Machine learning on non-contrast head CT scans to predict emergency department patient revisits with stroke

Student

Lyna Abrougui

Directeur.e(s) de recherche

Patrick Archambault

Simon Duchesne

Philippe Després

Start date

2022-09-01

Title of the research project

Machine learning on non-contrast head CT scans to predict emergency department patient revisits with stroke

Description

Prediction and early identification of stroke is crucial to prevent emergency department (ED) revisits and initiate treatment, reducing morbidity and mortality.

This project focuses on the analysis of non-contrast brain CT (NCCT) data to predict early ED revisits for patients coming back with a stroke diagnosis. The first objective will be gathering open-source NCCT data as well as NCCT data from the Integrated Health and Social Services Center from Chaudiere-Appalaches (CISSS-CA) to classify the presence/absence of stroke using an existing model. The second objective will be to develop and test a machine learning model with weights from the previous model and other relevant clinical data to classify short-term revisits to the ED as an outcome.

From a clinical perspective, the development of such a tool may help support neuroradiologists in image interpretation and clinical decision making in the ED.

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