A low-cost texture-based pipeline for predicting myocardial tissue remodeling and fibrosis using cardiac ultrasound

Kagiyama, Nobuyuki; Shrestha, Sirish; Cho, Jung Sun; Khalil, Mazhar; Singh, Yashbir; Challa, Abhiram; Casaclang‐Verzosa, Grace; Sengupta, Partho P.

doi:10.1016/j.ebiom.2020.102726

Cited by 42 publications

(39 citation statements)

References 34 publications

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“…It can also help distinguish between various pathological entities that appear similar to imaging. Furthermore, it can be used to detect certain phenotypes within these conditions ( 100 ).…”

Section: Discussionmentioning

confidence: 99%

The Role of Artificial Intelligence in Cardiovascular Imaging: State of the Art Review

Seetharam

Brito

Farjo

et al. 2020

Front. Cardiovasc. Med.

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In this current digital landscape, artificial intelligence (AI) has established itself as a powerful tool in the commercial industry and is an evolving technology in healthcare. Cutting-edge imaging modalities outputting multi-dimensional data are becoming increasingly complex. In this era of data explosion, the field of cardiovascular imaging is undergoing a paradigm shift toward machine learning (ML) driven platforms. These diverse algorithms can seamlessly analyze information and automate a range of tasks. In this review article, we explore the role of ML in the field of cardiovascular imaging.

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Section: Discussionmentioning

confidence: 99%

The Role of Artificial Intelligence in Cardiovascular Imaging: State of the Art Review

Seetharam

Brito

Farjo

et al. 2020

Front. Cardiovasc. Med.

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“…The application of radiomics and AI-based algorithms holds promise in echocardiographic interpretation of myocardial textural changes, which are often subtle, non-specific, and challenging to quantify. 40) 41) Radiomics is a process that converts digital medical images into mineable high-dimensional data, motivated by the concept that biomedical images contain information reflecting the underlying pathophysiology, and that relationships can be revealed via quantitative image analyses ( Figure 3 ). 42) Although radiomics was initiated in oncology using CT, MR, and positron emission tomography images, 43) 44) 45) it is potentially applicable to echocardiography for evaluation of cardiovascular disease.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence and Echocardiography

Cho

Kim

Chang

2021

J Cardiovasc Imaging

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Artificial intelligence (AI) is evolving in the field of diagnostic medical imaging, including echocardiography. Although the dynamic nature of echocardiography presents challenges beyond those of static images from X-ray, computed tomography, magnetic resonance, and radioisotope imaging, AI has influenced all steps of echocardiography, from image acquisition to automatic measurement and interpretation. Considering that echocardiography often is affected by inter-observer variability and shows a strong dependence on the level of experience, AI could be extremely advantageous in minimizing observer variation and providing reproducible measures, enabling accurate diagnosis. Currently, most reported AI applications in echocardiographic measurement have focused on improved image acquisition and automation of repetitive and tedious tasks; however, the role of AI applications should not be limited to conventional processes. Rather, AI could provide clinically important insights from subtle and non-specific data, such as changes in myocardial texture in patients with myocardial disease. Recent initiatives to develop large echocardiographic databases can facilitate development of AI applications. The ultimate goal of applying AI to echocardiography is automation of the entire process of echocardiogram analysis. Once automatic analysis becomes reliable, workflows in clinical echocardiographic will change radically. The human expert will remain the master controlling the overall diagnostic process, will not be replaced by AI, and will obtain significant support from AI systems to guide acquisition, perform measurements, and integrate and compare data on request.

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“…According to our results, different LVH groups did appear different myocardial texture features in TTE, and they could be identified by the computer. Previous researchers have reported the feasibility and usefulness of myocardial texture analysis based on CMR, cardiac CT and echocardiography in several myocardial abnormalities, such as myocardial infarction, myocarditis, cardiac amyloidosis and HCM (5)(6)(7)(8)(9)17). In this study, we explored TTE myocardial texture analysis in differentiating three specific LVH entities-HHD, HCM and UCM.…”

Section: Statistics Of Texture Featuresmentioning

confidence: 99%

“…Artificial intelligence (AI) has been developing rapidly in recent years. Previous researchers have reported the application of radiomics and AI-based algorithms from cardiac magnetic imaging (CMR), cardiac computed tomography (CT) and echocardiography data in several myocardial diseases (5)(6)(7)(8)(9). However, none has been devoted to multiple LVH etiology differentiation based on echocardiography.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence-based myocardial texture analysis in etiological differentiation of left ventricular hypertrophy

Huang

et al. 2021

Ann Transl Med

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Background: Transthoracic echocardiography (TTE) is widely used in clinics to evaluate left ventricular hypertrophy (LVH). However, TTE is usually insufficient for the etiological diagnoses when morphological and functional features are nonspecific. With the booming of computer science and artificial intelligence (AI), previous literature has reported the application of radiomics based on cardiac magnetic resonance imaging, cardiac computed tomography and TTE in diagnosing several myocardial abnormalities, such as myocardial infarction, myocarditis, cardiac amyloidosis, and hypertrophic cardiomyopathy (HCM). In this study, we explored the possibility of using myocardial texture features in differentiating HCM, hypertensive heart disease (HHD) and uremic cardiomyopathy (UCM) based on echocardiography. To our knowledge, this was the first study to explore TTE myocardial texture analysis for multiple LVH etiology differentiation.Methods: TTE images were reviewed retrospectively from January 2018 to collect 50 cases for each group of HHD, HCM and UCM. The apical four chamber view was retrieved. Seventeen first-order statistics and 60 gray level co-occurrence matrix (GLCM) features were extracted for statistics and classification test by support vector machine (SVM).Results: Of all the parameters, entropy of brightness (EtBrt), standard deviation (Std), coefficient of variation (CoV), skewness (Skew), contrast7 (Cont7) and homogeneity5 (Hm5) were found statistically significant among the three groups (all P<0.05) and with acceptable reproducibility (intraobserver and interobserver ICC >0.50). As a result, HCM showed the most homogeneous myocardial texture, and was significantly different from HHD and UCM (all six features: P≤0.005). HHD appeared slightly more homogeneous than UCM, as only EtBrt and CoV were significant (P=0.011 and P=0.008). According to higher areas under the receiver operating characteristic curve (AUC) (>0.50), EtBrt, Std, and CoV were selected for test of classification as a combination of features. The AUC derived from SVM model was slightly improved compared with those of EtBrt, Std and CoV individually.Conclusions: AI-based myocardial texture analysis using ultrasonic images may be a potential approach to aiding LVH etiology differentiation.

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A low-cost texture-based pipeline for predicting myocardial tissue remodeling and fibrosis using cardiac ultrasound

Cited by 42 publications

References 34 publications

The Role of Artificial Intelligence in Cardiovascular Imaging: State of the Art Review

The Role of Artificial Intelligence in Cardiovascular Imaging: State of the Art Review

Artificial Intelligence and Echocardiography

Artificial intelligence-based myocardial texture analysis in etiological differentiation of left ventricular hypertrophy

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