Machine learning for myocarditis diagnosis using cardiovascular magnetic resonance: a systematic review, diagnostic test accuracy meta-analysis, and comparison with human physicians

• Published in: The international journal of cardiovascular imaging Vol. 41; no. 10; pp. 1921 - 1947
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.10.2025; Springer Nature B.V
• Subjects: Accuracy; Adult; Artificial intelligence; Cardiac Imaging; Cardiology; Clinical Competence; Cost effectiveness; Deep learning; Diagnostic tests; Female; Heart diseases; Heterogeneity; Humans; Image Interpretation, Computer-Assisted - methods; Imaging; Large language models; Learning algorithms; Machine Learning; Magnetic Resonance Imaging; Magnetic Resonance Imaging, Cine; Male; Medical imaging; Medicine; Medicine & Public Health; Meta-analysis; Middle Aged; Myocarditis; Myocarditis - diagnostic imaging; Observer Variation; Physicians; Predictive Value of Tests; Quality assessment; Quality control; Radiology; Reproducibility of Results; Review; Software; Systematic review; Myocarditis; CMR; Diagnostic accuracy; Machine learning
• ISSN: 1875-8312; 1569-5794; 1875-8312; 1573-0743
• 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.