Artificial Intelligence and Cardiac Imaging: We need to talk about this

Abstract

With the rapid technological progress experienced by medical imaging in recent years, the conversion of digital images into high-dimensional data, that is, with a large number of variables, has been driven by the concept that images contain a myriad of underlying pathophysiological information that is often difficult identify and comprehend using conventional visual analysis.1 The quantitative analysis of these images and the organization of these parameters in complex databases (Big Data) — with large volume, variety and speed of information generation — brought radiology closer to the new technological frontiers, involving Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) (Figure 1). “Images are more than pictures, they are data.”1 The mantra of modern radiology portrays the potential of this new understanding of imaging in the new age of precision medicine, going far beyond diagnosis and having a decisive role in clinical decision making. In this new and complex context, Cardiology has been a broad and fertile ground for AI approaches, as many heterogeneous and sufficiently prevailing diseases (ideal for large databases), such as heart failure and coronary artery disease, are yet to be sub-phenotyped in the constant pursuit of increasingly customized treatments. Besides, problems with acquisition time, high costs, efficiency and misdiagnosis are commonly observed and thus expected to be mitigated with the promising new applications of AI in cardiovascular propaedeutics.2