Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography

Narula, Sukrit; Shameer, Khader; Omar, Alaa Mabrouk Salem; Dudley, Joel T.; Sengupta, Partho P.

doi:10.1016/j.jacc.2016.08.062

Cited by 328 publications

(220 citation statements)

References 28 publications

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“…These data are generally used to develop a model to predict or classify future events, or to find which variables are most relevant to the outcome. Examples of supervised learning algorithms include ordinary least squares regression,18 logistic regression,19 least absolute shrinkage and selection operator (LASSO) regression,20 ridge regression,21 elastic net regression,21 linear discriminant analysis,22 Naïve Bayes classifiers,9 support vector machines,23 Bayesian networks,24 a variety of decision trees25 especially Random Forests26 and AdaBoost or gradient boosting classifiers,27 artificial neural networks and ensemble methods 7. Some of the examples of supervised machine learning tasks include regression, classification, predictive modelling and survival analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Supervised machine learning can help circumvent classification problems in phenotypically difficult patients (figure 3). For example, we recently described the use of supervised learning for differentiating athlete’s heart and hypertrophic cardiomyopathy 7. In another similar example, we developed a cognitive machine learning-based classifier to distinguish between constrictive pericarditis and restrictive cardiomyopathy 8…”

Section: Introductionmentioning

confidence: 99%

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Machine learning in cardiovascular medicine: are we there yet?

Shameer

Johnson

Glicksberg

et al. 2018

Heart

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389

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Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing several terms like machine learning, cognitive learning, deep learning and reinforcement learning-based methods that can be used to integrate and interpret complex biomedical and healthcare data in scenarios where traditional statistical methods may not be able to perform. In this review article, we discuss the basics of machine learning algorithms and what potential data sources exist; evaluate the need for machine learning; and examine the potential limitations and challenges of implementing machine in the context of cardiovascular medicine. The most promising avenues for AI in medicine are the development of automated risk prediction algorithms which can be used to guide clinical care; use of unsupervised learning techniques to more precisely phenotype complex disease; and the implementation of reinforcement learning algorithms to intelligently augment healthcare providers. The utility of a machine learning-based predictive model will depend on factors including data heterogeneity, data depth, data breadth, nature of modelling task, choice of machine learning and feature selection algorithms, and orthogonal evidence. A critical understanding of the strength and limitations of various methods and tasks amenable to machine learning is vital. By leveraging the growing corpus of big data in medicine, we detail pathways by which machine learning may facilitate optimal development of patient-specific models for improving diagnoses, intervention and outcome in cardiovascular medicine.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine learning in cardiovascular medicine: are we there yet?

Shameer

Johnson

Glicksberg

et al. 2018

Heart

Self Cite

389

229

View full text Add to dashboard Cite

show abstract

“…Artificial algorithms based on deep learning architectures can be used for automatic segmentation of the ventricle walls [94,100–102] or the detection of the bounding box containing heart valves in 2D echocardiography [103]. Recently published work shows promising results of utilizing speckle-tracking data and ML for automated discrimination of hypertrophic cardiomyopathy from physiological hypertrophy seen in athletes [104]. These encouraging results are steps toward a general artificial intelligence model of 2D echocardiographic data interpretation.…”

Section: Machine Learningmentioning

confidence: 99%

Cardiac imaging: working towards fully-automated machine analysis & interpretation

Slomka

Dey

Sitek

et al. 2017

Expert Review of Medical Devices

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Introduction Non-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation.

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“…During the process, the machine system continues to learn, adjust, and adapt from corrections made by a human observer. [56] This is a form of supervised machine learning technique. The ability of AI to learn from image recognition and interpretation has resulted in the development of automated electroencephalogram, electrocardiography analyses, and facial recognition technology.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence in mitral valve analysis

et al. 2017

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Background:Echocardiographic analysis of mitral valve (MV) has become essential for diagnosis and management of patients with MV disease. Currently, the various software used for MV analysis require manual input and are prone to interobserver variability in the measurements.Aim:The aim of this study is to determine the interobserver variability in an automated software that uses artificial intelligence for MV analysis.Settings and Design:Retrospective analysis of intraoperative three-dimensional transesophageal echocardiography data acquired from four patients with normal MV undergoing coronary artery bypass graft surgery in a tertiary hospital.Materials and Methods:Echocardiographic data were analyzed using the eSie Valve Software (Siemens Healthcare, Mountain View, CA, USA). Three examiners analyzed three end-systolic (ES) frames from each of the four patients. A total of 36 ES frames were analyzed and included in the study.Statistical Analysis:A multiple mixed-effects ANOVA model was constructed to determine if the examiner, the patient, and the loop had a significant effect on the average value of each parameter. A Bonferroni correction was used to correct for multiple comparisons, and P = 0.0083 was considered to be significant.Results:Examiners did not have an effect on any of the six parameters tested. Patient and loop had an effect on the average parameter value for each of the six parameters as expected (P < 0.0083 for both).Conclusion:We were able to conclude that using automated analysis, it is possible to obtain results with good reproducibility, which only requires minimal user intervention.

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Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography

Cited by 328 publications

References 28 publications

Machine learning in cardiovascular medicine: are we there yet?

Machine learning in cardiovascular medicine: are we there yet?

Cardiac imaging: working towards fully-automated machine analysis & interpretation

Artificial intelligence in mitral valve analysis

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