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AI Aids Stroke Experimental Therapies
Researchers at the University of Southern California have developed an automated system based on magnetic resonance imaging to measure changes in brain tissue in animal models, aiming to evaluate experimental treatments for stroke. The system employs deep learning techniques to identify stroke damage, brain swelling, and tissue loss, while reducing variability in measurements across multiple imaging devices and different environments. The system was tested on over 2,000 mice and rats, demonstrating accuracy comparable to that of expert assessments, thereby enhancing the potential to evaluate treatment efficacy before proceeding to clinical trials. This system is an open-source tool that supports collaborative research, facilitating result replication and improving the assessment of experimental stroke treatments in animal studies.
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