Autonomous detection of myocarditis based on the fusion of improved quantum genetic algorithm and adaptive differential evolution optimization back propagation neural network

Wu, Lei; Guo, Siyu; Han, Lina; Song, Xiaowei; Zhao, Zhilei; Cekderi, Anil Baris

doi:10.1007/s13755-023-00237-8

Health Inf Sci Syst

2023

DOI: 10.1007/s13755-023-00237-8

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Autonomous detection of myocarditis based on the fusion of improved quantum genetic algorithm and adaptive differential evolution optimization back propagation neural network

Lei Wu

Siyu Guo

Lina Han

et al.

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Cited by 5 publications

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Artificial intelligence and myocarditis—a systematic review of current applications

Łajczak,

Jóźwik

2024

Heart Fail Rev

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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.

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Artificial intelligence and myocarditis—a systematic review of current applications

Łajczak,

Jóźwik

2024

Heart Fail Rev

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Deep recurrent learning based qualified sequence segment analytical model (QS2AM) for infectious disease detection using CT images

Suganyadevi,

Seethalakshmi

2023

Evolving Systems

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GAMe-BiLSTM: a novel modified metaheuristic deep learning technique for non-ST-segment elevation myocardial infarction classification

Patil,

Mohite-Patil,

Kalipindi

et al. 2024

Int. j. inf. tecnol.

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Autonomous detection of myocarditis based on the fusion of improved quantum genetic algorithm and adaptive differential evolution optimization back propagation neural network

Cited by 5 publications

References 56 publications

Artificial intelligence and myocarditis—a systematic review of current applications

Artificial intelligence and myocarditis—a systematic review of current applications

Deep recurrent learning based qualified sequence segment analytical model (QS2AM) for infectious disease detection using CT images

GAMe-BiLSTM: a novel modified metaheuristic deep learning technique for non-ST-segment elevation myocardial infarction classification

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