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
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Bin Ye, MD PhD
- Phone Number: +8615216616895
- Email: aydyebin@126.com
Study Locations
-
-
-
Shanghai, China
- Recruiting
- Ruijin Hospital, Shanghai Jiao Tong University School of Medicine
-
Contact:
- Bin Ye, MD PhD
- Phone Number: +8615216616895
- Email: aydyebin@126.com
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- Age ≥ 18 years;
- Underwent standard electronic nasopharyngolaryngoscopy;
- Patients who underwent biopsy sampling have a clear pathological diagnosis;
- Signed a written informed consent form.
Exclusion Criteria:
- Image quality is substandard with severe motion artifacts;
- Lesion images are unclear and incomplete.
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
Model training and validation cohorts
A deep learning model is trained using the training dataset and validated with the internal validation set.
|
The deep learning model is trained using the training dataset and tested with the internal validation set.
The prospective dataset is used for the comparative testing of the model and physicians.
|
|
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.
|
The deep learning model is trained using the training dataset and tested with the internal validation set.
The prospective dataset is used for the comparative testing of the model and physicians.
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
performance of lesion detection
Time Frame: Within 3 months after the completion of prospective data collection
|
The area under the receiver operating characteristic curve (ROC-AUC) of the model for abnormal lesion detection
|
Within 3 months after the completion of prospective data collection
|
|
performance of anatomic site recognition
Time Frame: Within 3 months after the completion of prospective data collection
|
The average precision (AP) of the model for recognizing nasopharyngeal and laryngeal anatomic sites
|
Within 3 months after the completion of prospective data collection
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Comparison of diagnostic performance between the model and physicians
Time Frame: Within 3 months after the completion of prospective data collection
|
Differences in sensitivity, specificity, and overall accuracy between the AI model and endoscopists with different years of experience
|
Within 3 months after the completion of prospective data collection
|
Collaborators and Investigators
Sponsor
Sponsor
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- 2025-811
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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