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An Explainable, Knowledge-Driven System for Difficult Airway Risk Stratification and Planning

Year: 2026

Type: book-chapter

Abstract: Anticipating a difficult airway during surgery is critical for patient safety, yet recognition and escalation can be inconsistent in practice. We present an ontology-based clinical decision support system that standardizes pre-anesthesia difficult-airway assessment and proposes evidence-based plans. The ontology models patient descriptors, predictive scores, and airway techniques. The system computes the ARNE score from recorded findings, applies an expert-curated mapping to provide appropriate techniques recommendations. The pipeline was evaluated on 20 simulated cases covering typical and edge presentations with 80% agreement on priority intubation technique. The system executed end-to-end for all cases, producing consistent, reasoner-validated outputs. In conclusion, these findings indicate that transparent, institution-aware decision support for airway planning is feasible and aligned with clinical realities.

Source: Studies in health technology and informatics

DOI: 10.3233/shti260181

Authors: Akram Redjdal, Adrien Ugon, Fahira Abdoulaime, Lehna Bouchama’, Antoine Ritz, Eve Bensoussan, Nolan Lestoclet-Yansens, Julien Bordes, Salah Boussen

Institutions: Aix-Marseille Université, Université Gustave Eiffel, ESIEE Paris, Hôpital d'Instruction des Armées Sainte-Anne

Language: English

Topic: Airway Management and Intubation Techniques

Subfield: Anesthesiology and Pain Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W7161948548

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