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Development and Validation of an Artificial Intelligence System for Anatomic Site Recognition and Lesion Detection Based on Electronic Nasopharyngolaryngoscopic Images: A Prospective Multicenter Study
An artificial intelligence-assisted system is trained and validated by collecting nasopharyngolaryngoscopy images from patients.
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.
Age
18 - No limit years
Sex
ALL
Healthy Volunteers
No
Ruijin Hospital, Shanghai Jiao Tong University School of Medicine
Shanghai, China
Start Date
December 12, 2025
Primary Completion Date
December 31, 2026
Completion Date
March 31, 2027
Last Updated
January 8, 2026
500
ESTIMATED participants
Diagnostic
OTHER
Diagnostic
OTHER
Lead Sponsor
Ruijin Hospital
Data Source & Attribution
This clinical trial information is sourced from ClinicalTrials.gov, a service of the U.S. National Institutes of Health.
Modifications: This data has been reformatted for display purposes. Eligibility criteria have been parsed into inclusion/exclusion sections. Location data has been geocoded to enable distance-based search. For the authoritative and most current information, please visit ClinicalTrials.gov.
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View ClinicalTrials.gov Terms and ConditionsNCT06316063