The machine learning horizon in cardiac hybrid imaging View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-12

AUTHORS

Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Sergey V. Nesterov, Sami Kajander, Juhani Knuuti

ABSTRACT

Machine learning (ML) represents a family of algorithms that has rapidly developed within the last years in a wide variety of knowledge areas. ML is able to elucidate and grasp complex patterns from data in order to approach prediction and classification problems. The present narrative review summarizes fundamental notions in ML as well as the evidence of its application in standard cardiac imaging and the potential for implementation in cardiac hybrid imaging. ML, and in particular Deep Learning, has begun to revolutionize medical imaging though the optimization of diagnostic and prognostic estimations at the individual-patient level. On the other hand, the spread and availability of high quality non-invasive imaging has provided growing amounts of data in the characterization of suspected cardiovascular diseases. At the same time, modern combined imaging equipment has set the ground for the concept of hybrid imaging to develop. Cardiac hybrid imaging refers to the combination of diagnostic images and offers the possibility to comprehensively characterize the heart and great vessels when a pathology is suspected or clinically known. Analysis and integration of large amounts of cardiac hybrid imaging data (and corresponding clinical profiles) constitutes a highly complex process and ML will likely be able to enhance it in the near future. ML conveys novel and powerful approaches in the processing of large and complex datasets that may include images as well as imaging-derived data. Given the growing amount of data in the realm of cardiac hybrid imaging and the rapid development of ML, it is highly desirable to implement and test ML in the optimization of our multimodality imaging diagnostic and prognostic evaluations in cardiovascular disease. More... »

PAGES

15

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s41824-018-0033-3

DOI

http://dx.doi.org/10.1186/s41824-018-0033-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1104174602


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