MACHINE LEARNING-DRIVEN RISK STRATIFICATION OF POST TRAUMATIC HYDROCEPHALUS BASED ON EARLY CRANIAL CT MORPHOMETRY (ОБРАЗОВАНИЕ НАУКА И ИННОВАЦИОННЫЕ ИДЕИ В МИРЕ, 2025)
Post-traumatic hydrocephalus (PTH) is a frequent complication following traumatic brain injury (TBI), potentially leading to delayed neurological deficits, cognitive decline, and functional impairment. Early detection is critical to enable timely intervention, prevent secondary brain injury, and improve outcomes. This study aimed to develop a predictive model integrating early ventricular morphometry derived from CT scans and key clinical variables. Retrospective data from 180 patients with moderate TBI (GCS 9–12) treated between 2021 and 2024 were analyzed. Morphometric indices, including Evans Index (EI), Bicaudate Index (BI), and Fronto-Occipital Horn Ratio (FOHR), were measured at the level of the foramen of Monro and combined with clinical parameters (age, sex, Glasgow Coma Scale [GCS], intraventricular hemorrhage [IVH], and decompressive craniectomy [DC]). Machine learning models, including gradient boosting, random forest, and logistic regression, predicted PTH occurrence within 6 months. PTH developed in 40 patients (22.2%). Gradient boosting achieved the highest predictive performance (AUC=0.88), with EI, IVH, DC, and FOHR identified as the most important predictors. This integrative approach facilitates early identification of high-risk patients and supports individualized monitoring strategies.