Neoplasms in the Nasal Cavity Identified and Tracked with an Artificial Intelligence-Assisted Nasal Endoscopic Diagnostic System.

ABSTRACT:

Objective

We aim to construct an artificial intelligence (AI)-assisted nasal endoscopy diagnostic system capable of preliminary differentiation and identification of nasal neoplasia properties, as well as intraoperative tracking, providing an important basis for nasal endoscopic surgery.

Methods

We retrospectively analyzed 1050 video data of nasal endoscopic surgeries involving four types of nasal neoplasms. Using Deep Snake, U-Net, and Att-Res2-UNet, we developed a nasal neoplastic detection network based on endoscopic images. After deep learning, the optimal network was selected as the initialization model and trained to optimize the SiamMask online tracking algorithm.

Results

The Att-Res2-UNet network demonstrated the highest accuracy and precision, with the most a

SUBMITTER: Xu X 

PROVIDER: S-EPMC11762390 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature