Evolving Applications of Artificial Intelligence in Emergency Surgery and Neurosurgery

Background Artificial intelligence is reshaping surgical practice, yet its integration faces ethical, educational, and technical barriers. Emergency surgery and neurosurgery present complementary challenges: the former defined by time pressure, incomplete data, and population heterogeneity; the latter by complex imaging interpretation and individualized prognostication. This thesis examines AI across both domains, from ethical governance and professional readiness to clinical prediction models. Objectives The aims were threefold: (1) map the ethical landscape and assess professional preparedness for surgical AI; (2) develop and validate AI models for neurosurgical diagnosis and prognosis in hydrocephalus, epilepsy, brain tumors, and glioblastoma; (3) explore emerging methodologies including quantum-enhanced hybrid feature selection and radiomic-transcriptomic associations. Methods A Delphi consensus engaged 12 experts using the EFTE method aligned with EU AI Ethics Guidelines. A cross-sectional survey collected responses from 650 surgeons across 71 countries under WSES endorsement. A Super Learner ensemble was trained on cortical thickness from 294 patients for iNPH diagnosis. Quantum-enhanced hybrid feature selection (QAOA, Simulated Bifurcation, Simulated Annealing) combined with gradient boosting machine classification predicted VNS response in 31 epilepsy patients. Interaction modeling examined cortical thickness, tumor grade, and memory in 97 glioma patients. Logistic regression assessed BMI as a predictor of cerebellar mutism in 50 pediatric posterior fossa tumor patients. Elastic Net with leave-one-patient-out cross-validation and linear mixed-effects models tested radiomic-transcriptomic associations in 28 glioblastoma patients from the IvyGAP atlas. Results The Delphi study distilled seven ethical requirements, with transparency and accountability as highest priority. The WSES survey found that 69% of surgeons reported AI familiarity yet only 17% provided concordant definitions; perceived importance rose from 3.06 (current) to 3.88 (five-year horizon). The Super Learner achieved AUC 0.843 for iNPH diagnosis, with caudal middle frontal and superior frontal cortical thickness as key discriminators. Gradient boosting machine achieved 77.1% cross-validated accuracy for VNS response prediction; age, total seizures, and time since diagnosis were the leading predictors. A significant three-way interaction between cortical thickness, tumor grade, and education predicted postoperative memory change in glioma patients (p = 0.035). BMI was the sole significant predictor of cerebellar mutism (p = 0.031, AUC = 0.749). In the radiomic-transcriptomic analysis, only Angiogenesis (R-squared-cv = 0.209) and Inflammatory Response (R-squared-cv = 0.185) demonstrated genuine associations among 24 pathways tested; 21 pathways showed no predictive signal. Conclusions This dual-axis investigation reveals that while emergency surgeons increasingly recognize AI's importance, foundational knowledge gaps persist. Neurosurgical AI models demonstrated diagnostic and prognostic value across hydrocephalus, epilepsy, glioma, and glioblastoma, though sample sizes constrain generalizability. Bridging ethical framework to clinical deployment requires targeted education, prospective multicenter validation, and explainable model design.