Integration of 2D and 3D Imaging Descriptors with Large Language Models for Assessing Pediatric Foreign-Body Aspiration Risk

Background/Objectives: Foreign-body aspiration (FBA) is a common and largely preventable pediatric emergency, yet current safety standards and risk assessments rely predominantly on object size and on anecdotal descriptions and bronchoscopy findings. We propose a clinically oriented proof-of-concept workflow that combines high-resolution three-dimensional (3D) scanning and calibrated two-dimensional (2D) imaging of retrieved objects with radiomic shape descriptors and large language model (LLM) reasoning to support aspiration risk assessment and guide prevention. Methods: Objects were obtained from the Susy Safe registry and historical series from the University Clinical Centre Tuzla. Each object was digitized with 3D scanning and photographed with a ruler. Morphometric descriptors—including volume, surface area, sphericity, elongation, flatness, curvature and convexity—were computed from stereolithography (STL) meshes; silhouette area, perimeter and Feret diameters were extracted from 2D photographs. Normative airway dimensions from radiographic and computed tomography (CT) studies provided anatomical context. A sharp, irregular metallic object recovered from a child’s laryngo-tracheal tract served as an illustrative case. Results: The object’s major axis approximated the anteroposterior glottic diameter, suggesting potential traversal when longitudinally oriented, whereas its irregular shape increased the likelihood of mucosal laceration and lodging. LLM-based synthesis provided a structured narrative interpretation consistent with a high-risk profile and highlighted preventive implications. Conclusions: Combining 2D/3D morphometry with LLM reasoning provides objective assessment of FBA hazards and may support safer product design, injury-prevention policies, and caregiver education.