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

Area of Science:

• Otolaryngology
• Medical Imaging
• Artificial Intelligence

Background:

• Nasal endoscopy is crucial for diagnosing sinonasal diseases.
• Current visualization methods have limitations.
• AI offers potential for image enhancement.

Purpose of the Study:

• To assess the feasibility of AI-enabled augmentation for nasal endoscopy video images.
• To evaluate the integration of edge computing with existing endoscopy equipment.
• To determine the real-time performance of AI in this clinical application.

Main Methods:

• Development and implementation of an AI algorithm for video augmentation.
• Integration of edge computing hardware with standard nasal endoscopy systems.
• Clinical testing to evaluate accuracy and processing speed.

Main Results:

• AI augmentation of nasal endoscopy videos is clinically feasible.
• Edge computing successfully interfaced with existing nasal endoscopy equipment.
• Real-time AI processing achieved a satisfactory balance between accuracy and efficiency.

Conclusions:

• AI-powered enhancement of nasal endoscopy videos is a viable clinical tool.
• Edge computing provides a practical solution for integrating AI into current workflows.
• The developed AI system demonstrates effective real-time performance for clinical use.