NCT05627310

Development and Validation of a Deep Learning System for Nasopharyngeal Carcinoma Using Endoscopic Images

Development and Validation of a Deep Learning System for Nasopharyngeal Carcinoma Using Endoscopic Images: a Multi-center Prospective Study

• Status: Unknown
• Phase: —
• Study type: Observational
• Enrollment: 50,000 (estimated)
• Sponsor: Eye & ENT Hospital of Fudan University · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Not accepted

Summary

Develop a deep learning algorithm via nasal endoscopic images from eight NPC treatment centerto detect and screen nasopharyngeal carcinoma(NPC).

Detailed description

Nasopharyngeal carcinoma (NPC) is an epithelial cancer derived from nasopharyngeal mucosa. Nasal endoscopy is the conventional examination for NPC screening. It is a major challenge for inexperienced endoscopists to accurately distinguish NPC and other benign dieseases. In this study, we collcet multi-center endoscopic images and train a deep learning model to detect NPC and indicate tumor location. Then, the model perfomance will be compared with endoscopists and be tested prospectively with external dataset.

Conditions

• Nasopharyngeal Carcinoma

Interventions

Type	Name	Description
OTHER	Diagnostic	Training dataset was used to train the deep learning model, which was validated and tested by external dataset.

Timeline

• Start date: 2022-11-01
• Primary completion: 2023-12-31
• Completion: 2024-03-31
• First posted: 2022-11-25
• Last updated: 2022-11-25

Locations

8 sites across 1 country: China