Overview of Pipeline for Image-Based Machine Learning Diagnosis We also present a comprehensive review of existing literature pertaining to applications of ML for image-based diagnosis of CVD.
In this paper we describe the main ML techniques and the procedures required to successfully design, implement, and validate new ML tools for image-based diagnosis. The superior diagnostic performance of AI image analysis has the potential to substantially alleviate the burden of cardiovascular disease through facilitation of faster and more accurate diagnostic decision making. Existing work already demonstrates the incremental value of image-based cardiovascular diagnosis with ML for a number of important conditions such as coronary artery disease (CAD) and heart failure (HF). Machine learning (ML) approaches to image-based diagnosis rely on algorithms/models that learn from past clinical examples through identification of hidden and complex imaging patterns. In recent years, the development of big data and availability of high computational power have driven exponential advancement of artificial intelligence (AI) technologies in medical imaging ( Figure 1). In order to optimize the diagnostic value 5 of cardiac imaging, there is need for more advanced image analysis techniques that allow deeper quantification of imaging phenotypes. Current image analysis techniques are mostly reliant on qualitative visual assessment of images and crude quantitative measures of cardiac structure and function. Cardiovascular imaging has a pivotal role in diagnostic decision making. Early and accurate diagnosis is key to improving CVD outcomes. 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.ĭespite significant advances in diagnosis and treatment, cardiovascular disease (CVD) remains the most common cause of morbidity and mortality worldwide, accounting for approximately one third of annual deaths ( 1, 2). 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. Until now, its role has been limited to visual and quantitative assessment of cardiac structure and function. 4Department of Diagnostic & Interventional Radiology, University Hospital Zurich, Zurich, SwitzerlandĬardiac imaging plays an important role in the diagnosis of cardiovascular disease (CVD).3William Harvey Research Institute, Queen Mary University of London, London, United Kingdom.
2Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom.
1Departament de Matemàtiques & Informàtica, Universitat de Barcelona, Barcelona, Spain.Campello 1, Cristian Izquierdo 1, Zahra Raisi-Estabragh 2,3, Bettina Baeßler 4, Steffen E.
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