Coronary artery disease (CAD) is one of the leading causes of death and disability worldwide. In CAD, the coronary arteries, that supply the myocardium with oxygen, are narrowed or even blocked by a process called atherosclerosis. Invasive coronary angiography (ICA) is the gold standard for the diagnosis of CAD, as well as for intraprocedural guidance of percutaneous coronary interventions. At this moment, stenosis severity is determined via visual inspection by a cardiologist. This method has several important drawbacks: a significant inter- and intra-rater variability and a high positive prediction bias. Currently available additional assessment techniques improve results, however, they come with prolonged procedural time, complication risks and increased costs. Additionally, a relevant number of syndromes exists that cannot be diagnosed sufficiently with these techniques. Automated software that uses all information within the ICA images and relates it to the context of complaints and outcomes, could be a valuable tool for improvement. Since machine learning (ML) can find relations between patient groups based on images, this could be a possible solution. However, despite promising results, clinical implementation of ML is still limited. Limited clinical applicability of developed algorithms and a lack of large high-quality datasets are the cause of this. In this research it was investigated if ML can help in solving the diagnostic and therapeutic challenges encountered in interventional cardiology (IC) and what is needed to apply ML to ICA images and other sources of coupled medical data. This was done in several steps. First the expectations and perceived barriers by interventional cardiologists on ML-based algorithms in clinical practice were assessed. Next, the feasibility of health insurance code-based querying of electronic health records (EHRs) for the creation of ML datasets was assessed. Third, a proof-of-concept study on creating a dataset and a deep learning network for predicting lesion significance on ICA images was carried out. Last, a roadmap for the curation of data for the development of ML models in IC was created.
Generally, interventional cardiologists have positive expectations for applying ML in their clinical practice. Furthermore, the willingness to collaborate in the development and clinical validation of ML algorithms is high. This is essential for translating ML models to clinical practice. Health insurance code-based querying of EHRs is not a feasible approach for creating datasets for the complex syndromes which currently pose diagnostic challenges in IC. However, the EHRs hold valuable data for the development of ML datasets. The same goes for the data collection strategy in the proof-of-concept study and it was shown that it is feasible to train deep learning networks on this data.
ML shows promising results for solving the diagnostic and therapeutic challenges encountered in interventional cardiology and directions for further research were identified. For the creation of algorithms that can be applied in clinical practice, close collaboration between ML professionals and clinicians is needed. Besides this, further research is needed to develop scalable strategies for the creation of large datasets, containing adequately labelled patients that represent the real-life population.

The aim of this project was the implementation of optical coherence tomography (OCT), an invasive intracoronary imaging (ICI) tool used for the visualization of coronary arteries. The implementation was performed in a structured manner by: (1) A literature review into the current applications of OCT in relation to current standards; (2) A thesis feasibility study into clinical practices, professional views and other processes regarding ICI; (3) A Master’s thesis including a cost-effectiveness analysis (CEA) and the actual implementation of OCT at the department. The project started with a literature review about the current applications of OCT in relation to intravascular ultrasound (IVUS) and coronary angiography (CAG). The literature review has been published in Cardiology and Therapy. In general, OCT-guidance seems to contribute to favorable clinical outcomes compared with CAG-guidance alone. However, OCT-guidance results in similar clinical outcomes as IVUS-guidance. OCT could be considered for lumen assessment and stent related morphology in more complex cases in which CAG interpretation remains uncertain. Since OCT and IVUS have distinct characteristics, these techniques are complementary and should be considered carefully for each patient case based on the benefits and limitations of both techniques. The thesis feasibility study aimed at acquiring insights into the clinical practices, the different views, and other processes (data, training, etcetera) related to ICI at the LUMC and other medical centers. This was achieved by observing clinical practices at the LUMC and other medical centers, and by interviewing clinicians and technicians at those centers. Both the literature review and the feasibility study provided extensive insights in current practices and controversies regarding ICI. Such insights were essential before implementation of OCT. These insights were used to carefully execute the implementation, while accounting for the interests of all stakeholders as much as possible. In addition, the knowledge gained from the literature review and feasibility study were used as input for the master’s thesis. The master’s thesis included a health economic evaluation comparing cost-effectiveness of OCT with IVUS and CAG, and the actual implementation of OCT at the department. The CEA was performed using the concept of Markov modelling. In general, OCT along with CAG was a more cost-effective intracoronary treatment strategy than IVUS along with CAG for patients with CAD. However, the gain in QALYs and reduction in costs were limited. Both IVUS and OCT outperform CAG alone in terms of cost-effectiveness. With this thesis the current applications of OCT in relation to IVUS and CAG were identified, insights into different views and practices regarding ICI in the interventional cardiology domain were obtained and the cost-effectiveness of OCT-guided PCI compared with IVUS-guided PCI and PCI solely guided by CAG was assessed. In addition, OCT was implemented at the department of interventional cardiology of the LUMC. The processes, procedures and considerations around the implementation of OCT at the department of interventional cardiology were identified and described. Finally, the thesis was concluded with an argument about the added value of the technical physician as a new healthcare professional.