Neural network spots previously missed epilepsy signs on brain MRIs
· Medical Xpressby Nicolas Posunko, Skolkovo Institute of Science and Technology
edited by Gaby Clark, reviewed by Andrew Zinin
Gaby Clark
Scientific Editor
Meet our editorial team
Behind our editorial process
Andrew Zinin
Chief Editor
Meet our editorial team
Behind our editorial process Editors' notes
This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility:
fact-checked
trusted source
proofread
The GIST Add to Preferred Sources
Researchers from the joint Biomedically-Informed AI Laboratory of Skoltech (a VEB.RF Group institution) and the University of Sharjah and their colleagues 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 is complemented by the machine's capability to spot subtle details that elude the human eye. In the future, such solutions will help doctors diagnose diseases and select treatment strategies.
The study was published in the proceedings of the 29th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2026), held in Strasbourg, France, from Sept. 27 to Oct. 1.
Computer vision algorithms are already fairly good at detecting signs of drug-resistant epilepsy and multiple sclerosis in medical images. This gives hope that neural networks will soon be able to assist radiologists in this work, thereby speeding up the analysis and relieving the doctors of some of their workload, while also improving the detection rate. About 30% of cases of epilepsy alone are currently deemed "MRI-negative": The doctor does not see signs of the disease on the scan, but the machine can.
One of the main difficulties, though, is that existing models are geared toward detecting the typical presentation of the disease. For instance, in drug-resistant epilepsy, a blurring of the boundary between gray and white brain matter is frequently observed, and this feature is easily detected by neural networks. However, there is a rarer scenario where compact areas with increased signal intensity serve as the key sign of the disease on the scan. Today's models largely ignore these.
While in epilepsy pinpointing the location of a lesion is necessary to perform surgical intervention, in multiple sclerosis monitoring the appearance of new disease foci of various sizes allows doctors to lay out or adapt a treatment plan. The problem here is that models are good at finding large lesions on scans but often miss small ones. Thus, in both epilepsy and multiple sclerosis, the model has to be taught that sometimes pathologies manifest atypically, and the features observed in those cases are significant—not merely random noise. This is what the authors of the study managed to achieve.
"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 Ph.D. 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."
The researchers tested their approach on clinical data. For both diseases, the efficiency of pathological area detection increased: by 2% for scans showing signs of multiple sclerosis, and by 8 percentage points—from 72% to 80%—on scans showing signs of epilepsy. Training also became more robust: The new model could match the performance of its predecessors with 30% less training data.
Technically, the innovation behind the improvement involved supplementing segmentation, which is a standard task in automated medical image analysis, with an auxiliary procedure, classification. Now, the model does not simply segment the image in the manner of "these areas are healthy tissue, and these other ones are pathological," but also categorizes the latter regions based on which part of the brain they appear in, whether the suspected lesion has blurred boundaries, and so on.
Thanks to this, the model begins to "pay attention" to biomedical features, which improves segmentation quality and—in the long run—allows the result to be accompanied by a comment for the doctor's benefit.
Assistant professor Maxim Sharaev, who co-directs the joint Skoltech-University of Sharjah BIMAI-Lab and was a principal investigator in the study, said, "Searching for small and atypical pathologies on MRIs is a critically important task, since missing them can cost patients the right treatment.
"What makes this study's approach original is that when detecting a pathology, the model begins to rely on biomedical features, much like a human physician. Consequently, the output becomes interpretable and more suitable for real-world diagnostics. Importantly, Nadezhda, who came up with this idea and brought it all the way to MICCAI level, managed to confirm the findings on clinical data."
More information
Nadezhda Alsahanova et al., BioGuide: Biomedically-Guided Segmentation for Medical Tasks (2026). Proceedings of the [29th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2026)](conferences.miccai.org/2026/en/), Strasbourg, France, Sept. 27 to Oct. 1. DOI: 10.1007/978-3-032-38085-2_7
Key medical concepts
Clinical categories
NeurologyDiagnostic radiology Provided by Skolkovo Institute of Science and Technology Who's behind this story?
Gaby Clark
MA in English, copy editor since 2021 with experience in higher education and health content. Dedicated to trustworthy science news. Full profile →
Andrew Zinin
Master's in physics with research experience. Long-time science news enthusiast. Plays key role in Science X's editorial success. Full profile →
Citation: Neural network spots previously missed epilepsy signs on brain MRIs (2026, October 27) retrieved 27 October 2026 from https://medicalxpress.com/news/2026-10-neural-network-previously-epilepsy-brain.html This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.