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真理图:在定量心血管磁共振中可视化人工智能方法的可靠性评估的一个新方法

Thomas Hadler^1,2,3, Clemens Ammann^1,2,3,4, Hadil Saad^1,2,3

¹Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, ECRC Experimental and Clinical Research Center, Berlin, Germany.

|May 16, 2025

概括

小说Verity情节通过清楚地显示AI-专家协议,提高了人工智能 (AI) 在心血管磁共振 (CMR) 中的可靠性. 这种可视化工具提高了自动心脏量化质量保证.

科学领域:

心血管成像技术和人工智能
在医学研究中的统计可视化.
定量心脏分析定量心脏分析

背景情况:

人工智能 (AI) 被广泛用于心血管磁共振 (CMR) 中的自动定量.
目前的质量保证方法侧重于AI专家之间的差异,缺乏对可靠性和协议的明确沟通.
引入了一种新的统计可视化,Verity图,以解决这个差距.

研究的目的:

在CMR中引入和评估Verity图形,以传达AI驱动的定量参数 (QP) 的可靠性.
为人工智能专家进行比较建立明确的协议标准和描述性统计数据.
将Verity图表的实用性与现有的可视化方法进行比较.

主要方法:

对人工智能专家偏差和差异的耐受性范围是从读者内部和读者之间的评估中得出的.
人工智能专家的协议是通过在既定范围内的偏差信心和差异容忍区间来定义的.
设计了verity图,将可靠性图与密度和散射图合并,并与相关性,盒子和Bland-Altman图进行了比较.

主要成果:

对于CMR体积,功能和组织特征QPs,成功建立了偏差和变异耐受范围.
真理图表在揭示统计属性,异常值和测试假设方面表现优于相关性,盒子和布兰德-阿尔特曼图表.
评估AI专家偏差和差异可接受性的框架由Verity图提供.

结论:

真理图有效地显示偏差,差异,趋势和异常值,有助于AI量化可接受性决定.
这些图案在CMR中的各种AI方法中成功应用,清楚地传达了AI-专家协议.
Verity图表代表了AI在心血管成像中的质量保证的重大进步.

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