SURG-44. Enhancing Decision-Making in Surgery for a Large Right Temporo-corneal Meningioma Through an Explainable Human-AI Collaboration

Abstract BACKGROUND This case report explores the integration of an explainable artificial intelligence (AI) system into the neurosurgical workflow to enhance preoperative planning, intraoperative decision-making, and postoperative outcome prediction. Case Presentation: Main Symptoms and Clinical Findings We report the case of a 48-year-old Arabic female with a one-year history of right-lateralized headaches that became generalized over time, along with episodes of loss of consciousness lasting 15–45 minutes, occurring 3–8 times daily. These episodes were characterized by a prodrome of palpitations and chest tightness, followed by transient unresponsiveness, urinary incontinence, and prolonged postictal periods. Neurological examination was unremarkable, with preserved motor and sensory functions. Main Diagnoses, Therapeutic Interventions, and Outcomes Initial evaluation in Algeria led to a diagnosis of epilepsy, for which the patient was prescribed multiple antiepileptic drugs. Further assessment in Belgium, including MRI and EEG, revealed a right temporal extra-axial mass (46×36×45 mm) consistent with meningioma. EEG findings were normal, suggesting psychogenic non-epileptic seizures (PNES) rather than epileptic seizures. A multidisciplinary approach, incorporating AI-driven imaging analysis and predictive modeling, was employed to optimize surgical strategies. The AI system provided insights into tumor segmentation, vascular involvement, and risk assessment, aiding in determining the safest resection trajectory. The patient underwent surgical resection of the tumor via a right pterional craniotomy, with total excision achieved with preserved neurological function. Intraoperative bleeding was significant (2L), but the postoperative course was favorable. Antiepileptic medication withdrawal was initiated, and no recurrent seizures were reported postoperatively. CONCLUSION This case highlights the role of explainable AI in improving neurosurgical decision-making by enhancing planning, execution, and outcome prediction. It also underscores the complexity of diagnosing seizure-like episodes in intracranial tumors, emphasizing thorough evaluation to differentiate PNES from epilepsy. Surgical intervention can resolve symptoms when structural lesions are present, reinforcing the need for a multidisciplinary approach to prevent unnecessary long-term antiepileptic treatment.