Date Thesis Awarded


Access Type

Honors Thesis -- Access Restricted On-Campus Only

Degree Name

Bachelors of Science (BS)




Ran Yang

Committee Members

Hannes Schniepp

Jeffery Nelson


This thesis introduces a video laryngoscope with improved visibility and an AI guidance system. The laryngoscope is equipped with a camera and can automatically stream live video taken during intubation to a server through WiFi that is accessible to any device connected to the same WiFi station (router, hotspot, etc.). It is also compatible with devices with Bluetooth Low Energy (BLE) and can thus establish a paired communication with most of the electronic devices on the market. The backend of the AI guidance system is a neural network trained for image segmentation. Our most recent model uses the CCNet architecture to identify the vocal folds in the laryngoscope’s video feed.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Available for download on Friday, May 06, 2033

On-Campus Access Only