ArticleScientific reports2025
Automated cardiac MRI analysis for robust profiling of heart failure models in mice.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- From intelligent models to clinical tools: the evolving landscape of AI in medical imaging.Scientific reports · 2026Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Heart failure with preserved ejection fraction (HFpEF) is a complex, age-related cardiovascular disease with limited treatment options, partly due to a poor understanding of underlying mechanisms, lack of robust preclinical models and diagnostic tools with limited specificity. Traditional cardiac magnetic resonance imaging (MRI) protocols and analysis in preclinical research are time-consuming, and manual analysis methods are prone to high inter-observer variability (correlation coefficient of 0.79 for left ventricular (LV) ejection fraction between observers). To accelerate and standardize phenotyping, we optimized a comprehensive non-contrast cardiac MRI protocol for high throughput, enabling acquisition of a stack of 12 short-axis slices in approximately seven minutes. This time-efficiency allowed us to add additional sequences, including cine-Arterial Spin Labeling (ASL) for myocardial perfusion mapping and dobutamine stress testing, allowing for a comprehensive cardiac exam. We developed a deep learning approach utilizing 3D Convolutional Neural Networks (CNNs) for fully automated segmentation and quantification of cardiac function. We validated this comprehensive pipeline in two multifactorial mouse models of HFpEF, combining diet-induced obesity (DIO) or high fat diet (HFD) and the hypertensive agent, deoxycorticosterone pivalate (DOCP). Our approach illustrated high technical sensitivity by detecting significant myocardial perfusion reduction in both the DIO (p = 0.02) and DIO + DOCP (p = 0.03) groups compared to control, along with subtle diastolic abnormalities, even in the absence of overt changes in ejection fraction. The CNN demonstrated high accuracy and reproducibility, achieving a mean Dice similarity coefficient greater than 0.9 for segmentation and Intraclass Correlation Coefficients (ICC) exceeding 0.95 for key left ventricular functional parameters (volumes and mass) compared to expert consensus reads. This optimized protocol and automated analysis pipeline provides a valuable tool for preclinical cardiovascular research, enabling efficient and reliable assessment of cardiac remodeling and contributing to a deeper understanding of HFpEF progression.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.