DEEP LEARNING-BASED PREDICTION OF POSTOPERATIVE NEUROLOGICAL DEFICITS IN BRAIN TUMOR SURGERY: ENHANCING RISK STRATIFICATION AND CLINICAL DECISION-MAKING

Abstract

Accurate prediction of postoperative neurological deficits remains a critical challenge in brain tumor surgery, directly influencing surgical planning, risk stratification, and patient outcomes. Despite advances in neuroimaging and intraoperative monitoring, conventional assessment methods are often limited in their ability to integrate complex, high-dimensional clinical data. In this context, deep learning has emerged as a powerful tool for predictive modeling in neurosurgery, offering the ability to analyze multimodal data and identify subtle patterns associated with postoperative complications.