Zbigniew Religa Scientific Club at Biophysics Department, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Zabrze, Poland.
Heart Fail Rev. 2024 Nov;29(6):1217-1234. doi: 10.1007/s10741-024-10431-9. Epub 2024 Aug 14.
Myocarditis, marked by heart muscle inflammation, poses significant clinical challenges. This study, guided by PRISMA guidelines, explores the expanding role of artificial intelligence (AI) in myocarditis, aiming to consolidate current knowledge and guide future research. Following PRISMA guidelines, a systematic review was conducted across PubMed, Cochrane Reviews, Scopus, Embase, and Web of Science databases. MeSH terms including artificial intelligence, deep learning, machine learning, myocarditis, and inflammatory cardiomyopathy were used. Inclusion criteria involved original articles utilizing AI for myocarditis, while exclusion criteria eliminated reviews, editorials, and non-AI-focused studies. The search yielded 616 articles, with 42 meeting inclusion criteria after screening. The identified articles, spanning diagnostic, survival prediction, and molecular analysis aspects, were analyzed in each subsection. Diagnostic studies showcased the versatility of AI algorithms, achieving high accuracies in myocarditis detection. Survival prediction models exhibited robust discriminatory power, particularly in emergency settings and pediatric populations. Molecular analyses demonstrated AI's potential in deciphering complex immune interactions. This systematic review provides a comprehensive overview of AI applications in myocarditis, highlighting transformative potential in diagnostics, survival prediction, and molecular understanding. Collaborative efforts are crucial for overcoming limitations and realizing AI's full potential in improving myocarditis care.
心肌炎表现为心肌炎症，具有重要的临床挑战。本研究以 PRISMA 指南为指导，探讨了人工智能（AI）在心肌炎中的作用不断扩大，旨在整合现有知识并指导未来的研究。按照 PRISMA 指南，对 PubMed、Cochrane Reviews、Scopus、Embase 和 Web of Science 数据库进行了系统评价。使用了包括人工智能、深度学习、机器学习、心肌炎和炎症性心肌病等在内的 MeSH 术语。纳入标准包括使用 AI 进行心肌炎的原始文章，而排除标准则排除了综述、社论和非 AI 重点研究。搜索共产生了 616 篇文章，经过筛选后有 42 篇符合纳入标准。确定的文章涵盖了诊断、生存预测和分子分析方面，在每个小节中进行了分析。诊断研究展示了 AI 算法的多功能性，在心肌炎检测方面达到了很高的准确性。生存预测模型表现出强大的区分能力，特别是在紧急情况下和儿科人群中。分子分析表明了 AI 在解析复杂免疫相互作用方面的潜力。本系统评价全面概述了 AI 在心肌炎中的应用，突出了其在诊断、生存预测和分子理解方面的变革潜力。协作努力对于克服限制和实现 AI 在改善心肌炎治疗方面的全部潜力至关重要。
