ConFormer: A Novel Collection of Deep Learning Models to Assist Cardiologists in the Assessment of Cardiac Function

• URL: http://arxiv.org/abs/2312.08567v2
• Date: Wed, 10 Jan 2024 23:36:07 GMT
• Title: ConFormer: A Novel Collection of Deep Learning Models to Assist Cardiologists in the Assessment of Cardiac Function
• Authors: Ethan Thomas, Salman Aslam
• Abstract summary: This paper presents ConFormer, a novel deep learning model designed to automate the estimation ofEF and Left Ventricular Wall Thickness from echocardiograms. The implementation of ConFormer has the potential to enhance preventative cardiology by enabling cost-effective, accessible, and comprehensive heart health monitoring.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Cardiovascular diseases, particularly heart failure, are a leading cause of death globally. The early detection of heart failure through routine echocardiogram screenings is often impeded by the high cost and labor-intensive nature of these procedures, a barrier that can mean the difference between life and death. This paper presents ConFormer, a novel deep learning model designed to automate the estimation of Ejection Fraction (EF) and Left Ventricular Wall Thickness from echocardiograms. The implementation of ConFormer has the potential to enhance preventative cardiology by enabling cost-effective, accessible, and comprehensive heart health monitoring, thereby saving countless lives. The source code is available at https://github.com/Aether111/ConFormer.

Related papers

• Cardiac Copilot: Automatic Probe Guidance for Echocardiography with World Model [66.35766658717205]
  There is a severe shortage of experienced cardiac sonographers, due to the heart's complex structure and significant operational challenges. We present a Cardiac Copilot system capable of providing real-time probe movement guidance. The core innovation lies in proposing a data-driven world model, named Cardiac Dreamer, for representing cardiac spatial structures. We train our model with real-world ultrasound data and corresponding probe motion from 110 routine clinical scans with 151K sample pairs by three certified sonographers.
  arXiv  Detail & Related papers  (2024-06-19T02:42:29Z)
• Echocardiogram Foundation Model -- Application 1: Estimating Ejection Fraction [2.4164193358532438]
  We introduce EchoAI, an echocardiogram foundation model, that is trained using self-supervised learning (SSL) on 1.5 million echocardiograms. We evaluate our approach by fine-tuning EchoAI to estimate the ejection fraction achieving a mean absolute percentage error of 9.40%.
  arXiv  Detail & Related papers  (2023-11-21T13:00:03Z)
• Semantic-aware Temporal Channel-wise Attention for Cardiac Function Assessment [69.02116920364311]
  Existing video-based methods do not pay much attention to the left ventricular region, nor the left ventricular changes caused by motion. We propose a semi-supervised auxiliary learning paradigm with a left ventricular segmentation task, which contributes to the representation learning for the left ventricular region. Our approach achieves state-of-the-art performance on the Stanford dataset with an improvement of 0.22 MAE, 0.26 RMSE, and 1.9% $R2$.
  arXiv  Detail & Related papers  (2023-10-09T05:57:01Z)
• Continuous 3D Myocardial Motion Tracking via Echocardiography [30.19879953016694]
  Myocardial motion tracking is an essential clinical tool in the prevention and detection of cardiovascular diseases. Current techniques suffer from incomplete and inaccurate motion estimation of the myocardium in both spatial and temporal dimensions. This paper introduces the Neural Cardiac Motion Field (NeuralCMF) to model the 3D structure and the comprehensive 6D forward/backward motion of the heart.
  arXiv  Detail & Related papers  (2023-10-04T13:11:20Z)
• M(otion)-mode Based Prediction of Ejection Fraction using Echocardiograms [13.112371567924802]
  We propose using the M(otion)-mode of echocardiograms for estimating the left ventricular ejection fraction (EF) and classifying cardiomyopathy. We generate multiple artificial M-mode images from a single echocardiogram and combine them using off-the-shelf model architectures. Our experiments show that the supervised setting converges with only ten modes and is comparable to the baseline method.
  arXiv  Detail & Related papers  (2023-09-07T15:00:58Z)
• Digital twinning of cardiac electrophysiology models from the surface ECG: a geodesic backpropagation approach [39.36827689390718]
  We introduce a novel method, Geodesic-BP, to solve the inverse eikonal problem. We show that Geodesic-BP can reconstruct a simulated cardiac activation with high accuracy in a synthetic test case. Given the future shift towards personalized medicine, Geodesic-BP has the potential to help in future functionalizations of cardiac models.
  arXiv  Detail & Related papers  (2023-08-16T14:57:12Z)
• Multi-scale Cross-restoration Framework for Electrocardiogram Anomaly Detection [33.48389041651675]
  Electrocardiogram (ECG) is a widely used diagnostic tool for detecting heart conditions. Rare cardiac diseases may be underdiagnosed using traditional ECG analysis, considering that no training dataset can exhaust all possible cardiac disorders. This paper proposes using anomaly detection to identify any unhealthy status, with normal ECGs solely for training.
  arXiv  Detail & Related papers  (2023-08-03T09:16:57Z)
• A dynamic risk score for early prediction of cardiogenic shock using machine learning [15.597400667978913]
  Myocardial infarction and heart failure are major cardiovascular diseases that affect millions of people in the US. Early recognition of cardiogenic shock is critical. We developed a deep learning-based risk stratification tool, called CShock, for patients admitted into the cardiac ICU to predict onset of cardiogenic shock.
  arXiv  Detail & Related papers  (2023-03-22T20:05:22Z)
• Improved Cardiac Arrhythmia Prediction Based on Heart Rate Variability Analysis [0.0]
  Ventricular tachycardia, ventricular fibrillation, and paroxysmal atrial fibrillation are the most commonly-occurring and dangerous arrhythmias. This thesis proposes novel arrhythmia detection and prediction methods to differentiate cardiac arrhythmias from non-life-threatening cardiac events.
  arXiv  Detail & Related papers  (2022-06-07T12:14:05Z)
• ECGDetect: Detecting Ischemia via Deep Learning [0.0]
  RCE- ECG-Detect is a machine learning model to detect the morphological patterns in significant ST change associated with myocardial ischemia. Our deep neural network model, equipped with convolution layers, achieves 90.31% ROC-AUC, 89.34% sensitivity, 87.81% specificity.
  arXiv  Detail & Related papers  (2020-09-28T11:57:26Z)
• A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation [37.70077538403524]
  We argue the importance of heart sound segmentation as a prior step for heart sound classification. We then propose a robust classifier for abnormal heart sound detection. Our new classifier is also shown to be robust, stable and most importantly, explainable, with an accuracy of almost 100% on the widely used PhysioNet dataset.
  arXiv  Detail & Related papers  (2020-05-21T06:36:28Z)

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.