (AUC),子得分和斯皮尔曼等级相关系数等指标来评估性能,将AI细分与手动概述和RAMRIS得分进行比较.

主要成果:

确定至少需要15个MRI阶段 (约2.5分钟的采集时间) 才能达到最佳性能.
人工智能模型表现出高精度,AUC范围从0.941到0.965.
人工智能细分和基本真相之间观察到强烈的相关性 (联合ROI的斯皮尔曼系数为0.884-0.927,整个手的0.736-0.831).

结论:

开发的基于人工智能的分类模型准确地识别了突炎像素,同时有效地排除了动脉信号.
采用至少15个阶段的优化方案,可以在最短的获取时间内对RA炎症活性进行定量评估.
这种人工智能方法为在类风湿性关节炎中有效和准确的疾病监测提供了有希望的解决方案.

关键词:

人工智能的人工智能是人工智能.动态对比增强的MRI增强了动态对比.一维时间强度数据.类风湿性关节炎类风湿性关节炎

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