Open AccessNon-hyperaemic assessment of coronary ischaemia: application of machine learning techniques
Author(s) -
James N Cameron,
A. Comella,
N. Sutherland,
Adam Brown,
Thanh G. Phan
Publication year - 2022
Publication title -
european heart journal. digital health
Language(s) - English
Resource type - Journals
ISSN - 2634-3916
DOI - 10.1093/ehjdh/ztac050
Subject(s) - medicine , fractional flow reserve , cardiology , aortic pressure , hyperaemia , circumflex , diastole , ischemia , blood pressure , coronary artery disease , gold standard (test) , artery , blood flow , coronary angiography , myocardial infarction
Hyperaemic and non-hyperaemic pressure ratios (NHPR) are routinely used to identify significant coronary lesions. Machine learning (ML) techniques may help better understand these indices and guide future practice. This study assessed the ability of a purpose-built ML algorithm to classify coronary ischaemia during non-hyperaemia compared with the existing gold-standard technique (fractional flow reserve, FFR). Further, it investigated whether ML could identify components of coronary and aortic pressure cycles indicative of ischaemia.