Abstract
AbstractBackgroundThe incorporation of Machine Learning (ML) into Laser Interstitial Thermal Therapy (LITT) represents a significant advancement in minimally invasive neurosurgery, particularly for treating brain tumors, vascular malformations, and epileptogenic foci. This systematic review focuses on evaluating the integration and impact of ML in enhancing the efficacy, precision, and outcomes of LITT in neurosurgical procedures.MethodsAn exhaustive search was conducted in major scientific databases for studies from 2015 to 2023 that specifically focused on the application of ML in LITT. The review assessed the development and implementation of ML algorithms in surgical planning, outcome prediction, and postoperative evaluation in LITT. Rigorous inclusion criteria were applied to select studies, and a combination of meta-analysis and qualitative synthesis was used to analyze the data.ResultsThe review synthesizes findings from a range of studies, including retrospective analyses and initial clinical trials. It highlights the role of ML in enhancing the selection criteria for LITT, optimizing surgical approaches, and improving patient-specific outcome predictions. While LITT showed favorable results in treating non-resectable lesions, the integration of ML was found to potentially refine these outcomes further. However, challenges such as the need for larger sample sizes, standardization of ML algorithms, and validation of these methods in clinical settings were noted.ConclusionsThe integration of ML into LITT procedures marks a promising frontier in neurosurgery, offering potential improvements in surgical accuracy and patient outcomes. The evidence suggests a need for continued development and rigorous testing of ML applications in LITT. Future research should focus on the refinement and validation of ML algorithms for wider clinical adoption, ensuring that technological advancements align with patient safety and treatment efficacy.
Publisher
Cold Spring Harbor Laboratory