Machine learning identifies stroke features between species
Salvador Castaneda Vega, Prateek Katiyar, Francesca Maria Russo, Kristin Patzwaldt, Luisa Schnabel, Sarah Mathes, Johann-Martin Hempel, Ursula Kohlhofer, Irene González-Menéndez, Leticia Quintanilla‐Martínez, Ulf Ziemann, Christian la Fougère, Ulrike Ernemann, Bernd J. Pichler, Jonathan A. Disselhorst, Sven Poli
Abstract
Our findings reveal that the segmentation produced by our proposed framework using 24 h MRI rat data strongly correlated with the final stroke volume, denoting a predictive effect. In addition, we show for the first time that the stroke imaging features can be directly translated between species, allowing identification of acute stroke in humans using the model trained on animal data. This discovery reduces the gap between the clinical and preclinical fields, unveiling a novel approach to directly co-analyze clinical and preclinical data. Such methods can provide further biological insights into human stroke and highlight the differences between species in order to help improve the experimental setups and animal models of the disease.