An artificial intelligence tool for automated analysis of large-scale unstructured clinical cine cardiac magnetic resonance databases.
An artificial intelligence tool for automated analysis of large-scale unstructured clinical cine cardiac magnetic resonance databases.
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DOI：
10.1093/ehjdh/ztad044
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发表时间：
2023-10
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--
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人工智能（AI）技术已被提出用于短轴（SAX）电影心脏磁共振（CMR）的自动分析，但没有CMR分析工具存在自动分析大型（非结构化）临床CMR数据集。我们开发并验证了一个强大的AI工具，用于在大型临床数据库中从SAX电影CMR开始到结束自动量化心脏功能。我们处理和分析CMR数据库的管道包括识别正确数据的自动化步骤、强大的图像预处理、用于SAX CMR双心室分割和功能生物标志物估计的AI算法，以及用于检测和纠正错误的自动分析后质量控制。分割算法在来自两家NHS医院的2793个CMR扫描上进行了训练，并在来自该数据集（n = 414）和五个外部数据集（n = 6888）的其他病例上进行了验证，包括使用来自所有主要供应商的CMR扫描仪在12个不同中心获得的一系列疾病患者的扫描。心脏生物标志物的中位绝对误差在观察者间变异范围内：所有数据集均<8.4 mL（左心室容积），<9.2 mL（右心室容积），<13.3 g（左心室质量）和<5.9%（射血分数）。根据心脏病表型和扫描仪供应商对病例进行分层，结果显示所有组的表现均良好。我们表明，我们提出的工具结合了图像预处理步骤、在大规模多域CMR数据集上训练的可推广AI算法和质量控制步骤，可以对来自多个中心、供应商和心脏病的（临床或研究）数据库进行稳健的分析。这使得我们的工具能够在大型多中心数据库的全自动处理中使用。基于AI的质量控制CMR分析工具。所提出的CMR分析工具包括CMR图像预处理步骤、在图像分析之前自动选择电影采集的AI方法、从短轴电影CMR堆栈分割心室和心肌的AI方法以及分析后QC步骤。QAgt，地面实况分割数据质量评估; QC，质量控制。
Artificial intelligence (AI) techniques have been proposed for automating analysis of short-axis (SAX) cine cardiac magnetic resonance (CMR), but no CMR analysis tool exists to automatically analyse large (unstructured) clinical CMR datasets. We develop and validate a robust AI tool for start-to-end automatic quantification of cardiac function from SAX cine CMR in large clinical databases. Our pipeline for processing and analysing CMR databases includes automated steps to identify the correct data, robust image pre-processing, an AI algorithm for biventricular segmentation of SAX CMR and estimation of functional biomarkers, and automated post-analysis quality control to detect and correct errors. The segmentation algorithm was trained on 2793 CMR scans from two NHS hospitals and validated on additional cases from this dataset (n = 414) and five external datasets (n = 6888), including scans of patients with a range of diseases acquired at 12 different centres using CMR scanners from all major vendors. Median absolute errors in cardiac biomarkers were within the range of inter-observer variability: <8.4 mL (left ventricle volume), <9.2 mL (right ventricle volume), <13.3 g (left ventricular mass), and <5.9% (ejection fraction) across all datasets. Stratification of cases according to phenotypes of cardiac disease and scanner vendors showed good performance across all groups. We show that our proposed tool, which combines image pre-processing steps, a domain-generalizable AI algorithm trained on a large-scale multi-domain CMR dataset and quality control steps, allows robust analysis of (clinical or research) databases from multiple centres, vendors, and cardiac diseases. This enables translation of our tool for use in fully automated processing of large multi-centre databases. AI-based, quality-controlled CMR analysis tool. The proposed CMR analysis tool consists of CMR image pre-processing steps, an AI method that automatically selects the cine acquisitions prior to image analysis, an AI method that segments the ventricles and the myocardium from short-axis cine CMR stacks, and a post-analysis QC step. QAgt, ground truth segmentation data quality assessment; QC, quality control.
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影响因子：
3.6
作者：
Geiger, Sheila;Steinbach, Jasmin;Skoda, Eva-Maria;Jahre, Lisa;Rentrop, Vanessa;Kocol, Dilara;Jansen, Christoph;Schueren, Lynik;Niedergethmann, Marco;Teufel, Martin;Baeuerle, Alexander
通讯作者：
Baeuerle, Alexander
DOI：
10.3390/healthcare10020322
发表时间：
2022-02-08
期刊：
Healthcare (Basel, Switzerland)
影响因子：
--
作者：
Cruz-Ramos NA;Alor-Hernández G;Colombo-Mendoza LO;Sánchez-Cervantes JL;Rodríguez-Mazahua L;Guarneros-Nolasco LR
通讯作者：
Guarneros-Nolasco LR
影响因子：
3.8
作者：
Gorst, Sarah L.;Armitage, Christopher J.;Brownsell, Simon;Hawley, Mark S.
通讯作者：
Hawley, Mark S.
影响因子：
3.2
作者：
Baines, Rebecca;Bradwell, Hannah;Edwards, Katie;Stevens, Sebastian;Prime, Samantha;Tredinnick-Rowe, John;Sibley, Miles;Chatterjee, Arunangsu
通讯作者：
Chatterjee, Arunangsu
影响因子：
2.2
作者：
Damerau M;Teufel M;Musche V;Dinse H;Schweda A;Beckord J;Steinbach J;Schmidt K;Skoda EM;Bäuerle A
通讯作者：
Bäuerle A