2022
DOI: 10.1007/s11886-022-01655-y
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Current State and Future Perspectives of Artificial Intelligence for Automated Coronary Angiography Imaging Analysis in Patients with Ischemic Heart Disease

Abstract: Purpose of Review Artificial intelligence (AI) applications in (interventional) cardiology continue to emerge. This review summarizes the current state and future perspectives of AI for automated imaging analysis in invasive coronary angiography (ICA). Recent Findings Recently, 12 studies on AI for automated imaging analysis In ICA have been published. In these studies, machine learning (ML) models have been developed for frame selection, segmentation, les… Show more

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Cited by 15 publications
(14 citation statements)
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“…For coronary artery disease (CAD), there is a large number of established ML algorithms for interpretation of CT scans and recently even approaches that aid decision-making and interpretation for invasive coronary angiography were proposed [ 54 ]. ML does not only aid in identifying patients with CAD but is also useful in prognosis prediction among these patients [ 2 ].…”
Section: Prognostic Value Of Machine Learning In Imagingmentioning
confidence: 99%
“…For coronary artery disease (CAD), there is a large number of established ML algorithms for interpretation of CT scans and recently even approaches that aid decision-making and interpretation for invasive coronary angiography were proposed [ 54 ]. ML does not only aid in identifying patients with CAD but is also useful in prognosis prediction among these patients [ 2 ].…”
Section: Prognostic Value Of Machine Learning In Imagingmentioning
confidence: 99%
“…Secondly, the absence of adequate knowledge of the use of AI in CAD patients, compounded with the absence of adequate training/knowledge regarding the basic process underlying this type of analysis may limit the diffusion of such techniques. Similarly, the absence of large datasets to train and validate AI modes may lead to poor performance of AI in uncommon diseases [32]. Conversely, it is also true that cardiovascular data needed for AI analysis are widely available in daily clinical practice (such as medical imaging, blood sample, and electronic health records); these aspects may facilitate the adoption of ML.…”
Section: Future Perspectivesmentioning
confidence: 99%
“…Machine learning (ML), an advanced digital solutions, has a tremendous potential impact on (interventional) cardiology (9,10). Lopes et al presented original research on outcome prediction in patients who undergo transcatheter aortic valve implantation (TAVI).…”
Section: Editorial On the Research Topic Digital Solutions In Cardiologymentioning
confidence: 99%