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Seizures: Classification

Seizures: Classification

Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
Seizures are typically classified into two main categories: focal and generalized seizures.
Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:

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Related Experiment Video

Updated: May 5, 2026

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

Ultra-fast deep-learned CNS tumour classification during surgery.

C Vermeulen^1,2, M Pagès-Gallego^1,2, L Kester³

¹Oncode Institute, Utrecht, The Netherlands.

|October 11, 2023

Summary

This summary is machine-generated.

A new AI tool, Sturgeon, accurately classifies central nervous system tumors during surgery using rapid sequencing. This machine-learned diagnosis aids neurosurgeons, potentially improving patient outcomes and reducing complications.

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Published on: November 30, 2022

Area of Science:

Neuro-oncology
Artificial Intelligence in Medicine
Genomic Medicine

Background:

Central nervous system (CNS) tumors are highly lethal, especially in children.
Neurosurgical tumor resection requires balancing maximal tumor removal with minimal neurological damage.
Current diagnostic methods like imaging and histology are often inconclusive or inaccurate.

Purpose of the Study:

To develop a patient-agnostic, transfer-learned neural network (Sturgeon) for molecular subclassification of CNS tumors.
To enable rapid, intraoperative CNS tumor diagnosis using sparse methylation profiles from nanopore sequencing.
To assess the accuracy and speed of Sturgeon in real-time surgical settings.

Main Methods:

Development of Sturgeon, a neural network trained on sparse methylation profiles.
Utilizing rapid nanopore sequencing for intraoperative data acquisition.
Validation of Sturgeon on retrospective and real-time surgical cases.

Main Results:

Sturgeon achieved accurate diagnoses in 45/50 retrospective samples within 40 minutes.
In real-time surgeries, Sturgeon provided diagnoses in under 90 minutes.
72% (18/25) of real-time diagnoses were correct, with others not reaching confidence thresholds.

Conclusions:

Machine-learned diagnosis via low-cost intraoperative sequencing can aid neurosurgical decision-making.
This approach has the potential to prevent neurological comorbidity and avoid repeat surgeries.
AI-powered molecular subclassification offers a promising advancement in CNS tumor management.