Diagnostic utility of artificial intelligence for left ventricular scar identification using cardiac magnetic resonance imaging-A systematic review.
Diagnostic utility of artificial intelligence for left ventricular scar identification using cardiac magnetic resonance imaging-A systematic review.
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DOI：
10.1016/j.cvdhj.2021.11.005
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发表时间：
2021-12
影响因子：
--
通讯作者：
Jamil-Copley S
中科院分区：
文献类型：
--
作者：
Jathanna N;Podlasek A;Sokol A;Auer D;Chen X;Jamil-Copley S
使用心脏磁共振成像（CMR）准确、快速地定量心室瘢痕对心律失常的治疗和患者预后具有重要意义。人工智能（AI）已成功应用于其他放射挑战。我们的目的是评估人工智能方法在CMR中用于左心室疤痕识别，用于训练的成像序列及其诊断评估。按照PRISMA的建议，系统检索PubMed、Embase、Web of Science、CINAHL、opendisserds、arXiv和IEEE explore，检索评估左心室疤痕识别算法的全文出版物，直至2021年6月。没有进行预先登记。随机效应荟萃分析评估骰子系数（DSC）重叠的学习和预定义阈值方法。35篇文章纳入最后审查。与预定义阈值模型相比，有监督学习和无监督学习模型具有相似的DSC (0.616 vs 0.633, P = .14)，但具有更高的灵敏度、特异性和准确性。4项研究的meta分析显示标准化平均差异为1.11；95%置信区间为-0.16 ~ 2.38,P = 0.09, I2 = 98%支持学习方法。将人工智能应用于CMR中疤痕检测任务的可行性已经得到证实，但模型评估仍然存在异质性。临床应用的进展需要详细、透明、标准化的模型比较和提高模型的通用性。
Accurate, rapid quantification of ventricular scar using cardiac magnetic resonance imaging (CMR) carries importance in arrhythmia management and patient prognosis. Artificial intelligence (AI) has been applied to other radiological challenges with success. We aimed to assess AI methodologies used for left ventricular scar identification in CMR, imaging sequences used for training, and its diagnostic evaluation. Following PRISMA recommendations, a systematic search of PubMed, Embase, Web of Science, CINAHL, OpenDissertations, arXiv, and IEEE Xplore was undertaken to June 2021 for full-text publications assessing left ventricular scar identification algorithms. No pre-registration was undertaken. Random-effect meta-analysis was performed to assess Dice Coefficient (DSC) overlap of learning vs predefined thresholding methods. Thirty-five articles were included for final review. Supervised and unsupervised learning models had similar DSC compared to predefined threshold models (0.616 vs 0.633, P = .14) but had higher sensitivity, specificity, and accuracy. Meta-analysis of 4 studies revealed standardized mean difference of 1.11; 95% confidence interval -0.16 to 2.38, P = .09, I2 = 98% favoring learning methods. Feasibility of applying AI to the task of scar detection in CMR has been demonstrated, but model evaluation remains heterogenous. Progression toward clinical application requires detailed, transparent, standardized model comparison and increased model generalizability.
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影响因子：
3.3
作者：
Carminati, M. Chiara;Boniotti, Cinzia;Caiani, Enrico G.
通讯作者：
Caiani, Enrico G.
影响因子：
3.3
作者：
Larroza, Andres;Materka, Andrzej;Moratal, David
通讯作者：
Moratal, David
DOI：
10.1007/s10840-019-00663-3
发表时间：
2020-01-27
影响因子：
1.8
作者：
Cronin, Edmond M.;Bogun, Frank M.;Zeppenfeld, Katja
通讯作者：
Zeppenfeld, Katja
影响因子：
4.8
作者：
Brahim, K.;Qayyum, A.;Meriaudeau, F.
通讯作者：
Meriaudeau, F.
影响因子：
3.3
作者：
Heidenreich, Julius F.;Gassenmaier, Tobias;Wech, Tobias
通讯作者：
Wech, Tobias