Non-hyperaemic assessment of coronary ischaemia: application of machine learning techniques.

ABSTRACT:

Aims

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.

Methods and results

Seventy-seven coronary vessel lesions (39 FFR defined ischaemia, 53 patients) with proximal and distal non-hyperaemic pressure waveforms and FFR values were assessed using supervised and unsupervised learning techniques

SUBMITTER: Cameron JN 

PROVIDER: S-EPMC9779890 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature