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Image-Based Cardiac Diagnosis With Machine Learning: A Review


Martin-Isla, Carlos; Campello, Victor M; Izquierdo, Cristian; Raisi-Estabragh, Zahra; Baeßler, Bettina; Petersen, Steffen E; Lekadir, Karim (2020). Image-Based Cardiac Diagnosis With Machine Learning: A Review. Frontiers in cardiovascular medicine, 7:epub ahead.

Abstract

Cardiac imaging plays an important role in the diagnosis of cardiovascular disease (CVD). Until now, its role has been limited to visual and quantitative assessment of cardiac structure and function. However, with the advent of big data and machine learning, new opportunities are emerging to build artificial intelligence tools that will directly assist the clinician in the diagnosis of CVDs. This paper presents a thorough review of recent works in this field and provide the reader with a detailed presentation of the machine learning methods that can be further exploited to enable more automated, precise and early diagnosis of most CVDs.

Abstract

Cardiac imaging plays an important role in the diagnosis of cardiovascular disease (CVD). Until now, its role has been limited to visual and quantitative assessment of cardiac structure and function. However, with the advent of big data and machine learning, new opportunities are emerging to build artificial intelligence tools that will directly assist the clinician in the diagnosis of CVDs. This paper presents a thorough review of recent works in this field and provide the reader with a detailed presentation of the machine learning methods that can be further exploited to enable more automated, precise and early diagnosis of most CVDs.

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Additional indexing

Item Type:Journal Article, refereed, further contribution
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Clinic for Diagnostic and Interventional Radiology
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Health Sciences > Cardiology and Cardiovascular Medicine
Language:English
Date:24 January 2020
Deposited On:16 Dec 2020 09:24
Last Modified:01 Jan 2021 21:06
Publisher:Frontiers Research Foundation
ISSN:2297-055X
OA Status:Gold
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.3389/fcvm.2020.00001
PubMed ID:32039241

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