Automated Pattern Recognition in Whole-Cardiac Cycle Echocardiographic Data: Capturing Functional Phenotypes with Machine Learning

Lončarić, Filip; Castellote, Pablo-Miki Marti; Sanchez-Martinez, Sergio; Fabijanović, Dora; Nunno, L; Mimbrero, M; Sanchís, Laura; Doltra, Adelina; Montserrat, Sílvia; Čikeš, Maja; Crispi, F.; Piella, Gema; Sitges, Marta; Bijnens, Bart

doi:10.1016/j.echo.2021.06.014

Cited by 14 publications

(11 citation statements)

References 37 publications

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“…Lastly, a ML ensemble model combined clinical data, quantitative stenosis, and plaque metrics from CT angiography to effectively detect lesion-specific ischemia (21). Another data-driven example of status interpretation is unsupervised machine learning for dimensionality reduction; a label-agnostic approach that orders individuals according to their similarity, i.e., those with a similar clinical presentation are grouped together, whereas those showing distinct pathophysiological features are positioned far apart (22). This allows identifying different levels of abnormality, or assessing the effect of therapies and interventions, as these are aimed to restore an individual toward increased "normality."…”

Section: Status Interpretation-comparison To Populationmentioning

confidence: 99%

“…Another data-driven example of status interpretation is unsupervised machine learning for dimensionality reduction; a label-agnostic approach that orders individuals according to their similarity, i.e., those with a similar clinical presentation are grouped together, whereas those showing distinct pathophysiological features are positioned far apart ( 22 ). This allows identifying different levels of abnormality, or assessing the effect of therapies and interventions, as these are aimed to restore an individual toward increased “normality.” An implementation of unsupervised dimensionality reduction provided useful insight into treatment response in large patient populations ( 23 ), and quantified patient changes after an intervention using temporally dynamic data ( 24 ).…”

Section: Status Interpretation—comparison To Populationmentioning

confidence: 99%

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Machine Learning for Clinical Decision-Making: Challenges and Opportunities in Cardiovascular Imaging

Sanchez-Martinez

Cámara

Piella

et al. 2022

Front. Cardiovasc. Med.

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The use of machine learning (ML) approaches to target clinical problems is called to revolutionize clinical decision-making in cardiology. The success of these tools is dependent on the understanding of the intrinsic processes being used during the conventional pathway by which clinicians make decisions. In a parallelism with this pathway, ML can have an impact at four levels: for data acquisition, predominantly by extracting standardized, high-quality information with the smallest possible learning curve; for feature extraction, by discharging healthcare practitioners from performing tedious measurements on raw data; for interpretation, by digesting complex, heterogeneous data in order to augment the understanding of the patient status; and for decision support, by leveraging the previous steps to predict clinical outcomes, response to treatment or to recommend a specific intervention. This paper discusses the state-of-the-art, as well as the current clinical status and challenges associated with the two later tasks of interpretation and decision support, together with the challenges related to the learning process, the auditability/traceability, the system infrastructure and the integration within clinical processes in cardiovascular imaging.

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Section: Status Interpretation-comparison To Populationmentioning

confidence: 99%

Section: Status Interpretation—comparison To Populationmentioning

confidence: 99%

Machine Learning for Clinical Decision-Making: Challenges and Opportunities in Cardiovascular Imaging

Sanchez-Martinez

Cámara

Piella

et al. 2022

Front. Cardiovasc. Med.

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“…Apart from directly using GLS data, researchers also explored disease phenotypes based on strain curves. Loncaric F. et al [ 37 ] conducted a study including 189 patients with hypertension and 97 controls. Based on the strain curve and pulse Doppler velocity curve of mitral and aortic valves, an unsupervised ML algorithm was developed to automatically identify the patterns in the strain and velocity curves throughout cardiac cycles.…”

Section: Ai’s Application In Left Ventricular Systolic Function—glsmentioning

confidence: 99%

Artificial Intelligence-Enhanced Echocardiography for Systolic Function Assessment

Zhang

Zhu

Liu

et al. 2022

JCM

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The accurate assessment of left ventricular systolic function is crucial in the diagnosis and treatment of cardiovascular diseases. Left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS) are the most critical indexes of cardiac systolic function. Echocardiography has become the mainstay of cardiac imaging for measuring LVEF and GLS because it is non-invasive, radiation-free, and allows for bedside operation and real-time processing. However, the human assessment of cardiac function depends on the sonographer’s experience, and despite their years of training, inter-observer variability exists. In addition, GLS requires post-processing, which is time consuming and shows variability across different devices. Researchers have turned to artificial intelligence (AI) to address these challenges. The powerful learning capabilities of AI enable feature extraction, which helps to achieve accurate identification of cardiac structures and reliable estimation of the ventricular volume and myocardial motion. Hence, the automatic output of systolic function indexes can be achieved based on echocardiographic images. This review attempts to thoroughly explain the latest progress of AI in assessing left ventricular systolic function and differential diagnosis of heart diseases by echocardiography and discusses the challenges and promises of this new field.

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“…Finally, ML may identify functional phenotypes from whole-cardiac cycle echocardiography. In particular, Loncaric et al used unsupervised learning trained on a dataset of 189 patients with known hypertension and 97 healthy controls and found that their software could automatically identify patterns in velocity and deformation which correlate with specific structural and functional remodeling [28]. Similarly, AI has been used to analyze diastolic parameters correlating with specific phenotypes, thus leading to a more personalized patient management [29].…”

Section: Echocardiographymentioning

confidence: 99%

Radiomics in Cardiovascular Disease Imaging: from Pixels to the Heart of the Problem

Spadarella

Perillo

Ugga

et al. 2022

Curr Cardiovasc Imaging Rep

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Purpose of Review This review of the literature aims to present potential applications of radiomics in cardiovascular radiology and, in particular, in cardiac imaging. Recent Findings Radiomics and machine learning represent a technological innovation which may be used to extract and analyze quantitative features from medical images. They aid in detecting hidden pattern in medical data, possibly leading to new insights in pathophysiology of different medical conditions. In the recent literature, radiomics and machine learning have been investigated for numerous potential applications in cardiovascular imaging. They have been proposed to improve image acquisition and reconstruction, for anatomical structure automated segmentation or automated characterization of cardiologic diseases. Summary The number of applications for radiomics and machine learning is continuing to rise, even though methodological and implementation issues still limit their use in daily practice. In the long term, they may have a positive impact in patient management.

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Automated Pattern Recognition in Whole-Cardiac Cycle Echocardiographic Data: Capturing Functional Phenotypes with Machine Learning

Cited by 14 publications

References 37 publications

Machine Learning for Clinical Decision-Making: Challenges and Opportunities in Cardiovascular Imaging

Machine Learning for Clinical Decision-Making: Challenges and Opportunities in Cardiovascular Imaging

Artificial Intelligence-Enhanced Echocardiography for Systolic Function Assessment

Radiomics in Cardiovascular Disease Imaging: from Pixels to the Heart of the Problem

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