arXiv:2610.20623v1 Announce Type: new Abstract: Endoscopy is widely used in medicine to visualize anatomical structures for diagnosis and treatment evaluation. However, it does not currently allow rapid, quantitative measurement of airway cross-sectional area (CSA), a key metric for diagnosing and managing respiratory pathologies. There is growing interest in AI-enabled quantitative endoscopy using depth estimation, but a fundamental challenge is the need for ground-truth depth data to train algorithms capable of quantifying depth in physical units (e.g., meters). In this study, 60,000 paired virtual endoscopy images and depth maps were created in Blender from 3D airway models built from computed tomography. A U-Net convolutional neural network was trained to predict the depth maps from the endoscopy images. Point clouds were reconstructed from the AI-generated depth maps using the Equisolid fisheye lens model, and airway perimeters were identified by intersecting a plane with the point cloud to estimate CSA. Nasopharyngeal CSA, a key metric in obstructive sleep apnea, was quantified using this AI-enabled approach with a median error of 5.3 \% relative to the 3D model ground-truth. This pilot study using synthetic data provides strong evidence for the feasibility of developing AI-enabled quantitative endoscopy for airway CSA measurement.