Abstract

ALVAREZ MARCOS, Francisco; ALONSO GOMEZ, Noelia  and  HARO MIRALLES, Joaquín de. Artificial intelligence, machine learning, vascular surgery, automatic image processing. Implications for clinical practice. Angiología [online]. 2021, vol.73, n.2, pp.65-75.  Epub May 17, 2021. ISSN 1695-2987.  https://dx.doi.org/10.20960/angiologia.00177.

Evidence-based clinical decision is based overall in broad-spectrum randomized studies. However, the patient's reality may be much more complex, and capturing it as a whole justifies the so-called precision medicine, which takes into account genetic, phenotypic and psycho-social variables. This approach is possible thanks to the management of big data, using complex computing system based in artificial intelligence (AI) and machine learning (ML).

This update, based on over 50 publications, intends to give a view on IA and ML application on every aspect of contemporary vascular and endovascular practice. The field with a greater potential development is automatic image processing, that allows vessel segmentation, deformation and movement estimations and the subsequent integration into treatment guidance. IA and ML also offer great possibilities in simulation, especially of open surgical procedures, and also in the improvement of machine-operator interaction with workstations and robotic systems. Finally, big data integration opens new horizons in outcome prediction, almost matching the quality and potential impact of register to these of randomized evidence, and overcoming the results of traditional statistics.

Keywords : Artificial intelligence; Machine learning; Automatic image processing; Simulation; Big data.

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