Cardiac motion modeling using LDDMM and shape splines.
problem Difficulties in probing cardiac function due to shape and deformation interactions.
method LDDMM framework, parallel transport, normalization, shape splines.
result Significant differences in model parameters between pathologies, revealing insights into disease dynamics.
Deep learning models trained on adult cardiac MRI data struggle to accurately segment rare congenital heart diseases.
problem Accuracy of U-Net-based segmentation models trained on adult cardiac MRI data when applied to rare congenital heart diseases like Tetralogy of Fallot.
method Cross-validation with four-fold, evaluation on unseen data from different pathologies.
result Deep learning models overfit to the training data, leading to significant accuracy drops when applied to other pathologies.
Radiomics identifies subtle cardiac changes in hypertension.
problem Subtle cardiac alterations in hypertension not captured by conventional imaging.
method Combines feature selection and machine learning for identifying structural and tissue changes.
result Radiomics model detects changes beyond conventional imaging.
Improved deep learning for cardiac disease detection from limited ECG data.
problem Generalizing deep learning models to unseen classes and variations in cardiac disease detection.
method Utilizing unsupervised learning to construct latent spaces for better generalization.
result Significant improvements in F1-scores compared to state-of-the-art deep learning solutions.
This study benchmarks changepoint detection algorithms on cardiac time series data.
problem Identifying state changes in cardiac time series for disease classification.
method Comparison of 8 changepoint detection algorithms on artificial and real cardiac time series data.
result RMDM algorithm achieved highest true positive rate and cross validated accuracy for classification.
Clusters of ACS patients identified for better therapeutic stratification.
problem Data-driven classification and subtyping of ACS patients for improved treatment.
method Outcome-driven clustering using a multi-task neural network with attention.
result Seven patient clusters with distinct characteristics and risk profiles identified.
Efficient method predicts coronary calcium scores in cardiac and chest CTs.
problem Quantifying coronary artery calcium for risk assessment.
method Two ConvNets for direct regression of calcium scores, with optional decision feedback.
result Predicted calcium scores are highly correlated with manual scores and provide insight into decision-making.
Objectives: Atrial fibrillation (AF) is a common heart rhythm disorder associated with deadly and debilitating consequences including heart failure, stroke, poor mental health, reduced quality of life and death. Having an automatic system that diagnoses various types of cardiac arrhythmias would assist cardiologists to…
In the United States, heart disease is the leading cause of death for both men and women, accounting for 610,000 deaths each year [1]. Physicians use Magnetic Resonance Imaging (MRI) scans to take images of the heart in order to non-invasively estimate its structural and functional parameters for cardiovascular diagnos…
This study benchmarks algorithms for automatic segmentation of LGE-MRI images of the left atrium.
problem Challenging segmentation of LGE-MRI images due to low contrast.
method Organized a large-scale benchmarking challenge with 154 3D LGE-MRIs and 27 teams.
result Top method achieved 93.2% dice score and 0.7 mm mean surface to surface distance.
Anatomical and biophysical modeling of left atrium (LA) and proximal pulmonary veins (PPVs) is important for clinical management of several cardiac diseases. Magnetic resonance imaging (MRI) allows qualitative assessment of LA and PPVs through visualization. However, there is a strong need for an advanced image segment…
Method generates natural ECGs with 25 interpretable features.
problem Lack of labeled ECGs for supervised learning and automatic diagnostics.
method Variational autoencoder for ECG generation and feature extraction.
result Low Maximum Mean Discrepancy (0.00383) indicates good ECG generation quality.
Imaging fluorescent disease biomarkers in tissues and skin is a non-invasive method to screen for health conditions. We report an automated process that combines intraoral fluorescent porphyrin biomarker imaging, clinical examinations and machine learning for correlation of systemic health conditions with periodontal d…
The morphological structure of left ventricle segmented from cardiac magnetic resonance images can be used to calculate key clinical parameters, and it is of great significance to the accurate and efficient diagnosis of cardiovascular diseases. Compared with traditional methods, the segmentation algorithms based on ful…
Study finds AI can predict diverse cardiac and non-cardiac diagnoses from a single ECG.
problem Narrow focus of ECG analysis models for diverse medical conditions.
method Exploratory study using a single AI model to predict multiple ICD codes.
result Model achieved AUROC scores > 0.8 for 253 cardiac and 172 non-cardiac diagnoses.
Paper establishes a comprehensive benchmark for ECG time-series analysis.
problem Incomplete understanding of ECG signal properties and limitations in evaluation metrics.
method Categorization of downstream applications, identification of limitations, introduction of a novel metric, benchmarking of time-series models.
result Validation of the effectiveness of the proposed metric and model architecture.
Study analyzes how blood pressure impacts cardiac health.
problem Impact of blood pressure on cardiac function.
method Combines deep learning and variational autoencoder for interpretable biomarkers.
result Identifies key factors and patterns of cardiac adaptation.
Heart diseases constitute a global health burden, and the problem is exacerbated by the error-prone nature of listening to and interpreting heart sounds. This motivates the development of automated classification to screen for abnormal heart sounds. Existing machine learning-based systems achieve accurate classificatio…
GeoECG augments ECG data to improve heart disease detection.
problem Insufficient labeled ECG data and vulnerability to adversarial attacks.
method Wasserstein geodesic perturbation for data augmentation.
result Improved accuracy and robustness in ECG-based heart disease detection.
Deep neural network predicts cardiac shape from MRI images and patient data.
problem Automatic 3D cardiac shape analysis for large-scale studies.
method Uses deep neural networks combining MRI images and patient metadata.
result Significant agreement with reference shapes in cardiac parameters.
Automatically computes reference ranges for UK Biobank cardiac data.
problem Improving healthcare by discovering patterns in large-scale population data.
method Fully automatic pipeline for 3D cardiac MR image analysis.
result Statistically significant agreement between manual and automatic indexes.
The paper uses geometric methods to classify medical data histograms.
problem Classifying medical data histograms for disease diagnosis.
method Information geometry of beta distributions for comparing and classifying histograms.
result Geometric tools, particularly negatively curved Fisher information, enable unique mean calculation and K-means classification.
Enhanced deep CNNs improve cardiac abnormality diagnosis from ECGs.
problem Diagnosing cardiac abnormalities from 12-lead ECGs.
method Training an enhanced deep convolutional neural network with hand-crafted features, data preprocessing, and augmentation.
result Promising generalization performance in ECG diagnosis.
Proposes a hybrid deep learning network for better heart failure survival prediction.
problem Improving survival prediction in heart failure patients.
method Joint analysis of cardiac motion features and clinical risk factors using a hybrid deep learning network.
result Optimal integration of clinical risk factors into deep prediction networks.
Study creates a multimodal learning framework for CVD risk prediction.
problem Predicting cardiovascular disease risk in diverse populations.
method Combines cross modal transformers, graph neural networks, and causal representation learning.
result Model predicts personalized CVD risk with causal invariance across subpopulations.
Novel framework monitors cardiac image segmentation models in real-time.
problem Ensuring continuous high model performance and segmentation results in clinics.
method Formulated as anomaly detection, the framework derives surrogate quality measures for segmentation.
result Demonstrated accurate, fast, and scalable quality control monitoring.
We propose a method to classify cardiac pathology based on a novel approach to extract image derived features to characterize the shape and motion of the heart. An original semi-supervised learning procedure, which makes efficient use of a large amount of non-segmented images and a small amount of images segmented manu…
The chest X-ray (CXR) is by far the most commonly performed radiological examination for screening and diagnosis of many cardiac and pulmonary diseases. There is an immense world-wide shortage of physicians capable of providing rapid and accurate interpretation of this study. A radiologist-driven analysis of over two m…
hyperSBINN improves drug cardiosafety assessment by efficiently modeling cardiac action potentials.
problem Complexity and limited data in modeling cardiac effects of drugs.
method Combining meta-learning with SBINNs to solve parameterized cardiac action potential models.
result hyperSBINN outperforms traditional solvers in speed and accuracy for predicting APD90 values.
The segmentation of the left ventricle (LV) from CINE MRI images is essential to infer important clinical parameters. Typically, machine learning algorithms for automated LV segmentation use annotated contours from only two cardiac phases, diastole, and systole. In this work, we present an analysis work-flow for fully-…
Non-invasive detection of cardiovascular disorders from radiology scans requires quantitative image analysis of the heart and its substructures. There are well-established measurements that radiologists use for diseases assessment such as ejection fraction, volume of four chambers, and myocardium mass. These measuremen…
GANs improve echo frame generation with labeled patches.
problem Automated echocardiography with limited labels.
method Conditional GAN with patch-based discriminator.
result GAN-enhanced echo frames match given segmentation masks.
CardiacGen generates realistic ECG signals for training deep learning models.
problem Creating realistic synthetic ECG signals for training deep learning models.
method Hierarchical deep generative model with multi-objective loss functions.
result Synthetic ECG signals from CardiacGen can be used for data augmentation and improve classifier performance.
Method identifies cardiac ectopic activity sites from 12-lead ECG.
problem Locate dangerous ectopic activation sites in the heart.
method Bayesian optimization on cardiac model and ECG data.
result Method converges to minimum after 11.7-3.5 iterations.
Researchers use operator learning to predict cardiac activation and repolarization times.
problem Computational demands and need for clear, interpretable information in cardiac electrophysiology.
method Exploiting Fourier Neural Operators (FNO) and Kernel Operator Learning (KOL) to learn operator mappings.
result Both FNO and KOL approaches are computationally efficient and robust to hyperparameters.
We introduce a model-based reconstruction framework with deep learned (DL) and smoothness regularization on manifolds (STORM) priors to recover free breathing and ungated (FBU) cardiac MRI from highly undersampled measurements. The DL priors enable us to exploit the local correlations, while the STORM prior enables us …
Method synthesizes 4D CMR images from XCAT model using GAN and SPADE.
problem Synthesizing realistic 4D CMR images with annotations and adaptable styles.
method Hybrid GAN approach with XCAT anatomical ground truth and SPADE for semantic preservation.
result Synthesized images with modality-specific features learned from real CMR data.
Bayesian optimization on cardiac models using a graph convolutional VAE.
problem Optimizing tissue properties in cardiac models with spatially varying properties.
method Graph convolutional VAE for generative modeling of non-Euclidean data.
result Effective optimization of cardiac tissue properties using a novel generative model.
System detects and predicts cardiac anomalies from ECG data.
problem Early detection of cardiac anomalies to reduce healthcare costs and mortality.
method Discrete Wavelet Transform (DWT) and Undecimated Wavelet Transform (UWT) for feature extraction; Bayesian Network Classifier for anomaly prediction.
result Average accuracy of 96.6% for PAC, 92.8% for MI, and 87% for PVC on real ECG datasets.
Performing inference over simulators is generally intractable as their runtime means we cannot compute a marginal likelihood. We develop a likelihood-free inference method to infer parameters for a cardiac simulator, which replicates electrical flow through the heart to the body surface. We improve the fit of a state-o…
Improved cardiac arrhythmia detection in wearable devices with neural networks.
problem Resource constraints in low-power wearable devices for accurate arrhythmia detection.
method Adapted a convolutional-recurrent neural network to a low-power microcontroller, optimizing for precision and memory usage.
result Reduced F1 score from 0.8 to 0.784 in fixed-point precision, with a 195.6KB memory footprint and 33.98MOps/s throughput. Radiomics approach improves cardiac CVD diagnosis from cine-MRI.
problem Inaccurate expert visualization or clinical indices for CVD classification.
method Estimating radiomic features from cine-MRI, feature selection, advanced machine learning.
result Radiomics features correctly classified 100 cases of five cardiac classes.
CLOCS uses contrastive learning to improve cardiac signal representations.
problem Lack of labelled data in cardiac signal analysis.
method Contrastive learning across space, time, and patients.
result CLOCS outperforms state-of-the-art methods and achieves strong generalization.
Recent introduction of wearable single-lead ECG devices of diverse configurations has caught the intrigue of the medical community. While these devices provide a highly affordable support tool for the caregivers for continuous monitoring and to detect acute conditions, such as arrhythmia, their utility for cardiac diag…
Framework for imputing missing heart data to simulate brain-heart interactions.
problem Lack of multi-modal patient data representing heart and brain processes.
method Probabilistic framework for joint cardiac data imputation and mechanistic model personalization.
result Accurate imputation of missing cardiac features in incomplete datasets.
Causal analysis reveals regional discrepancies in TOPCAT trial results.
problem Inconclusive results in TOPCAT trial for heart failure treatment.
method Causal discovery methods with domain knowledge integration.
result Significant causal effects shown for some subgroups globally.
This paper is an attempt to separate cardiac and respiratory signals from an electrical bio-impedance (EBI) dataset. For this two well-known algorithms, namely Principal Component Analysis (PCA) and Independent Component Analysis (ICA), were used to accomplish the task. The ability of the PCA and the ICA methods first …
Novel method embeds generative model into Bayesian optimization for HD cardiac model parameter estimation.
problem High-dimensional optimization of patient-specific cardiac model parameters with limited data.
method Embeds a generative variational auto-encoder into the objective function of Bayesian optimization.
result Improves accuracy of parameter estimation with more than 10x gain in efficiency.