NCT07326358

AI System for Anatomic Recognition & Lesion Detection in Nasopharyngolaryngoscopy: A Prospective Study

Development and Validation of an Artificial Intelligence System for Anatomic Site Recognition and Lesion Detection Based on Electronic Nasopharyngolaryngoscopic Images: A Prospective Multicenter Study

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 500 (estimated)
• Sponsor: Ruijin Hospital · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

An artificial intelligence-assisted system is trained and validated by collecting nasopharyngolaryngoscopy images from patients.

Detailed description

To address the clinical pain points of traditional nasopharyngolaryngoscopy, such as incomplete visualization, inaccurate identification, and unclear imaging, this study will retrospectively collect nasopharyngolaryngoscopy images and baseline information (including gender and age) of patients who underwent nasopharyngolaryngoscopy at participating centers for model training and validation. Deep learning algorithms will be applied to construct the model. The final clinical performance evaluation of the model will be conducted using an independent, prospectively collected test cohort.

Conditions

• Nasopharyngeal Neoplasms
• Laryngeal Disease

Interventions

Type	Name	Description
OTHER	Diagnostic	The deep learning model is trained using the training dataset and tested with the internal validation set.
OTHER	Diagnostic	The prospective dataset is used for the comparative testing of the model and physicians.

Timeline

• Start date: 2025-12-12
• Primary completion: 2026-12-31
• Completion: 2027-03-31
• First posted: 2026-01-08
• Last updated: 2026-01-08

Locations

1 site across 1 country: China