Trusting Machine Learning Results from Medical Procedures in the Operating Room

TL;DR

This study investigates the reliability of machine learning in detecting cerebral ischemia during surgeries, highlighting challenges with data quality and procedure duration affecting trustworthiness of results.

Contribution

It compares machine learning performance across different surgical settings, revealing limitations due to data quality and procedure length impacting result reliability.

Findings

Consistent results in carotid endarterectomy cases

Unreliable and extreme accuracy values in thrombectomy cases

Data quality and procedure duration affect trust in ML results

Abstract

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.