Supervised machine learning for visual detection of seizures
Abnormal synchronous brain activity leads to seizures resulting in twitching, stiffness, or jerking, which are used in visual scoring systems such as the Racine scale to quantify seizure intensity.
There is a need for scalable and rigorous quantitative approaches since visual systems are slow, time consuming and subjective.
The researchers used non-invasive video recordings of mice to develop a method using supervised machine learning to automatically quantify seizure severity.
This approach provides a scalable and objective tool for seizure assessment, augmenting manual scoring and offering a more precise method for drug discovery and disease modeling in preclinical research.
https://www.cell.com/cell-reports-methods/fulltext/S2667-2375(25)00278-4
