Automated Echocardiographic Detection of Severe Coronary Artery Disease Using Artificial Intelligence

Upton, Ross; Mumith, Angela; Beqiri, Arian; Parker, Andrew R.; Hawkes, William; Gao, Side; Porumb, Mihaela; Sarwar, Rizwan; Marques, Patrícia Angélica Alves; Markham, D C; Kenworthy, Jared B.; O’Driscoll, Jamie M; Hassanali, Neelam; Groves, Katherine; Dockerill, Cameron; Woodward, William E.; Alshargi, Mishari; McCourt, Annabelle; Wilkes, Edmund H.; Heitner, Stephen B.; Yadava, Mrinal; Stojanovski, David; Lamata, Pablo; Woodward, Gary; Leeson, Paul

doi:10.1016/j.jcmg.2021.10.013

Cited by 64 publications

(57 citation statements)

References 39 publications

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“…Approximately, half of the included studies were conducted in years 2020 (12/46, 26.0%) ( 14 , 15 , 17 , 19 , 20 , 25 , 26 , 30 , 36 , 37 , 45 , 56 ) and 2021 (12/46, 26.0%) ( 13 , 16 , 21 , 24 , 29 , 33 , 39 , 40 , 43 , 46 , 50 , 53 ) ( Figure 2 ). The corresponding authors of the included studies were from 13 countries, including the United States (14/46, 30.3%) ( 12 , 16 , 21 , 22 , 29 , 31 , 34 , 36 , 39 , 41 , 42 , 48 , 49 , 53 ), China (11/46, 23.9%) ( 15 , 19 , 20 , 35 , 37 , 38 , 40 , 45 , 46 , 52 , 54 ), Japan (8/46, 17.3%) ( 17 , 23 , 25 , 27 , 28 , 32 , 55 , 56 ), Greece (2/46, 4.3%) ( 13 , 14 ), and Netherlands (2/46, 4.3%) ( 44 , 47 ), whereas Italy ( 26 ), Canada ( 11 ), India ( 30 ), Ko...…”

Section: Resultsmentioning

confidence: 99%

“…For data sources, private data (data collected by centers) (35/46, 76.0%) ( 11 – 21 , 23 , 24 , 27 , 33 – 50 , 53 , 54 , 56 ) were the most commonly used data sources for the development of AI models. Except for one article for which the data source is unclear ( 28 ), public data (10/46, 21.7%) were the other sources of data for AI models ( 22 , 25 , 26 , 29 – 32 , 51 , 52 , 55 ).…”

Section: Resultsmentioning

confidence: 99%

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Using artificial intelligence in the development of diagnostic models of coronary artery disease with imaging markers: A scoping review

Wang

Wang²,

Wang³

et al. 2022

Front. Cardiovasc. Med.

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BackgroundCoronary artery disease (CAD) is a progressive disease of the blood vessels supplying the heart, which leads to coronary artery stenosis or obstruction and is life-threatening. Early diagnosis of CAD is essential for timely intervention. Imaging tests are widely used in diagnosing CAD, and artificial intelligence (AI) technology is used to shed light on the development of new imaging diagnostic markers.ObjectiveWe aim to investigate and summarize how AI algorithms are used in the development of diagnostic models of CAD with imaging markers.MethodsThis scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guideline. Eligible articles were searched in PubMed and Embase. Based on the predefined included criteria, articles on coronary heart disease were selected for this scoping review. Data extraction was independently conducted by two reviewers, and a narrative synthesis approach was used in the analysis.ResultsA total of 46 articles were included in the scoping review. The most common types of imaging methods complemented by AI included single-photon emission computed tomography (15/46, 32.6%) and coronary computed tomography angiography (15/46, 32.6%). Deep learning (DL) (41/46, 89.2%) algorithms were used more often than machine learning algorithms (5/46, 10.8%). The models yielded good model performance in terms of accuracy, sensitivity, specificity, and AUC. However, most of the primary studies used a relatively small sample (n < 500) in model development, and only few studies (4/46, 8.7%) carried out external validation of the AI model.ConclusionAs non-invasive diagnostic methods, imaging markers integrated with AI have exhibited considerable potential in the diagnosis of CAD. External validation of model performance and evaluation of clinical use aid in the confirmation of the added value of markers in practice.Systematic review registration[https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022306638], identifier [CRD42022306638].

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Using artificial intelligence in the development of diagnostic models of coronary artery disease with imaging markers: A scoping review

Wang

Wang²,

Wang³

et al. 2022

Front. Cardiovasc. Med.

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“…Furthermore, evolving technology of the portable machines would allow automated calculation of ventricular size and function using artificial intelligence to enable more objective and accurate assessments. 35…”

Section: Discussionmentioning

confidence: 99%

Focused Cardiac Ultrasound to Guide the Diagnosis of Heart Failure in Pregnant Women in India

Alsharqi¹,

Ismavel²,

Arnold³

et al. 2022

Journal of the American Society of Echocardiography

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“…Recently, Upton et al [50] undertook a multi-centre, multi-vendor trial that used a CNN to develop a model that can identify patients with angiographically confirmed prognostic coronary artery disease on stress echocardiograms. The model was then tested on a dataset of 154 stress echocardiograms, showing a specificity of 92.7% and a sensitivity of 84.4% [50]. The authors then put it into practice as an "AI assistant" to be used by clinicians reporting the studies, and it was found to increase the sensitivity for disease detection by 10%, achieving an area under the curve of 0.93 [50].…”

Section: Stress Echocardiographymentioning

confidence: 99%

Artificial Intelligence in Echocardiography: The Time is Now

Sehly¹,

Jaltotage²,

He³

et al. 2022

Rev. Cardiovasc. Med.

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Artificial Intelligence (AI) has impacted every aspect of clinical medicine, and is predicted to revolutionise diagnosis, treatment and patient care. Through novel machine learning (ML) and deep learning (DL) techniques, AI has made significant grounds in cardiology and cardiac investigations, including echocardiography. Echocardiography is a ubiquitous tool that remains first-line for the evaluation of many cardiovascular diseases, with large data sets, objective parameters, widespread availability and an excellent safety profile, it represents the perfect candidate for AI advancement. As such, AI has firmly made its stamp on echocardiography, showing great promise in training, image acquisition, interpretation and analysis, diagnostics, prognostication and phenotype development. However, there remain significant barriers in real-world clinical application and uptake of AI derived algorithms in echocardiography, most importantly being the lack of clinical outcome studies. While AI has been shown to match or even best its human counterparts, an improvement in real world outcomes remains to be established. There are also legal and ethical concerns that hinder its progress. Large outcome focused trials and a collaborative multi-disciplinary effort will be necessary to push AI into the clinical workspace. Despite this, current and emerging trials suggest that these systems will undoubtedly transform echocardiography, improving clinical utility, efficiency and training.

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Automated Echocardiographic Detection of Severe Coronary Artery Disease Using Artificial Intelligence

Cited by 64 publications

References 39 publications

Using artificial intelligence in the development of diagnostic models of coronary artery disease with imaging markers: A scoping review

Using artificial intelligence in the development of diagnostic models of coronary artery disease with imaging markers: A scoping review

Focused Cardiac Ultrasound to Guide the Diagnosis of Heart Failure in Pregnant Women in India

Artificial Intelligence in Echocardiography: The Time is Now

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