URL: https://www.clinicaltrials.gov. Unique identifier: NCT03139968.

Purpose of Review Artificial intelligence (AI) applications in (interventional) cardiology continue to emerge. This review summarizes the current state and future perspectives of AI for automated imaging analysis in invasive coronary angiography (ICA). Recent Findings Recently, 12 studies on AI for automated imaging analysis In ICA have been published. In these studies, machine learning (ML) models have been developed for frame selection, segmentation, lesion assessment, and functional assessment of coronary flow. These ML models have been developed on monocenter datasets (in range 31–14,509 patients) and showed moderate to good performance. However, only three ML models were externally validated. Summary Given the current pace of AI developments for the analysis of ICA, less-invasive, objective, and automated diagnosis of CAD can be expected in the near future. Further research on this technology in the catheterization laboratory may assist and improve treatment allocation, risk stratification, and cath lab logistics by integrating ICA analysis with other clinical characteristics.

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Background Interventional cardiologists are inevitably exposed to low-dose radiation, and consequently are at risk for radiation induced diseases like cataract and left-sided brain tumours. Operator behaviour may possibly be the largest influencer on radiation exposure. We hypothesised that awareness regarding radiation exposure grows as skill and the general experience in the catheterization laboratory increase. Objectives In this study we determined the difference in the relative radiation exposure of staff interventional cardiologists compared with cardiology fellows-in-training. Methods During this prospective trial the operator’s radiation exposure (E in µSv) was measured at chest height during 766 diagnostic catheterisations and percutaneous coronary interventions. Also, the patient exposure (DAP in mGy·cm 2 ), representing the amount of radiation administered by the operator per procedure, was collected. The primary outcome of this study was the difference in relative exposure between staff interventional cardiologists versus cardiology fellows-in-training (E/DAP). Results From January to May 2017, staff interventional cardiologists performed 637 procedures and cardiology fellows-in-training 129 procedures. The performance of relatively complex procedures by staff interventional cardiologists resulted in a 74% higher use of radiation compared with fellows-in-training. Consequently, staff interventional cardiologists were exposed to 50% higher levels of actual radiation exposure. However, when correcting for the complexity of the procedure, by comparing the relative operator exposure (E/DAP), fellows-in-training were exposed to a 34% higher relative exposure compared with staff interventional cardiologists ( p = 0.025). Conclusions In the current study, when corrected for complexity, cardiology fellows-in-training were exposed to significantly higher radiation levels than staff interventional cardiologists during catheterisation procedures.

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