CardioMOD-Net: A Modal Decomposition-Neural Network Framework for Diagnosis and Prognosis of HFpEF from Echocardiography Cine Loops

• URL: http://arxiv.org/abs/2601.01176v1
• Date: Sat, 03 Jan 2026 12:41:14 GMT
• Title: CardioMOD-Net: A Modal Decomposition-Neural Network Framework for Diagnosis and Prognosis of HFpEF from Echocardiography Cine Loops
• Authors: Andrés Bell-Navas, Jesús Garicano-Mena, Antonella Ausiello, Soledad Le Clainche, María Villalba-Orero, Enrique Lara-Pezzi,
• Abstract summary: Heart failure with preserved ejection fraction (HFpEF) arises from diverse comorbidities and progresses through prolonged subclinical stages.<n>Current echocardiography-based Artificial Intelligence (AI) models focus primarily on binary HFpEF detection in humans.<n>We developed a unified AI framework, CardioMOD-Net, to perform multiclass diagnosis and continuous prediction of HFpEF onset.
• Score: 4.50720422463361
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Introduction: Heart failure with preserved ejection fraction (HFpEF) arises from diverse comorbidities and progresses through prolonged subclinical stages, making early diagnosis and prognosis difficult. Current echocardiography-based Artificial Intelligence (AI) models focus primarily on binary HFpEF detection in humans and do not provide comorbidity-specific phenotyping or temporal estimates of disease progression towards decompensation. We aimed to develop a unified AI framework, CardioMOD-Net, to perform multiclass diagnosis and continuous prediction of HFpEF onset directly from standard echocardiography cine loops in preclinical models. Methods: Mouse echocardiography videos from four groups were used: control (CTL), hyperglycaemic (HG), obesity (OB), and systemic arterial hypertension (SAH). Two-dimensional parasternal long-axis cine loops were decomposed using Higher Order Dynamic Mode Decomposition (HODMD) to extract temporal features for downstream analysis. A shared latent representation supported Vision Transformers, one for a classifier for diagnosis and another for a regression module for predicting the age at HFpEF onset. Results: Overall diagnostic accuracy across the four groups was 65%, with all classes exceeding 50% accuracy. Misclassifications primarily reflected early-stage overlap between OB or SAH and CTL. The prognostic module achieved a root-mean-square error of 21.72 weeks for time-to-HFpEF prediction, with OB and SAH showing the most accurate estimates. Predicted HFpEF onset closely matched true distributions in all groups. Discussion: This unified framework demonstrates that multiclass phenotyping and continuous HFpEF onset prediction can be obtained from a single cine loop, even under small-data conditions. The approach offers a foundation for integrating diagnostic and prognostic modelling in preclinical HFpEF research.

Related papers

• Artificial Intelligence-Enabled Spirometry for Early Detection of Right Heart Failure [14.45988844325437]
  Right heart failure (RHF) is a disease characterized by abnormalities in the structure or function of the right ventricle (RV)<n>Lung disease often causes increased right ventricular load, leading to RHF.<n>We propose a self-supervised representation learning method to early detecting RHF from patients with cor pulmonale.
  arXiv  Detail & Related papers  (2025-11-17T15:03:04Z)
• FDP: A Frequency-Decomposition Preprocessing Pipeline for Unsupervised Anomaly Detection in Brain MRI [44.4791295950757]
  We develop an unsupervised anomaly detection (UAD) approach for brain MRI.<n>We conduct the first systematic frequency-domain analysis of pathological signatures.<n>We show that Frequency-Decomposition Preprocessing (FDP) framework can leverage frequency-domain reconstruction for simultaneous pathology suppression and anatomical preservation.
  arXiv  Detail & Related papers  (2025-11-17T02:40:14Z)
• SS-DPPN: A self-supervised dual-path foundation model for the generalizable cardiac audio representation [2.013977297550879]
  Self-Supervised Dual-Path Prototypical Network (SS-DPPN) is a foundation model for cardiac audio representation and classification from unlabeled data.<n>SS-DPPN achieves state-of-the-art performance on four cardiac audio benchmarks.
  arXiv  Detail & Related papers  (2025-10-12T17:43:57Z)
• FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis [1.1666234644810893]
  FoundationalECGNet is a foundational framework for automated ECG classification.<n>It first distinguishes between Normal and Abnormal ECG signals, and then classifies the Abnormal signals into one of five cardiac conditions.<n>It shows state-of-the-art performance in multi-class disease detection, including a 99% F1-score for Conduction Disorders and Hypertrophy.
  arXiv  Detail & Related papers  (2025-09-10T19:48:07Z)
• SFD-Mamba2Net: Structure-Guided Frequency-Enhanced Dual-Stream Mamba2 Network for Coronary Artery Segmentation [10.610715643574034]
  Invasive Coronary Angiography (ICA) is regarded as the gold standard for CAD diagnosis.<n>ICA images are characterized by low contrast, high noise levels, and complex, fine-grained vascular structures.<n>We propose SFD-Mamba2Net, an end-to-end framework tailored for ICA-based vascular segmentation and stenosis detection.
  arXiv  Detail & Related papers  (2025-09-10T18:58:45Z)
• Acoustic Index: A Novel AI-Driven Parameter for Cardiac Disease Risk Stratification Using Echocardiography [0.0]
  We introduce the Acoustic Index, a novel AI-derived echocardiographic parameter designed to quantify cardiac dysfunction from standard ultrasound views.<n>The model combines Extended Dynamic Mode Decomposition (EDMD) based on Koopman operator theory with a hybrid neural network that incorporates clinical metadata.<n>In a prospective cohort of 736 patients, encompassing various cardiac pathologies and normal controls, the Acoustic Index achieved an area under the curve (AUC) of 0.89 in an independent test set.<n>Cross-validation across five folds confirmed the robustness of the model, showing that both sensitivity and specificity exceeded 0.8 when evaluated on independent data.
  arXiv  Detail & Related papers  (2025-07-17T21:27:28Z)
• Q-PART: Quasi-Periodic Adaptive Regression with Test-time Training for Pediatric Left Ventricular Ejection Fraction Regression [45.69922532213079]
  We address the challenge of adaptive pediatric Left Ventricular Ejection Fraction (LVEF) assessment.<n>We propose a novel textbfQuasi-textbfPeriodic textbfAdaptive textbfRegression with textbfTest-time Training (Q-PART) framework.
  arXiv  Detail & Related papers  (2025-03-06T06:24:51Z)
• SQUWA: Signal Quality Aware DNN Architecture for Enhanced Accuracy in Atrial Fibrillation Detection from Noisy PPG Signals [37.788535094404644]
  Atrial fibrillation (AF) significantly increases the risk of stroke, heart disease, and mortality. Photoplethysmography ( PPG) signals are susceptible to corruption from motion artifacts and other factors often encountered in ambulatory settings. We propose a novel deep learning model, designed to learn how to retain accurate predictions from partially corrupted PPG.
  arXiv  Detail & Related papers  (2024-04-15T01:07:08Z)
• Development of Automated Neural Network Prediction for Echocardiographic Left ventricular Ejection Fraction [36.58987036154144]
  This paper proposes a new pipeline method based on deep neural networks and ensemble learning to quantify left ventricular ejection fraction (LVEF) The method was developed and validated in an open-source dataset containing 10,030 echocardiograms. This study demonstrates that an automated neural network-based calculation of LVEF is comparable to expert clinicians performing frame-by-frame manual evaluation of cardiac systolic function.
  arXiv  Detail & Related papers  (2024-03-18T18:09:22Z)
• Improving Diffusion Models for ECG Imputation with an Augmented Template Prior [43.6099225257178]
  noisy and poor-quality recordings are a major issue for signals collected using mobile health systems. Recent studies have explored the imputation of missing values in ECG with probabilistic time-series models. We present a template-guided denoising diffusion probabilistic model (DDPM), PulseDiff, which is conditioned on an informative prior for a range of health conditions.
  arXiv  Detail & Related papers  (2023-10-24T11:34:15Z)
• Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model [55.09533240649176]
  The aim of this work is to develop an accurate automatic segmentation method based on deep learning models for the myocardial borders on LGE-MRI. A total number of 320 exams (with a mean number of 6 slices per exam) were used for training and 28 exams used for testing. The performance analysis of the proposed ensemble model in the basal and middle slices was similar as compared to intra-observer study and slightly lower at apical slices.
  arXiv  Detail & Related papers  (2020-05-27T20:44:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.