Neuroimaging-Based Deep Learning Applications for Lesion Detection and Predicting the Outcome Following Epilepsy...

Area of Science:

• Neurology
• Medical Imaging
• Artificial Intelligence

Background:

• Neuroimaging is crucial for assessing drug-resistant focal epilepsy and surgical candidacy.
• Deep learning models have emerged for detecting epileptogenic lesions and predicting surgical outcomes from MR imaging.
• While lesion detection models show promise in multi-center studies, outcome prediction models are still exploratory.

Purpose of the Study:

• To review the current state and future directions of neuroimaging-based deep learning models in epilepsy surgery.
• To highlight the potential and limitations of these models in clinical practice.

Main Methods:

• Review of recent neuroimaging and deep learning studies in epilepsy surgery.
• Analysis of model performance for epileptogenic lesion detection and surgical outcome prediction.

Main Results:

• Deep learning models have demonstrated effectiveness in detecting epileptogenic lesions on MR imaging.
• Neuroimaging-based prediction of post-surgical seizure outcomes using deep learning remains an active area of research with preliminary results.

Conclusions:

• Neuroimaging-based deep learning holds significant potential for improving epilepsy surgery evaluation.
• Further research focusing on transparency, interpretability, and prospective validation is necessary for clinical translation of outcome prediction models.