A Deep Learning-Driven Pipeline for Differentiating Hypertrophic Cardiomyopathy from Cardiac Amyloidosis Using 2D Multi-View Echocardiography

• URL: http://arxiv.org/abs/2404.16522v1
• Date: Thu, 25 Apr 2024 11:27:58 GMT
• Title: A Deep Learning-Driven Pipeline for Differentiating Hypertrophic Cardiomyopathy from Cardiac Amyloidosis Using 2D Multi-View Echocardiography
• Authors: Bo Peng, Xiaofeng Li, Xinyu Li, Zhenghan Wang, Hui Deng, Xiaoxian Luo, Lixue Yin, Hongmei Zhang,
• Abstract summary: Hypertrophic cardiomyopathy (HCM) and cardiac amyloidosis (CA) are both heart conditions that can progress to heart failure if untreated. This paper introduces a novel multi-view deep learning approach that utilizes 2D echocardiography for differentiating between HCM and CA.
• Score: 10.098930200447583
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hypertrophic cardiomyopathy (HCM) and cardiac amyloidosis (CA) are both heart conditions that can progress to heart failure if untreated. They exhibit similar echocardiographic characteristics, often leading to diagnostic challenges. This paper introduces a novel multi-view deep learning approach that utilizes 2D echocardiography for differentiating between HCM and CA. The method begins by classifying 2D echocardiography data into five distinct echocardiographic views: apical 4-chamber, parasternal long axis of left ventricle, parasternal short axis at levels of the mitral valve, papillary muscle, and apex. It then extracts features of each view separately and combines five features for disease classification. A total of 212 patients diagnosed with HCM, and 30 patients diagnosed with CA, along with 200 individuals with normal cardiac function(Normal), were enrolled in this study from 2018 to 2022. This approach achieved a precision, recall of 0.905, and micro-F1 score of 0.904, demonstrating its effectiveness in accurately identifying HCM and CA using a multi-view analysis.

Related papers

• 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)
• 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)
• Three-dimensional micro-structurally informed in silico myocardium -- towards virtual imaging trials in cardiac diffusion weighted MRI [58.484353709077034]
  We propose a novel method to generate a realistic numerical phantom of myocardial microstructure. In-silico tissue models enable evaluating quantitative models of magnetic resonance imaging.
  arXiv  Detail & Related papers  (2022-08-22T22:01:44Z)
• AI-enabled Assessment of Cardiac Systolic and Diastolic Function from Echocardiography [1.0082848901582044]
  Left ventricular (LV) function is an important factor in terms of patient management, outcome, and long-term survival of patients with heart disease. Recently published clinical guidelines for heart failure recognise that over reliance on only one measure of cardiac function is suboptimal. Recent advances in AI-based echocardiography analysis have shown excellent results on automated estimation of LV volumes and LV ejection fraction.
  arXiv  Detail & Related papers  (2022-03-21T10:59:48Z)
• Analysis of Digitalized ECG Signals Based on Artificial Intelligence and Spectral Analysis Methods Specialized in ARVC [0.0]
  Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited heart muscle disease that appears between the second and forth decade of a patient's life. The effective and punctual diagnosis of this disease based on Electrocardiograms (ECGs) could have a vital role in reducing premature cardiovascular mortality.
  arXiv  Detail & Related papers  (2022-02-28T13:12:50Z)
• MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images [84.02849948202116]
  This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) MyoPS combines three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation.
  arXiv  Detail & Related papers  (2022-01-10T06:37:23Z)
• Estimation of atrial fibrillation from lead-I ECGs: Comparison with cardiologists and machine learning model (CurAlive), a clinical validation study [0.0]
  This study presents a method to detect atrial fibrillation with lead-I ECGs using artificial intelligence. The aim of the study is to compare the accuracy of the diagnoses estimated by cardiologists and artificial intelligence over lead-I ECGs.
  arXiv  Detail & Related papers  (2021-04-15T12:50:16Z)
• Identification of Ischemic Heart Disease by using machine learning technique based on parameters measuring Heart Rate Variability [50.591267188664666]
  In this study, 18 non-invasive features (age, gender, left ventricular ejection fraction and 15 obtained from HRV) of 243 subjects were used to train and validate a series of several ANN. The best result was obtained using 7 input parameters and 7 hidden nodes with an accuracy of 98.9% and 82% for the training and validation dataset.
  arXiv  Detail & Related papers  (2020-10-29T19:14:41Z)
• Multilabel 12-Lead Electrocardiogram Classification Using Gradient Boosting Tree Ensemble [64.29529357862955]
  We build an algorithm using gradient boosted tree ensembles fitted on morphology and signal processing features to classify ECG diagnosis. For each lead, we derive features from heart rate variability, PQRST template shape, and the full signal waveform. We join the features of all 12 leads to fit an ensemble of gradient boosting decision trees to predict probabilities of ECG instances belonging to each class.
  arXiv  Detail & Related papers  (2020-10-21T18:11:36Z)
• Neural collaborative filtering for unsupervised mitral valve segmentation in echocardiography [60.08918310097638]
  We propose an automated and unsupervised method for the mitral valve segmentation based on a low dimensional embedding of the echocardiography videos. The method is evaluated in a collection of echocardiography videos of patients with a variety of mitral valve diseases and on an independent test cohort. It outperforms state-of-the-art emphunsupervised and emphsupervised methods on low-quality videos or in the case of sparse annotation.
  arXiv  Detail & Related papers  (2020-08-13T12:53:26Z)
• 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.