In this article we will investigate the different categories of machine learning driven solutions that can provide added value in cathlab procedures. Categorizing machine learning applications in the cathlab, and structurally investigating their respective data needs, aids in developing a systematic approach to the data collection and algorithm development challenges. Machine learning applications in the cathlab can be divided in four data categories, dependent on the type of data they receive as input: 1) image-based, 2) 1D signals such as ECG, respiratory, etc., 3) natural...

In recent years machine learning techniques have seen a tremendous increase in adoption, initially fueled by the massive use of social media leading to very large databases. A development which has also translated to the medical arena. For clinical applications, however, the sizeable data collections machine learning demands remains a challenge. Categorizing machine learning applications in the cathlab, and structurally investigating their respective data needs, aids in developing a systematic approach to the data collection and algorithm development challenges. Categories Machine learning applications in the...

In this section several high level applications, and their machine learning building blocks will be further examined. Device navigation Typically, in minimally invasive procedures the in-body device can only be navigated and monitored through external imaging. Suitable imaging modalities are ultrasound, interventional x-ray, and real-time CT and MR. AI can be employed to detect and locate interventional devices, such as intra-vascular devices, and other percutaneous devices. Intra-vascular devices comprise catheters and guidewires [1] (Figure 1), intra-vascular valves (Figure 2), stents (Figure 3), etc. Other percutaneous...

Machine learning employed in the cathlab can be categorized along multiple dimensions. The segmentation can be performed based on input data type, fine-grained algorithmic tasks, and high level applications. An overview of the data categories aids in structurally addressing data needs and development efforts. Acknowledgements Pierre Ambrosini, Fred van Nijnatten and Marco Verstege are acknowledged for the development, annotation and training of the machine learning algorithms used in the figures.

D. Ruijters: Employee: Philips

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