Artificial intelligence in outcome prediction and neurosurgical planning
DOI:
https://doi.org/10.34024/rnc.2025.v33.19739Keywords:
Artificial Intelligence, Neurosurgery, Machine Learning, PlanningAbstract
Introduction. The metastasis of solid tumors is common in neurosurgery, and its treatment faces challenges such as radioresistance and difficulty accessing central areas of the brain. Advanced technologies, especially those involving machine learning, have improved diagnostic and predictive accuracy in neurosurgery; however, gaps remain in the literature regarding their application. Objective. This review aims to analyze the potential of machine learning (ML) techniques in neurosurgical processes. Method. Data were collected from the PubMed, Scopus, and Web of Science databases using the descriptors Brain Neoplasm, Brain Tumor, Machine Learning, and Neurosurgical Procedure. Results. Recent ML models demonstrated high accuracy in predicting therapeutic responses and metastasis progression, surpassing traditional methods, especially when integrated with clinical and radiomic data. Automated tools have also advanced tumor segmentation, though improvements are still needed for detecting small lesions. ML has stood out in longitudinal monitoring, optimizing volumetric analyses and clinical decision-making. In surgical navigation, accelerates tumor dissection and enhances tumor identification, showing great potential to personalize treatments accurately and individually. Conclusion. Exploring the applications of ML techniques in neurosurgery, focusing on brain tumor treatment, has revealed a promising outlook on how ML can transform the field of neuroscience, from planning to executing surgical interventions.
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Copyright (c) 2025 Wesley Lima Guimarães, Ana Paula Beirigo Barbosa, Gerley Adriano Miranda Cruz, Marcus Kalyel Ferreira Godoi, Maria Carolina Mota Mendes, Salvador Rassi Filho, Jalsi Tacon Arruda
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Accepted 2025-03-12
Published 2025-03-25
