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Explainable Knowledge-Guided Algorithm for Contrast Extravasation Detection on Computed Tomography
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DOI：10.1109/jtehm.2026.3681662.png)
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Abstract
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Objective: To develop an explainable, knowledge-guided framework for automated detection of contrast media extravasation from sequential computed tomography (CT) images and to evaluate its potential to accelerate time-critical trauma triage while maintaining clinically acceptable sensitivity.Methods: A mathematical framework was formulated to explicitly encode three expert-derived diagnostic rules: 1) progressive increase of contrast outside anatomically plausible vessels, 2) appearance of contrast in non-vascular regions, and 3) localized irregularity of vessel caliber. Sequential two-dimensional CT slices were analyzed using a 2.5D formulation integrating temporal intensity evolution, anatomical plausibility, vessel morphology, and inter-slice continuity. The model outputs a confidence score and a binary alert. Model parameters and decision thresholds were initialized using a single representative clinical case guided by expert interpretation. Performance was evaluated against senior emergency surgeon assessment, emphasizing sensitivity and time-to-decision.Results: The proposed framework achieved clinically acceptable sensitivity for detection of contrast extravasation while substantially reducing time-to-decision relative to manual review. Early-trigger analysis demonstrated that positive cases were identified within the initial portion of the CT volume, supporting rapid screening and prioritization in emergency workflows.Conclusion: This study demonstrates the feasibility of translating expert clinical reasoning into an interpretable computational model for time-critical imaging tasks. The knowledge-guided design enables rapid automated screening while preserving transparency and clinician oversight. The framework shows promise as a decision-support tool for accelerating trauma triage, with future work focused on prospective validation and broader multi-center evaluation. Clinical Impact: The proposed knowledge-guided algorithm enables rapid extravasation alerts on trauma CT, supporting earlier triage and prioritization for angiography or surgery within existing emergency imaging workflows.
Keywords:
Trauma
contrast media extravasation
computed tomography
clinical knowledge
knowledge-guided detection.
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