Real-time augmentation of diagnostic nasal endoscopy video using AI-enabled edge computing.

KEY POINTS: AI-enabled augmentation of nasal endoscopy video images is feasible in the clinical setting. Edge computing hardware can interface with existing nasal endoscopy equipment. Real-time AI performance can achieve an acceptable balance of accuracy and efficiency.

Enhancing nasal endoscopy: Classification, detection, and segmentation of anatomic landmarks using a convolutional neural network.

KEY POINTS: A convolutional neural network (CNN)-based model can accurately localize and segment turbinates in images obtained during nasal endoscopy (NE). This model represents a starting point for algorithms that comprehensively interpret NE findings.