Predicting recovery following stroke: deep learning, multimodal data and feature selection using explainable AI

no code implementations • 29 Oct 2023 • Adam White, Margarita Saranti, Artur d'Avila Garcez, Thomas M. H. Hope, Cathy J. Price, Howard Bowman

The highest classification accuracy 0. 854 was observed when 8 regions-of-interest was extracted from each MRI scan and combined with lesion size, initial severity and recovery time in a 2D Residual Neural Network. Our findings demonstrate how imaging and tabular data can be combined for high post-stroke classification accuracy, even when the dataset is small in machine learning terms.

feature selection Stroke Classification

Predicting Language Recovery after Stroke with Convolutional Networks on Stitched MRI

no code implementations • 26 Nov 2018 • Yusuf H. Roohani, Noor Sajid, Pranava Madhyastha, Cathy J. Price, Thomas M. H. Hope

One third of stroke survivors have language difficulties.