Automatic Quantification of Volumes and Biventricular Function in Cardiac Resonance. Validation of a New Artificial Intelligence Approach

TL;DR

This paper presents a new AI method that accurately and quickly quantifies cardiac volumes and biventricular function from MRI scans, matching expert performance and suitable for clinical use.

Contribution

A novel AI approach using convolutional networks that improves reproducibility and speed in cardiac quantification compared to traditional methods.

Findings

High correlation with manual measurements (Pearson 0.98, 0.92, 0.96, 0.8)

Quantification completed in about 5 seconds per study

Equivalent accuracy to expert assessments

Abstract

Background: Artificial intelligence techniques have shown great potential in cardiology, especially in quantifying cardiac biventricular function, volume, mass, and ejection fraction (EF). However, its use in clinical practice is not straightforward due to its poor reproducibility with cases from daily practice, among other reasons. Objectives: To validate a new artificial intelligence tool in order to quantify the cardiac biventricular function (volume, mass, and EF). To analyze its robustness in the clinical area, and the computational times compared with conventional methods. Methods: A total of 189 patients were analyzed: 89 from a regional center and 100 from a public center. The method proposes two convolutional networks that include anatomical information of the heart to reduce classification errors. Results: A high concordance (Pearson coefficient) was observed between manual quantification and the proposed quantification of cardiac function (0.98, 0.92, 0.96 and 0.8 for volumes and biventricular EF) in about 5 seconds per study. Conclusions: This method quantifies biventricular function and volumes in seconds with an accuracy equivalent to that of a specialist.