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Updated: Jan 15, 2026

Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging

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Published on: May 24, 2021

Multi-phase deep learning model for automated disease classification from cardiac cine MRI.

Nicharee Srikijkasemwat¹, Mauricio Villarroel¹, Abhirup Banerjee^1,2

¹Department of Engineering Science, University of Oxford, Oxford, Oxfordshire, UK.

Journal of the Royal Society, Interface

|October 15, 2025

Summary

Deep learning models can automatically classify cardiovascular diseases (CVDs) from cine Magnetic Resonance Imaging (MRI) scans. This AI approach enhances diagnostic accuracy and provides explainability, building clinical trust in automated CVD detection.

Keywords:

cardiovascular disease cine MRI classification deep learning explainability

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Published on: May 24, 2021

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Published on: June 21, 2024

Area of Science:

Cardiology
Medical Imaging
Artificial Intelligence

Background:

Cardiovascular diseases (CVDs) are a leading global cause of mortality.
Cine Magnetic Resonance Imaging (MRI) is a non-invasive, gold-standard imaging technique for assessing cardiac function and structure.
Current CVD diagnosis relies on expert interpretation of complex imaging data.

Purpose of the Study:

To develop and evaluate a deep learning model for automated classification of CVDs using cine MRI.
To incorporate cardiac function by utilizing both end-diastolic (ED) and end-systolic (ES) phases for improved classification.
To provide model explainability for enhanced clinical trust and understanding.

Main Methods:

Investigated single-phase (ED or ES) and multi-phase (ED and ES) deep learning models (ResNet, DenseNet, VGG).
Trained and tested models on cine MRI datasets for CVD classification.
Employed explainability techniques to visualize disease-specific regions within the heart.

Main Results:

Single-phase models achieved test F1-scores of 71.0% (ED) and [Formula: see text] (ES).
The multi-phase model, incorporating cardiac function, achieved a higher test F1-score of [Formula: see text].
Explainability visualizations highlighted regions indicative of specific CVDs, aiding interpretation.

Conclusions:

Deep learning models can effectively automate CVD classification from cine MRI.
Multi-phase analysis incorporating cardiac function improves classification performance.
Model explainability is crucial for clinical adoption and trust in AI-driven diagnostics.