The application of machine learning for treatment selection of unruptured brain arteriovenous malformations: A secondary analysis of the ARUBA trial data.

Journal: Clinical neurology and neurosurgery

Published Date: Dec 8, 2024

OBJECTIVE: To build a supervised machine learning (ML) model that selects the best first-line treatment strategy for unruptured bAVMs.

Tejas Venkataram

Department of Neurosurgery, St. John's Medical College Hospital, Bengaluru, India.
Shreyas Kashyap

Independent Researcher, Bengaluru, India.
Mandara M Harikar

Clinical Trials Programme, Usher Institute of Molecular, Genetic, and Population Health Sciences, The University of Edinburgh, Edinburgh, UK.
Francesco Inserra

Department of Neurosurgery, Trauma Center, Gamma Knife Center, Cannizzaro Hospital, Catania, Italy.
Fabio Barone

Department of Neurosurgery, Trauma Center, Gamma Knife Center, Cannizzaro Hospital, Catania, Italy.
Mario Travali

Department of Diagnostic and Interventional Neuroradiology, Azienda Ospedaliera Cannizzaro, Catania, Italy.
Valeriox Da Ros

Diagnostic Imaging Unit, Department of Biomedicine and Prevention, 9318 University of Rome Tor Vergata, Italy.
Giuseppe E Umana

Department of Neurosurgery, Trauma Center, Gamma Knife Center, Cannizzaro Hospital, Catania, Italy.
Oluseye A Ogunbayo

Edinburgh Surgery Online, Clinical Science Teaching Organisation, Clinical Surgery, College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, UK.
Benjamin Aribisala

Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK; Lothian Birth Cohort studies, Department of Psychology, University of Edinburgh, Edinburgh, UK; Department of Computer Science, Lagos State University, Nigeria. Electronic address: Benjamin.Aribisala@ed.ac.uk.

Adult Female Humans Intracranial Arteriovenous Malformations Machine Learning Male Middle Aged Supervised Machine Learning

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