Diagnostic utility of artificial intelligence for left ventricular scar identification using cardiac magnetic resonance imaging—A systematic review

Cardiovascular Digital Health Journal

Albert Sokol, MBBS¹  Anna Podlasek, MD²  Xin Chen, PhD²  Dorothee Auer, MD, PhD³  Nikesh Jathanna, BMedSci, MRCP⁴  Shahnaz Jamil-Copley, MRCP, PhD⁵ 

[1] Address reprint requests and correspondence: Dr Nikesh Jathanna, Cardiology Research Office, Nottingham City Hospital, Hucknall Rd, Nottingham, Nottinghamshire, NG5 1PB, England.;University of Nottingham, Nottingham, United Kingdom;NIHR Nottingham Biomedical Research Centre, Queen’s Medical Centre, Nottingham, United Kingdom;Trent Cardiac Centre, Nottingham City Hospital, Nottingham University Hospitals NHS Trust, Nottingham, United Kingdom;University of Nottingham, Nottingham, United Kingdom;

关键词: Artificial intelligence;    Cardiac scar;    Deep learning;    Imaging – cardiac magnetic resonance imaging (MRI);    Machine learning;    Neural networks;   

DOI  :  

来源: DOAJ

【 摘 要 】

Background: Accurate, rapid quantification of ventricular scar using cardiac magnetic resonance imaging (CMR) carries importance in arrhythmia management and patient prognosis. Artificial intelligence (AI) has been applied to other radiological challenges with success. Objective: We aimed to assess AI methodologies used for left ventricular scar identification in CMR, imaging sequences used for training, and its diagnostic evaluation. Methods: Following PRISMA recommendations, a systematic search of PubMed, Embase, Web of Science, CINAHL, OpenDissertations, arXiv, and IEEE Xplore was undertaken to June 2021 for full-text publications assessing left ventricular scar identification algorithms. No pre-registration was undertaken. Random-effect meta-analysis was performed to assess Dice Coefficient (DSC) overlap of learning vs predefined thresholding methods. Results: Thirty-five articles were included for final review. Supervised and unsupervised learning models had similar DSC compared to predefined threshold models (0.616 vs 0.633, P = .14) but had higher sensitivity, specificity, and accuracy. Meta-analysis of 4 studies revealed standardized mean difference of 1.11; 95% confidence interval -0.16 to 2.38, P = .09, I2 = 98% favoring learning methods. Conclusion: Feasibility of applying AI to the task of scar detection in CMR has been demonstrated, but model evaluation remains heterogenous. Progression toward clinical application requires detailed, transparent, standardized model comparison and increased model generalizability.

【 授权许可】

Unknown