Deep learning automatically distinguishes myocarditis patients from normal subjects based on MRI

Myocarditis, characterized by inflammation of the myocardial tissue, presents substantial risks to cardiovascular functionality, potentially precipitating critical outcomes including heart failure and arrhythmias. This investigation primarily aims to identify the optimal cardiovascular magnetic reso...

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Bibliographic Details
Published in:The international journal of cardiovascular imaging 2024-12, Vol.40 (12), p.2617-2629
Main Authors: Hatfaludi, Cosmin-Andrei, Roșca, Aurelian, Popescu, Andreea Bianca, Chitiboi, Teodora, Sharma, Puneet, Benedek, Theodora, Itu, Lucian Mihai
Format: Article
Language:English
Subjects:
Accuracy
Adult
Algorithms
Arrhythmia
Automation
Cardiac arrhythmia
Cardiac Imaging
Cardiology
Case-Control Studies
Congestive heart failure
Consent
Datasets
Deep Learning
Female
Gadolinium
Heart diseases
Heart failure
Humans
Image Interpretation, Computer-Assisted
Imaging
Machine learning
Magnetic Resonance Imaging
Magnetic Resonance Imaging, Cine
Male
Medicine
Medicine & Public Health
Middle Aged
Myocarditis
Myocarditis - diagnostic imaging
Myocarditis - physiopathology
Myocardium - pathology
Neural networks
Original Paper
Patients
Predictive Value of Tests
Radiology
Reproducibility of Results
Retrospective Studies
Young Adult
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Description
Summary:Myocarditis, characterized by inflammation of the myocardial tissue, presents substantial risks to cardiovascular functionality, potentially precipitating critical outcomes including heart failure and arrhythmias. This investigation primarily aims to identify the optimal cardiovascular magnetic resonance imaging (CMRI) views for distinguishing between normal and myocarditis cases, using deep learning (DL) methodologies. Analyzing CMRI data from a cohort of 269 individuals, with 231 confirmed myocarditis cases and 38 as control participants, we implemented an innovative DL framework to facilitate the automated detection of myocarditis. Our approach was divided into single-frame and multi-frame analyses to evaluate different views and types of acquisitions for optimal diagnostic accuracy. The results demonstrated a weighted accuracy of 96.9%, with the highest accuracy achieved using the late gadolinium enhancement (LGE) 2-chamber view, underscoring the potential of DL in distinguishing myocarditis from normal cases on CMRI data.
ISSN:1875-8312
1569-5794
1875-8312
1573-0743
DOI:10.1007/s10554-024-03284-8