Deciphering Seizure Semiology in Corpus Callosum Injuries: A Comprehensive Systematic Review with Machine Learning Insights

Introduction 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. Objective 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. Methods Adhering to the PRISMA guidelines, a comprehensive search across multiple databases, including PubMed/Medline, NIH, Embase, and Cochrane Library, was conducted. 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. Results Out of 1250 initially identified articles, 41 studies met the inclusion criteria, encompassing 56 cases. 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 cases post-treatment. Conclusion 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.