Semi-supervised learning method augments minority class examples for robust anomaly detection in clinical signals.
problem Class imbalance in minority class instances impairs robustness of clinical analytics solutions.
method Intelligent augmentation of minority class examples to balance class distribution and construct a smooth decision boundary.
result The proposed method outperforms state-of-the-art algorithms in anomaly detection for clinical signals.
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.
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.
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.
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.
Semi-supervised learning classifies cardiac pathology using motion features from cine MRI.
problem Classifying cardiac pathology based on motion features from cine MRI.
method Semi-supervised learning of apparent flow to generate motion features from non-segmented images.
result The model achieves 95% classification accuracy on ACDC test set.
Study shows DL models trained on healthy subjects perform worse on patients' ECG data.
problem Inefficiency of DL models on heterogeneous datasets for heart beat detection.
method Investigated and evaluated the use of Transfer Learning to adapt DL models to different datasets.
result Transfer Learning improves classification performance on small sample size datasets.
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.
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.
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-…
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.
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.
SVM and N-best algorithm classify microbial marker clades from genome sequences.
problem Classifying microbial clades from genome sequences, especially new species.
method Support vector machine (SVM) with N-best algorithm, time series feature extraction, random fragment generation, k-mer size selection.
result Recognition accuracy rates above 28% in top-1 candidate, above 91% in top-10 candidate.
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.
Improves disease progression prediction using auxiliary surrogate labels and health markers.
problem Challenges in predicting disease progression due to unknown true disease states.
method Integrates hidden Markov model with time-varying discriminative classification model.
result Significant improvement in distinguishing LBD from AD using objective markers.
Novel method identifies proteomic risk markers for Alzheimer disease.
problem Lack of comprehensive proteomic risk markers for Alzheimer disease diagnosis.
method Deep belief network-based feature selection method using proteomic and clinical data.
result Identified an optimal subset of proteins achieving 90% accuracy in Alzheimer disease diagnosis.
AR app visualizes Quranic Surah al-Fil for Islamic education.
problem Lack of interactive and context-rich learning materials for Quranic studies.
method Research and development approach, including data collection, user requirement analysis, interface design, 3D asset creation, and integration of AR technology.
result AR application achieved high accuracy and user satisfaction, enhancing learner engagement and understanding.
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.
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 …
MarkerMap selects key genes for cell type analysis in single-cell RNA-seq.
problem Selecting informative genes from large single-cell RNA-seq datasets is challenging and computationally intensive.
method MarkerMap is a generative model that identifies minimal gene sets explaining cell type variability.
result MarkerMap outperforms existing methods in both supervised and unsupervised marker selection.
New method identifies rare genetic markers for Alzheimer's disease using MRI and genomics.
problem Identifying rare genetic markers for Alzheimer's disease.
method Combining MRI data with genome sequencing, using CNNs for brain traits, and kernel-based tests for rare variants.
result CNNs provide precise brain traits and novel kernels identify rare genetic markers.
Predicts clinical events using a landmark approach with machine learning for large biomarker histories.
problem Dynamic prediction of clinical events from large biomarker histories.
method Landmark approach extended to endogenous markers history combined with machine learning methods for survival data.
result Superlearner combining regularized regressions and random survival forests outperforms standard survival models.
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.
This paper introduces the factorial marked temporal point process model and presents efficient learning methods. In conventional (multi-dimensional) marked temporal point process models, event is often encoded by a single discrete variable i.e. a marker. In this paper, we describe the factorial marked point processes w…
This research synthesizes 12-lead ECG from a single-lead ECG device.
problem Limited cardiac diagnostics from single-lead ECG devices.
method Random forest machine learning model using historical 12-lead recordings.
result Synthesized 12-lead ECG with accuracies exceeding 90%.
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.
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.
CNMs detect tipping points in complex systems using causal network markers.
problem Identifying tipping points ahead of critical transitions in complex systems.
method Introducing CNMs that incorporate causality indicators to detect tipping points.
result CNMs show higher predictive power and accuracy than traditional DNB indicators.
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. The diagnosis of Alzheimer's disease (AD) in routine clinical practice is most commonly based on subjective clinical interpretations. Quantitative electroencephalography (QEEG) measures have been shown to reflect neurodegenerative processes in AD and might qualify as affordable and thereby widely available markers to f…
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.
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…
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.
Modeling latent dynamics in high-dimensional event sequences without prior knowledge.
problem Modeling latent dynamics in high-dimensional event sequences with unknown marker relations.
method Adversarial imitation learning framework decomposed into latent structural intensity model, efficient random walk model, and seq2seq discriminator.
result Effective detection of hidden network among markers and decent prediction for future events.
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.
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.
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 …
Deep learning predicts preterm birth risk with improved accuracy.
problem Improving accuracy in predicting spontaneous preterm deliveries.
method U-Net segmentation network for automatic extraction of cervical length and anterior cervical angle.
result Combined markers reduce false-negative ratio from 30% to 18%
fiBAG integrates multiplatform genomic data to identify disease markers.
problem Understanding complex mechanisms underlying human diseases from multiplatform genomic data.
method fiBAG uses Gaussian process models and Bayes factors to identify functional evidence and guide variable selection.
result fiBAG improves detection of disease-related markers compared to non-integrative methods.
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.