Paweł Łajczak, Oguz Kagan Sahin, Jakub Matyja, Luis Rene Puglla Sanchez, Iqbal Farhan Sayudo, Ayesha Ayesha, Vitor Lopes, Mir Wajid Majeed, Mrinal Murali Krishna, Meghna Joseph, Mable Pereira, Ogechukwu Obi, Railla Silva, Caterina Lecchi, Michele Schincariol
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. © The Author(s) 2025.
Medical University of Silesia, Katowice, Poland; Edremit State Hospital, Balikesir, Turkey; TU Delft, Delft, Netherlands; Clinico Lozano Blesa University Hospital, Zaragoza, Spain; Medical Research Unit, Universitas Syiah Kuala, Banda Aceh, Indonesia; Shifa College of Medicine, Islamabad, Pakistan; Federal University of Amazonas, Manaus, Brazil; Government Medical College, Srinagar, India; Medical College, Thiruvananthapuram, India; Lincoln American University School of Medicine, Georgetown, Guyana; New York Institute of Technology College of Osteopathic Medicine, Old Westbury, NY, United States; Federal University of Ceara, Fortaleza, Brazil; University of Svizzera Italiana, Lugano, Switzerland; Klinikum Fürth, Friedrich-Alexander-University Erlangen- Nürnberg, Fürth, Germany
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