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Elmezzi Scholars · Active

Predicting response to vagus nerve stimulation in drug-resistant epilepsy

Machine learning models can predict which patients with drug-resistant epilepsy will respond to vagus nerve stimulation therapy, achieving an average area under the receiver operating characteristic curve (AUROC) of 0.84 across 12 studies comprising 535 patients.

Forest plot of weighted AUROC across all machine learning models for predicting VNS response, pooling to 0.84.
Forest plot of weighted AUROC across all machine learning models for predicting VNS response, pooling to 0.84.Bioelectronic Medicine, 2025 · CC BY-NC-ND 4.0 (opens in new tab)
Status
Active
Program
Elmezzi Scholars
Publications
1, latest 2025
Team
2 members

On this project

Alejandro Quintero Villegas, MD

Elmezzi Scholar

Theodoros Zanos, PhD

Professor & AVP

From the papers

Published figures

Figures reproduced from the papers behind this project. Each panel keeps its original source and license.

Pediatric-only cohorts show higher predictive performance (AUROC 0.88) and lower heterogeneity than adults.
Adult-only cohorts predict VNS response less accurately (AUROC 0.76) than pediatric populations.

Pediatric-only cohorts show higher predictive performance (AUROC 0.88) and lower heterogeneity than adults. Bioelectronic Medicine, 2025 (opens in new tab) · CC BY-NC-ND 4.0

Publication

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Bioelectronic MedicineNov 2025

Predicting response to neuromodulation therapies in drug-resistant epilepsy using machine learning models: a meta-analysis and systematic review

(opens in new tab)

More Elmezzi Scholar projects

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Sleep apnea screening in women

A machine learning approach to screening women for moderate-to-severe obstructive sleep apnea, a condition underdiagnosed in women because standard screening was built around male presentation.

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Uveal melanoma screening from fundus photographs

A systematic review and meta-analysis of artificial intelligence that distinguishes uveal melanoma from benign nevus on fundus photographs, the basis for automated screening in ophthalmology.

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Deep learning in pancreatic cancer and PET interpretation

Machine learning to predict completion of treatment for pancreatic cancer, and systematic reviews of deep learning for PET image interpretation and for pancreatic cancer care.