Trusting Machine Learning Results from Medical Procedures in the OperatingRoom
Other conference contribution, 2022
Machine learning can be used to analyse physiological data for several purposes. Detection of cerebral ischemia is an achievement that would have high impact on patient care. We attempted to study if collection of continous physiological data from non-invasive monitors, and analysis with machine learning could detect cerebral ischemia in tho different setting, during surgery for carotid endarterectomy and during endovascular thrombectomy in acute stroke. We compare the results from the two different group and one patient from each group in details. While results from CEA-patients are consistent, those from thrombectomy patients are not and frequently contain extreme values such as 1.0 in accuracy. We conlcude that this is a result of short duration of the procedure and abundance of data with bad quality resulting in small data sets. These results can therefore not be trusted.
Author
Ali El-Merhi
University of Gothenburg
Sahlgrenska University Hospital
Helena Odenstedt Hergès
University of Gothenburg
Sahlgrenska University Hospital
Linda Block
University of Gothenburg
Sahlgrenska University Hospital
Mikael Elam
University of Gothenburg
Richard Vithal
University of Gothenburg
Sahlgrenska University Hospital
Jaquette Liljencrantz
University of Gothenburg
Miroslaw Staron
University of Gothenburg
Chalmers, Computer Science and Engineering (Chalmers), Software Engineering (Chalmers)
AAAI 2022 Workshop: Trustworthy AI for Healthcare
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Subject Categories (SSIF 2025)
Neurosciences
Cardiology and Cardiovascular Disease
Neurology