AI Model Reads Gray and White Matter Signals to Spot Autism with 87% Accuracy
Bioengineer.org
A new AI model using gray and white matter signals from brain imaging detects autism with 87.49% accuracy, revealing novel communication patterns between brain regions. The model combines a Mamba-based architecture with convolutional neural networks to capture both spatial and temporal dynamics of brain activity more effectively than previous methods. The approach improves upon traditional models by efficiently integrating dynamic functional connectivity patterns for more accurate autism identification.
All articles from 13 October 2026. Summaries are AI-generated and may contain errors; the linked article is the authority.