Artificial intelligence for automatic detection and segmentation of nasal polyposis: a pilot study.

Vittorio RampinelliAlberto PadernoCarlo ContiGabriele TestaClaudia Lodovica ModestiEdoardo AgostiIsabelle DohinTommaso SaccardoAlessandro VinciguerraMarco FerrariAlberto SchreiberDavide MattavelliPiero NicolaiChris HolsingerCesare Piazza

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 study shows that a carefully trained AI algorithm can effectively identify and delineate nasal polyps in patients with CRSwNP. Despite certain limitations like the focus on CRSwNP-specific samples, the algorithm presents a promising complementary tool to existing diagnostic methods.

Keyphrases

artificial intelligence
deep learning
chronic rhinosinusitis
machine learning
big data
convolutional neural network
resistance training
body composition
quantum dots
sensitive detection
high intensity