Deciphering seizure semiology in corpus callosum injuries: A comprehensive systematic review with machine learning insights.

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Abstract

Seizure disorders have often been found to be associated with corpus callosum injuries, but in most cases, they remain undiagnosed. Understanding the clinical, electrographic, and neuroradiological alternations can be crucial in delineating this entity.This systematic review aims to analyze the effects of corpus callosum injuries on seizure semiology, providing insights into the neuroscientific and clinical implications of such injuries.Adhering to the PRISMA guidelines, a comprehensive search across multiple databases, including PubMed/Medline, NIH, Embase, Cochrane Library, and Cross-ref, was conducted until September 25, 2023. Studies on seizures associated with corpus callosum injuries, excluding other cortical or sub-cortical involvements, were included. Machine learning (Random Forest) and deep learning (1D-CNN) algorithms were employed for data classification.Initially, 1250 articles were identified from the mentioned databases, and additional 350 were found through other relevant sources. Out of all these articles, 41 studies met the inclusion criteria, collectively encompassing 56 patients The most frequent clinical manifestations included generalized tonic-clonic seizures, complex partial seizures, and focal seizures. The most common callosal injuries were related to reversible splenial lesion syndrome and cytotoxic lesions. Machine learning and deep learning analyses revealed significant correlations between seizure types, semiological parameters, and callosal injury locations. Complete recovery was reported in the majority of patients post-treatment.Corpus callosum injuries have diverse impacts on seizure semiology. This review highlights the importance of understanding the role of the corpus callosum in seizure propagation and manifestation. The findings emphasize the need for targeted diagnostic and therapeutic strategies in managing seizures associated with callosal injuries. Future research should focus on expanding the data pool and exploring the underlying mechanisms in greater detail.Copyright © 2024 Elsevier B.V. All rights reserved.