Non-Hyperaemic Assessment of Coronary Ischaemia – Application of Machine Learning Techniques

James N Cameron and Andrea Comella contributed equally to this work. 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. FFR measurements were obtained from the right coronary artery (13), left anterior descending (46), left circumflex (11), left main (1), obtuse marginal (2) and diagonal (4).