Can machine learned algorithms further illuminate intracoronary imaging in PCI and improve the human touch?

EuroIntervention 2021;17:18-19. DOI: 10.4244/EIJV17I1A3

Thomas  W. Johnson
Thomas W. Johnson1, BSc, MBBS, MD, FRCP, FESC; Peter D. O'Kane2, BSc, MBBS, MD, FRCP
1. Bristol Heart Institute, University Hospitals Bristol & Weston NHS Foundation Trust, Bristol, United Kingdom; 2. Dorset Heart Centre, University Hospitals Dorset NHS Foundation Trust, Bournemouth, United Kingdom
Intravascular optical coherence tomography (IVOCT) is a mature technology although its adoption remains limited in most regions. An international survey in 20181 observed that >50% of European respondents undertook intracoronary imaging (ICI) in <5% of patients, compared with >95% of Japanese respondents who reported imaging use in >15% of patients (the reality being that >90% of all Japanese procedures are image guided). This stark contrast may be based on reimbursement. Interestingly, however, increasing PCI experience was associated with an increased adoption of imaging. This observation is consistent with our own experience that greater adoption of ICI has highlighted significant challenges in ...

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