Automatic morphological classification of mitral valve diseases in echocardiographic images based on explainable deep learning methods

Majid Vafaeezadeh; Hamid Behnam; Ali Hosseinsabet; Parisa Gifani

doi:10.1007/s11548-021-02542-7

Automatic morphological classification of mitral valve diseases in echocardiographic images based on explainable deep learning methods

Int J Comput Assist Radiol Surg. 2022 Feb;17(2):413-425. doi: 10.1007/s11548-021-02542-7. Epub 2021 Dec 12.

Authors

Majid Vafaeezadeh¹, Hamid Behnam², Ali Hosseinsabet³, Parisa Gifani⁴

Affiliations

¹ Biomedical Engineering Department, Iran University of Science and Technology, Tehran, Iran.
² Biomedical Engineering Department, Iran University of Science and Technology, Tehran, Iran. behnam@iust.ac.ir.
³ Cardiology Department, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran.
⁴ Medical Sciences and Technologies Department, Science and Research Branch, Islamic Azad University, Tehran, Iran.

PMID: 34897594
DOI: 10.1007/s11548-021-02542-7

Abstract

Purpose: Carpentier's functional classification is a guide to explain the types of mitral valve regurgitation based on morphological features. There are four types of pathological morphologies, regardless of the presence or absence of mitral regurgitation: Type I, normal; Type II, mitral valve prolapse; Type IIIa, mitral valve stenosis; and Type IIIb, restricted mitral leaflet motion. The aim of this study was to automatically classify mitral valves using echocardiographic images.

Methods: In our procedure, after the classification of apical 4-chamber (A4C) and parasternal long-axis (PLA) views, we extracted the systolic/diastolic phase of the cardiac cycle by calculating the left ventricular area. Six typical pre-trained models were fine-tuned with a 4-class model for the PLA and a 3-class model for the A4C views. As an additional contribution, to provide explainability, we applied the Gradient-weighted Class Activation Mapping (Grad-CAM) algorithm to visualize areas of echocardiographic images where the different models generated a prediction.

Results: This approach conferred a proper understanding of where various networks "look" into echocardiographic images to predict the four types of pathological mitral valve morphologies. Considering the accuracy metric and Grad-CAM maps and by applying the Inception-ResNet-v2 architecture to classify Type II in the PLA view and ResNeXt50 architecture to classify the other three classes in the A4C view, we achieved an 80% rate of model accuracy in the test data set.

Conclusions: We suggest an explainable, fully automated, and rule-based procedure to classify the four types of mitral valve morphologies based on Carpentier's functional classification using deep learning on transthoracic echocardiographic images. Our study results infer the feasibility of the use of deep learning models to prepare quick and precise assessments of mitral valve morphologies in echocardiograms. According to our knowledge, our study is the first one that provides a public data set regarding the Carpentier classification of MV pathologies.

Keywords: Carpentier classification; DCNN; Echocardiography; Mitral valve morphology.

MeSH terms

Deep Learning*
Echocardiography
Humans
Mitral Valve / diagnostic imaging
Mitral Valve Insufficiency* / diagnostic imaging
Mitral Valve Prolapse* / diagnostic imaging