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Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging
Published on: May 24, 2021
Deep-Learning-Based Disease Classification in Patients Undergoing Cine Cardiac MRI
Athira J Jacob1, Teodora Chitiboi1, U Joseph Schoepf2
1Digital Technology and Innovation, Siemens Healthineers, Princeton, New Jersey, USA.
Insights
This study developed a deep learning algorithm using MRI data to classify cardiovascular diseases. The algorithm achieved high accuracy in distinguishing normal subjects from patients with dilated cardiomyopathy, hypertrophic cardiomyopathy, and ischemic heart disease.
Area of Science:
- Cardiovascular Imaging
- Artificial Intelligence in Medicine
- Medical Image Analysis
Background:
- Automated analysis of cardiovascular diseases from MRI can provide fast and reproducible clinical assessments.
- Deep learning (DL) offers potential for automated disease classification from medical imaging.
Purpose of the Study:
- To develop a DL algorithm for classifying cardiovascular diseases using MRI data.
- The algorithm aims to differentiate between normal subjects (NORM) and patients with dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), and ischemic heart disease (IHD).
Main Methods:
- A retrospective study involving 1337 subjects (568 NORM, 151 DCM, 177 HCM, 441 IHD).
- Bi-ventricular morphological, functional, and left ventricular strain features were automatically extracted from cine MRI.
- Variational autoencoder models were trained and evaluated using tenfold cross-validation.
Main Results:
- The DL algorithm achieved high Area Under the Curve (AUC) values: 0.952 (NORM), 0.881 (DCM), 0.908 (HCM), and 0.856 (IHD), with an overall accuracy of 0.778.
- Specificity for the normal class was 0.908, increasing to 0.961 with cotraining using unlabeled data.
- Strain features were identified as important for disease classification.
Conclusions:
- Automatically extracted cardiac function features from cine MRI show promise for disease classification, particularly for normal-abnormal differentiation.
- Strain features play a significant role in accurate disease labeling.
- Cotraining with unlabeled data can enhance the specificity of normal-abnormal classification.
Background:
Automated approaches may allow for fast, reproducible clinical assessment of cardiovascular diseases from MRI.
Purpose:
To develop an MRI-based deep learning (DL) disease classification algorithm to distinguish among normal subjects (NORM), patients with dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), and ischemic heart disease (IHD).
Study Type:
Retrospective.
Population:
A total of 1337 subjects (55% female), comprising normal subjects (N = 568), and patients with DCM (N = 151), HCM (N = 177), and IHD (N = 441).
Field Strength/Sequence:
Balanced steady-state free precession cine sequence at 1.5/3.0 T.
Assessment:
Bi-ventricular morphological and functional features and global and segmental left ventricular strain features were automatically extracted from short- and long-axis cine images. Variational autoencoder models were trained on the extracted features and compared against consensus disease label provided by two expert readers (13 and 14 years of experience). Adding unlabeled, normal data to the training was explored to increase specificity of NORM class.
Statistical Tests:
Tenfold cross-validation for model development; mean, standard deviation (SD) for measurements; classification metrics: area under the curve (AUC), confusion matrix, accuracy, specificity, precision, recall; 95% confidence intervals; Mann-Whitney U test for significance.
Results:
AUCs of 0.952 for NORM, 0.881 for DCM, 0.908 for HCM, and 0.856 for IHD and overall accuracy of 0.778 were obtained, with specificity of 0.908 for the NORM class using both SAX and LAX features. Longitudinal strain features slightly improved classification metrics by 0.001 to 0.03 points, except for HCM-AUC. Differences in accuracy, metrics for NORM class and HCM-AUC were statistically significant. Cotraining using unlabeled data increased the specificity for the NORM class to 0.961.
Data Conclusion:
Cardiac function features automatically extracted from cine MRI have potential to be used for disease classification, especially for normal-abnormal classification. Feature analyses showed that strain features were important for disease labeling. Cotraining using unlabeled data may help to increase specificity for normal-abnormal classification.
Level Of Evidence:
3 TECHNICAL EFFICACY: Stage 1.
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