The shortage of pediatric datasets challenges the development and evaluation of safe and effective artificial intelligence and machine learning (AI/ML)-based medical devices for pediatrics. This is the case for computer-aided triage (CADt) of intracranial hemorrhage (ICH) where the incidence of ICH in the pediatric population is lower than adults thus limiting pediatric data for evaluations. Here we introduce tools for generating synthetic adult and pediatric non-contrast computed tomography (NCCT) datasets with ICH modeled from limited real data to address the shortage of pediatric data for ICH device assessments. Read More

Meeting name: SPR 2025 Annual Meeting , 2025

Authors: Weaver Jayse, Nelson Brandon

Keywords: Intracranial Hemorrhage, Deep Learning, Computed Tomography