**4.1 Patient selection**

A quantitative means of evaluating an individual patient outcome preoperatively is highly desirable in improving surgical decision-making. At the present moment, clinical outcome judgment is heavily reliant on the individual neurosurgeon. Prognostic indices in use today, though easily applicable, lack adequate predictive performance primarily due to the streamlining of numerical data to categorical data [44, 45]. Conversely, ML, by its very nature, could circumvent such a simplification.

Until now, previous literature has compared neurosurgical patient outcome predictive performance between ML algorithms, classical logistic regressions, prognostic indices, and neurosurgeons with differential results. Against classical logistic regressions, ML models have demonstrated superior performance in predictions of successful endoscopic third ventriculostomy, postoperative ventricular peritoneal shunt infection, mortality after embolization of AVMs, patient satisfaction after

laminectomy for lumbar spinal stenosis, in-hospital mortality in patients with traumatic brain injury, cerebral vasospasm after aneurysmal subarachnoid hemorrhage, and outcomes after a burr-hole procedure for a chronic subdural hematoma [45–52]. Against current logistic regression prognostic indices for prediction of successful endoscopic third ventriculostomy (ETV) 6 months postoperatively, ANNs have demonstrated superior performance [45]. Masoudi et al. found that for ETV prediction 6 months postoperative, their multi-layer perceptron ANN demonstrated an AUC of 0.913 compared to a logistic regression AUC of 0.819 [53]. Some ML models have shown better performance compared to prognostic indices predicting outcome after stereotactic radiosurgery for cerebral arteriovenous malformation (AVM) with AUCs of 0.70–0.71 vs. 0.57–0.69 [44, 52]. A random forest classifier (RFC), a class of ML model achieved an AUC of 0.80, with 0.34 sensitivity, 0.95 specificity, 0.73 positive predictive value, 0.80 negative predictive value, and 0.79 accuracy for the prediction of traumatic brain injury in children following a cranial CT of the brain, demonstrating a substantial alternative to the currently used nomogram for the prediction of intracranial injury following CT in children with TBI [54].

Some recent studies have investigated the differences in ML and clinician performance in predicting neurosurgical outcomes in patients. Emblem et al. found that against fuzzy C-means, a class of ML model, neuroradiologists performed similarly in survival predictions for newly diagnosed glioma patients [55]. Emblem et al. also discovered that a support vector machine (SVM) model combined with perfusionweighted magnetic resonance (MR) imaging better predicted survival in glioblastoma patients compared to neuroradiologists [56]. Currently, although especially experienced neurosurgeons have been demonstrated to exhibit strong patient survival prediction skills in patients with high-grade glioma undergoing surgery on groupwide metrics, they often missed on the individual level [57]. Hence, future AI tools could help bridge this gap by supporting neurosurgeons' insights in the prediction of patient survival.
