AI System for Anatomic Recognition & Lesion Detection in Nasopharyngolaryngoscopy: A Prospective Study
NCT ID: NCT07326358
Last Updated: 2026-01-08
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
500 participants
OBSERVATIONAL
2025-12-12
2027-03-31
Brief Summary
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Detailed Description
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Conditions
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Study Design
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OTHER
OTHER
Study Groups
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Model training and validation cohorts
A deep learning model is trained using the training dataset and validated with the internal validation set.
Diagnostic
The deep learning model is trained using the training dataset and tested with the internal validation set.
Prospective test cohort
Patients are prospectively enrolled, nasopharyngolaryngoscopy examination videos are collected, and the video data are processed to form a prospective test dataset, which is then used for testing.
Diagnostic
The prospective dataset is used for the comparative testing of the model and physicians.
Interventions
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Diagnostic
The deep learning model is trained using the training dataset and tested with the internal validation set.
Diagnostic
The prospective dataset is used for the comparative testing of the model and physicians.
Eligibility Criteria
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Inclusion Criteria
* Underwent standard electronic nasopharyngolaryngoscopy;
* Patients who underwent biopsy sampling have a clear pathological diagnosis;
* Signed a written informed consent form.
Exclusion Criteria
* Lesion images are unclear and incomplete.
18 Years
ALL
No
Sponsors
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Ruijin Hospital
OTHER
Responsible Party
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Locations
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Ruijin Hospital, Shanghai Jiao Tong University School of Medicine
Shanghai, , China
Countries
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Central Contacts
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Facility Contacts
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Other Identifiers
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2025-811
Identifier Type: -
Identifier Source: org_study_id
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