Łajczak Paweł, Sahin Oguz Kagan, Matyja Jakub, Puglla Sanchez Luis Rene, Sayudo Iqbal Farhan, Ayesha Ayesha, Lopes Vitor, Majeed Mir Wajid, Krishna Mrinal Murali, Joseph Meghna, Pereira Mable, Obi Ogechukwu, Silva Railla, Lecchi Caterina, Schincariol Michele
Medical University of Silesia, Katowice, Poland.
Edremit State Hospital, Balikesir, Turkey.
Int J Cardiovasc Imaging. 2025 Sep 9. doi: 10.1007/s10554-025-03497-5.
Myocarditis is an inflammation of heart tissue. Cardiovascular magnetic resonance imaging (CMR) has emerged as an important non-invasive imaging tool for diagnosing myocarditis, however, interpretation remains a challenge for novice physicians. Advancements in machine learning (ML) models have further improved diagnostic accuracy, demonstrating good performance. Our study aims to assess the diagnostic accuracy of ML in identifying myocarditis using CMR. A systematic search was performed using PubMed, Embase, Web of Science, Cochrane, and Scopus to identify studies reporting the diagnostic accuracy of ML in the detection of myocarditis using CMR. The included studies evaluated both image-based and report-based assessments using various ML models. Diagnostic accuracy was estimated using a Random-Effects model (R software). We found a total of 141 ML model results from a total of 12 studies, which were included in the systematic review. The best models achieved 0.93 (95% Confidence Interval (CI) 0.88-0.96) sensitivity and 0.95 (95% CI 0.89-0.97) specificity. Pooled area under the curve was 0.97 (95% CI 0.93-0.98). Comparisons with human physicians showed comparable results for diagnostic accuracy of myocarditis. Quality assessment concerns and heterogeneity were present. CMR augmented using ML models with advanced algorithms can provide high diagnostic accuracy for myocarditis, even surpassing novice CMR radiologists. However, high heterogeneity, quality assessment concerns, and lack of information on cost-effectiveness may limit the clinical implementation of ML. Future investigations should explore cost-effectiveness and minimize biases in their methodologies.
心肌炎是心脏组织的炎症。心血管磁共振成像（CMR）已成为诊断心肌炎的重要非侵入性成像工具，然而，对于新手医生来说，解读仍然是一项挑战。机器学习（ML）模型的进步进一步提高了诊断准确性，表现良好。我们的研究旨在评估ML在使用CMR识别心肌炎方面的诊断准确性。通过使用PubMed、Embase、Web of Science、Cochrane和Scopus进行系统检索，以识别报告ML在使用CMR检测心肌炎方面诊断准确性的研究。纳入的研究使用各种ML模型评估了基于图像和基于报告的评估。使用随机效应模型（R软件）估计诊断准确性。我们共从12项研究中获得了141个ML模型结果，并将其纳入系统评价。最佳模型的灵敏度为0.93（95%置信区间（CI）0.88 - 0.96），特异度为0.95（95%CI 0.89 - 0.97）。汇总曲线下面积为0.97（95%CI 0.93 - 0.98）。与人类医生的比较显示，心肌炎诊断准确性的结果相当。存在质量评估问题和异质性。使用具有先进算法的ML模型增强的CMR可为心肌炎提供高诊断准确性，甚至超过新手CMR放射科医生。然而，高异质性、质量评估问题以及缺乏成本效益信息可能会限制ML的临床应用。未来的研究应探索成本效益并尽量减少其方法中的偏差。
