An automated approach for real-time informative frames classification in laryngeal endoscopy using deep learning.

Chiara BaldiniMuhammad Adeel AzamClaudio SampieriAlessandro IoppiLaura Ruiz-SevillaIsabel VilasecaBerta AlegreAlessandro TirritoAlessia PennacchiGiorgio PerettiSara MocciaLeonardo S Mattos

Published in: European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery (2024)

The deep learning model demonstrated excellent performance in identifying diagnostically relevant frames within laryngoscopic videos. With its solid accuracy and real-time capabilities, the system is promising for its development in a clinical setting, either autonomously for objective quality control or in conjunction with other algorithms within a comprehensive AI toolset aimed at enhancing tumor detection and diagnosis.

Keyphrases

deep learning
quality control
artificial intelligence
machine learning
convolutional neural network
loop mediated isothermal amplification
real time pcr