Detection of Fibrosis in Cine Magnetic Resonance Images Using Artificial Intelligence Techniques

TL;DR

This study presents a novel AI-based method using convolutional neural networks to detect and quantify myocardial fibrosis in cine cardiac MRI images, achieving high accuracy and spatial correlation.

Contribution

The paper introduces a new AI approach that analyzes myocardial texture in cine MRI to identify fibrosis, demonstrating promising accuracy and spatial localization.

Findings

89% accuracy on validation data

70% accuracy on test data

High spatial correlation in lesion localization

Abstract

Background: Artificial intelligence techniques have demonstrated great potential in cardiology, especially to detect imperceptible patterns for the human eye. In this sense, these techniques seem to be adequate to identify patterns in the myocardial texture which could lead to characterize and quantify fibrosis. Purpose: The aim of this study was to postulate a new artificial intelligence method to identify fibrosis in cine cardiac magnetic resonance (CMR) imaging. Methods: A retrospective observational study was carried out in a population of 75 subjects from a clinical center of San Carlos de Bariloche. The proposed method analyzes the myocardial texture in cine CMR images using a convolutional neural network to determine local myocardial tissue damage. Results: An accuracy of 89% for quantifying local tissue damage was observed for the validation data set and 70% for the test set. In addition, the qualitative analysis showed a high spatial correlation in lesion location. Conclusions: The postulated method enables to spatially identify fibrosis using only the information from cine nuclear magnetic resonance studies, demonstrating the potential of this technique to quantify myocardial viability in the future or to study the lesions etiology