🧠 Researchers from the joint Biomedically-Informed AI Laboratory of Skoltech and the University of Sharjah have improved the performance of computer vision algorithms that look for signs of epilepsy and multiple sclerosis on brain MRI scans. Rather than merely analyzing an image as a set of pixels, the neural network now also attends to medical features known to physicians. This allows it to spot atypical manifestations of diseases and small lesions that current models often miss. Detection accuracy increased by 8% for epilepsy and by 2% for multiple sclerosis, while training required 30% less data. “By integrating biological features into the neural network, we make it work more in line with the actual reasoning of physicians,” said the study’s lead author, Nadezhda Alsahanova, a research engineer at Skoltech and a PhD student in the Institute’s Computational and Data Science and Engineering program. “Further development of this approach will make the results of computer vision analysis more interpretable. The model will justify its conclusions in a comment, making the findings more trustworthy in the eyes of a physician.” In the future, such interpretable solutions will help doctors diagnose diseases and select treatment strategies faster. The Russian Science Foundation-backed study was published in the proceedings of MICCAI 2026. How it works: 🔹 in the cards; 🔹 in the detailed article on the Skoltech website; 🔹 in the proceedings of Medical Image Computing and Computer Assisted Intervention — an A-rated international conference on medical imaging and AI. Skoltech is part of the VEB.RF group.