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.