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

• CardiacNet: Learning to Reconstruct Abnormalities for Cardiac Disease Assessment from Echocardiogram Videos [10.06966396329022]
  We propose a novel reconstruction-based approach named CardiacNet to learn a better representation of local cardiac structures and motion abnormalities. CardiacNet is accompanied by the Consistency Deformation Codebook (CDC) and the Consistency Deformed-Discriminator (CDD) to learn the commonalities across abnormal and normal samples. In experiments, our CardiacNet can achieve state-of-the-art results in three different cardiac disease assessment tasks.
  arXiv  Detail & Related papers  (2024-10-28T06:11:03Z)
• Integrating Deep Learning with Fundus and Optical Coherence Tomography for Cardiovascular Disease Prediction [47.7045293755736]
  Early identification of patients at risk of cardiovascular diseases (CVD) is crucial for effective preventive care, reducing healthcare burden, and improving patients' quality of life. This study demonstrates the potential of retinal optical coherence tomography ( OCT) imaging combined with fundus photographs for identifying future adverse cardiac events. We propose a novel binary classification network based on a Multi-channel Variational Autoencoder (MCVAE), which learns a latent embedding of patients' fundus and OCT images to classify individuals into two groups: those likely to develop CVD in the future and those who are not.
  arXiv  Detail & Related papers  (2024-10-18T12:37:51Z)
• Self-supervised inter-intra period-aware ECG representation learning for detecting atrial fibrillation [41.82319894067087]
  We propose an inter-intra period-aware ECG representation learning approach. Considering ECGs of atrial fibrillation patients exhibit the irregularity in RR intervals and the absence of P-waves, we develop specific pre-training tasks for interperiod and intraperiod representations. Our approach demonstrates remarkable AUC performances on the BTCH dataset, textiti.e., 0.953/0.996 for paroxysmal/persistent atrial fibrillation detection.
  arXiv  Detail & Related papers  (2024-10-08T10:03:52Z)
• Sequence-aware Pre-training for Echocardiography Probe Guidance [66.35766658717205]
  Cardiac ultrasound faces two major challenges: (1) the inherently complex structure of the heart, and (2) significant individual variations. Previous works have only learned the population-averaged 2D and 3D structures of the heart rather than personalized cardiac structural features. We propose a sequence-aware self-supervised pre-training method to learn personalized 2D and 3D cardiac structural features.
  arXiv  Detail & Related papers  (2024-08-27T12:55:54Z)
• 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)
• 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)
• 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)

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.