Automated Differentiation of Hypertrophic Cardiomyopathy and Cardiac Amyloidosis Using Multi-View Deep Learning Analysis of 2D Echocardiography

Conceitos Básicos

A deep learning-driven pipeline that utilizes multi-view 2D echocardiography to accurately differentiate between hypertrophic cardiomyopathy and cardiac amyloidosis.

Resumo

This study introduces a novel deep learning-based pipeline for differentiating hypertrophic cardiomyopathy (HCM) and cardiac amyloidosis (CA) using 2D multi-view echocardiography. The key highlights are:

1. The pipeline consists of three stages:
   a. View classification using a Vision Transformer (ViT) to classify echocardiographic views into five distinct categories: apical 4-chamber, parasternal long axis of left ventricle, parasternal short axis at mitral valve, papillary muscle, and apex.
   b. Feature extraction using a modified ResNet neural network to extract features from each view separately, and then fusing the features.
   c. Disease classification using a linear network classifier to differentiate between HCM, CA, and normal cardiac function.

2. The dataset includes 212 HCM patients, 30 CA patients, and 200 individuals with normal cardiac function, collected from 2018 to 2022.

3. The proposed multi-view approach achieved a precision of 0.905, recall of 0.905, and micro-F1 score of 0.904, demonstrating its effectiveness in accurately identifying HCM and CA.

4. Visualization using Grad-CAM highlighted the regions of interest that the model focuses on for each view and category, providing interpretability.

5. Error analysis on view classification showed the importance of accurate view identification, as errors can significantly impact the final disease diagnosis.

The study presents a promising deep learning-driven pipeline for the automated and accurate differentiation of HCM and CA using 2D multi-view echocardiography, which can serve as a valuable diagnostic tool in clinical practice.

Estatísticas

Hypertrophic cardiomyopathy (HCM) is one of the most prevalent forms of hereditary primary cardiomyopathy, with a crude prevalence of approximately 0.16 in the Chinese population.
Cardiac amyloidosis (CA) is the most common type of secondary restrictive cardiomyopathy.
The dataset includes 212 HCM patients, 30 CA patients, and 200 individuals with normal cardiac function, collected from 2018 to 2022.

Citações

"Hypertrophic cardiomyopathy (HCM) stands out as one of the most prevalent forms of hereditary primary cardiomyopathy. Statistics indicate that the crude prevalence of HCM in the Chinese population is approximately 0.16, with an adjusted prevalence of 80 per 100,000 after considering age and gender factors [1]."
"Cardiac amyloidosis (CA) is the most common type of secondary restrictive cardiomyopathy. Amyloidosis is a group of diseases in which insoluble amyloid deposits in organs, leading to dysfunction of organ and tissue. The heart is an organ often affected by amyloidosis, usually manifested as myocardial hypertrophy and restrictive congestive heart failure [2]."

Principais Insights Extraídos De

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

by Bo Peng,Xiao... às arxiv.org 04-26-2024

https://arxiv.org/pdf/2404.16522.pdf

Perguntas Mais Profundas

How can the proposed pipeline be further improved to handle data variability across different medical centers and imaging devices?

In order to address data variability across different medical centers and imaging devices, several strategies can be implemented to enhance the robustness and generalizability of the proposed pipeline:

Data Augmentation: Incorporating data augmentation techniques can help in generating additional training samples, thereby increasing the diversity of the dataset. Techniques such as rotation, flipping, scaling, and adding noise can simulate variations in imaging protocols and devices.

Transfer Learning: Utilizing transfer learning with pre-trained models can assist in adapting the model to new datasets from different centers. By leveraging knowledge learned from a large dataset, the model can be fine-tuned on smaller, more specific datasets, improving performance on varied data.

Normalization Techniques: Implementing normalization techniques can standardize the data across different centers and devices. Normalizing pixel values, standardizing image sizes, and adjusting contrast levels can help in reducing variability in input data.

Multi-Center Data Integration: Collecting data from multiple centers and incorporating it into the training process can help the model learn from a diverse range of imaging protocols and devices. This can enhance the model's ability to generalize to unseen data.

Ensemble Learning: Employing ensemble learning techniques by combining predictions from multiple models trained on different datasets can improve the model's overall performance and robustness. By aggregating diverse models, the ensemble can provide more accurate and reliable predictions.

How can the proposed pipeline be further improved to handle data variability across different medical centers and imaging devices?

In order to address data variability across different medical centers and imaging devices, several strategies can be implemented to enhance the robustness and generalizability of the proposed pipeline:

Data Augmentation: Incorporating data augmentation techniques can help in generating additional training samples, thereby increasing the diversity of the dataset. Techniques such as rotation, flipping, scaling, and adding noise can simulate variations in imaging protocols and devices.

Transfer Learning: Utilizing transfer learning with pre-trained models can assist in adapting the model to new datasets from different centers. By leveraging knowledge learned from a large dataset, the model can be fine-tuned on smaller, more specific datasets, improving performance on varied data.

Normalization Techniques: Implementing normalization techniques can standardize the data across different centers and devices. Normalizing pixel values, standardizing image sizes, and adjusting contrast levels can help in reducing variability in input data.

Multi-Center Data Integration: Collecting data from multiple centers and incorporating it into the training process can help the model learn from a diverse range of imaging protocols and devices. This can enhance the model's ability to generalize to unseen data.

Ensemble Learning: Employing ensemble learning techniques by combining predictions from multiple models trained on different datasets can improve the model's overall performance and robustness. By aggregating diverse models, the ensemble can provide more accurate and reliable predictions.

How can the proposed pipeline be further improved to handle data variability across different medical centers and imaging devices?

In order to address data variability across different medical centers and imaging devices, several strategies can be implemented to enhance the robustness and generalizability of the proposed pipeline:

Data Augmentation: Incorporating data augmentation techniques can help in generating additional training samples, thereby increasing the diversity of the dataset. Techniques such as rotation, flipping, scaling, and adding noise can simulate variations in imaging protocols and devices.

Transfer Learning: Utilizing transfer learning with pre-trained models can assist in adapting the model to new datasets from different centers. By leveraging knowledge learned from a large dataset, the model can be fine-tuned on smaller, more specific datasets, improving performance on varied data.

Normalization Techniques: Implementing normalization techniques can standardize the data across different centers and devices. Normalizing pixel values, standardizing image sizes, and adjusting contrast levels can help in reducing variability in input data.

Multi-Center Data Integration: Collecting data from multiple centers and incorporating it into the training process can help the model learn from a diverse range of imaging protocols and devices. This can enhance the model's ability to generalize to unseen data.

Ensemble Learning: Employing ensemble learning techniques by combining predictions from multiple models trained on different datasets can improve the model's overall performance and robustness. By aggregating diverse models, the ensemble can provide more accurate and reliable predictions.