30/04/2026
AI in FEES is moving beyond detection, and into something much more clinically meaningful!
This paper (Araújo et al, 2926) presents a framework that combines anatomical tracking, image enhancement, and pixel-based analysis to objectively identify airway invasion and classify pharyngeal residue.
What’s most interesting isn’t just the accuracy, it’s the shift toward standardising visuoperceptual analysis.
Because we all know:
Two clinicians can look at the same FEES and interpret it differently.
This kind of technology doesn’t replace clinical reasoning, but it has the potential to support it by making our observations more consistent, reproducible, and transparent.
Particularly relevant for:
• clinician training
• consistency across services
• supporting complex decision-making
Still early, but definitely a space worth watching!
REFERENCE (APA)
Araújo, L., Rangel, E., Cotrina-Atencio, A., Santos, V. G., Reis, A. M. C. S., Magalhães, H., Ferreira, L., Dantas, A. F. O. A., & Espírito-Santo, C. C. (2026). Artificial intelligence and image processing framework for automated airway invasion detection and residue classification from swallowing endoscopy. Scientific Reports. https://doi.org/10.1038/s41598-026-44495-4