Machine Learning of Endoscopy Images to Identify, Classify, and Segment Sinonasal Masses.

ABSTRACT:

Background

We developed and assessed the performance of a machine learning model (MLM) to identify, classify, and segment sinonasal masses based on endoscopic appearance.

Methods

A convolutional neural network-based model was constructed from nasal endoscopy images from patients evaluated at an otolaryngology center between 2013 and 2024. Images were classified into four groups: normal endoscopy, nasal polyps, benign, and malignant tumors. Polyps and tumors were confirmed with histopathological diagnosis. Images were annotated by an otolaryngologist and independently verified by two other otolaryngologists. We used high- and low-quality images to mirror real-world conditions. The models used for classification (EfficientNet-B2) and segmentation (nnUNet) were trained, validat

SUBMITTER: Levi L 

PROVIDER: S-EPMC12048764 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature