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

Vafaeezadeh, Majid; Behnam, Hamid; Hosseinsabet, Ali; Gifani, Parisa

doi:10.1007/s11548-021-02542-7

Cited by 7 publications

(15 citation statements)

References 19 publications

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“…Grad-CAM was used for visualizations and identified RoI for predicting the systemic phenotypes which are otherwise considered difficult for the human experts [245]. Transthoracic Echocardiography images were used for an automatic Carpentier's functional classification of mitral valve diseases with eight deep CNN models [246]. In visualization with Grad-CAM, ResNeXt50 provided the best explainable results highlighting the RoI [246].…”

Section: Clinical/observational Datamentioning

confidence: 99%

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Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks

Nazir¹,

Dickson²,

Akram³

2023

Computers in Biology and Medicine

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Section: Clinical/observational Datamentioning

confidence: 99%

“…Transthoracic Echocardiography images were used for an automatic Carpentier's functional classification of mitral valve diseases with eight deep CNN models [246]. In visualization with Grad-CAM, ResNeXt50 provided the best explainable results highlighting the RoI [246].…”

Section: Clinical/observational Datamentioning

confidence: 99%

Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks

Nazir¹,

Dickson²,

Akram³

2023

Computers in Biology and Medicine

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“…For instance, Pérez-Pelegrıó et al 28 developed a new explainable approach that combines class activation mapping with U-net to automatically estimate the LV volume in end diastole and obtain the result in the form of a segmentation mask without segmentation labels to train the algorithm. Grad-CAM was used in 7 cardiac imaging studies, either for classification 18,34,40,[48][49][50] or segmentation. 51 The latter in particular proposed a new interpretable CNN model (fast and accurate echocardiographic automatic segmentation based on U-Net) that integrates U-net architecture and transfer learning (from Visual Geometry Group 19) to segment 2-dimensional echocardiography of 88 patients into 3 regions (LV, interventricular septal, and posterior LV wall).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Grad-CAM was used in 7 cardiac imaging studies, either for classification 18,34,40,48–50 or segmentation. 51 The latter in particular proposed a new interpretable CNN model (fast and accurate echocardiographic automatic segmentation based on U-Net) that integrates U-net architecture and transfer learning (from Visual Geometry Group 19) to segment 2-dimensional echocardiography of 88 patients into 3 regions (LV, interventricular septal, and posterior LV wall).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The aims of these articles were diverse and included the automatic calculation of the LV volume and ejection fraction 28,35 ; the automatic segmentation of cardiac structures as LV/RV, myocardium, interventricular septal, and posterior LV wall 39,51,55 ; and classification approaches. Classification was investigated in the context of cardiac disorders such as mitral valve diseases, 48 myocardial injury, 50 coronary artery disease (CAD), 34,54 cardiomyopathy, 33 congenital heart disease, 40 and HF 32 ; even in newborn 49 for the presence of intracardiac devices (eg, catheters, pacemaker, and defibrillator leads) or motion 18 ; for severe left atrial dilation and LV hypertrophy 43 ; and for CMR image view. 27 In order to improve the interpretability of the segmentation/prediction/classification results, these articles generally focused on DL-based XAI methods as class activation mapping or Grad-CAM, SmoothGrad, saliency maps, testing with concept activation vectors, and guided backpropagation.…”

Section: Literature Reviewmentioning

confidence: 99%

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Explainable Artificial Intelligence and Cardiac Imaging: Toward More Interpretable Models

Salih

Galazzo

Gkontra

et al. 2023

Circ: Cardiovascular Imaging

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Artificial intelligence applications have shown success in different medical and health care domains, and cardiac imaging is no exception. However, some machine learning models, especially deep learning, are considered black box as they do not provide an explanation or rationale for model outcomes. Complexity and vagueness in these models necessitate a transition to explainable artificial intelligence (XAI) methods to ensure that model results are both transparent and understandable to end users. In cardiac imaging studies, there are a limited number of papers that use XAI methodologies. This article provides a comprehensive literature review of state-of-the-art works using XAI methods for cardiac imaging. Moreover, it provides simple and comprehensive guidelines on XAI. Finally, open issues and directions for XAI in cardiac imaging are discussed.

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CarpNet: Transformer for mitral valve disease classification in echocardiographic videos

Vafaeezadeh

Behnam

Hosseinsabet

et al. 2023

Int J Imaging Syst Tech

Self Cite

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Mitral valve (MV) diseases constitute one of the etiologies of cardiovascular mortality and morbidity. MV pathologies need evaluating and classifying via echocardiographic videos. Transformers have significantly advanced video analytics. MV motion is divided by Carpentier functional classification into four types: normal, increased, restricted, and restricted only during systole. This paper introduces CarpNet, a deep transformer network that incorporates video transformers capable of direct MV pathology Carpentier's classification from the parasternal long‐axis (PLA) echocardiographic videos. The network, instead of processing frames independently, analyzes stacks of temporally consecutive frames using multi‐head attention modules to incorporate MV temporal dynamics into the learned model. To that end, different convolutional neural networks (CNNs) are evaluated as the backbone, and the best model is selected using the information of the PLA view. The use of information obtained by our proposed deep transformer network from consecutive echocardiographic frames yielded better results concerning the Carpentier functional classification than information obtained by CNN‐based (single‐frame) models. Using the Inception_Resnet_V2 architecture as the backbone, CarpNet achieved 71% accuracy in the test dataset. Deep learning and transformers in echocardiographic videos can render quick, precise, and stable evaluations of various MV pathologies.

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

Cited by 7 publications

References 19 publications

Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks

Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks

Explainable Artificial Intelligence and Cardiac Imaging: Toward More Interpretable Models

CarpNet: Transformer for mitral valve disease classification in echocardiographic videos

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