Nasopharyngeal NeoplasmsNCT07326358
AI System for Anatomic Recognition & Lesion Detection in Nasopharyngolaryngoscopy: A Prospective Study
An observational study of Nasopharyngeal Neoplasms and Laryngeal Disease, run by Ruijin Hospital at 1 site in China.
Trial summary
An artificial intelligence-assisted system is trained and validated by collecting nasopharyngolaryngoscopy images from patients. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Nasopharyngeal Neoplasms, Laryngeal Disease
- What is being tested?
- Diagnostic
- How many people
- 500 participants
- Where
- 1 siteChina
- Study dates
- 12 Dec 2025to 31 Mar 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Image data of patients who underwent nasopharyngolaryngoscopy and met the research requirements were collected from various sub-centers nationwide.
Who may be able to join
- Age ≥ 18 years;
- Underwent standard electronic nasopharyngolaryngoscopy;
- Patients who underwent biopsy sampling have a clear pathological diagnosis;
- Signed a written informed consent form.
Who cannot join
- Image quality is substandard with severe motion artifacts;
- Lesion images are unclear and incomplete.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
Ruijin Hospital, Shanghai Jiao Tong University School of Medicine
Shanghai
Bin Ye, MD PhD, +8615216616895, aydyebin@126.com
About this study
Official title: 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.
Read the 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
- Being tested
- Diagnostic
- Medical subject headings
- Nasopharyngeal Neoplasms, Laryngeal Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
DiagnosticOther
The deep learning model is trained using the training dataset and tested with the internal validation set.
DiagnosticOther
The prospective dataset is used for the comparative testing of the model and physicians.
| Study group | Role | What they receive |
|---|---|---|
| Model training and validation cohorts | A deep learning model is trained using the training dataset and validated with the internal validation set. Receives: Other: Diagnostic | |
| 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. Receives: Other: Diagnostic |
What is being measured
The primary outcome is the main question the study is designed to answer.
performance of lesion detection
The area under the receiver operating characteristic curve (ROC-AUC) of the model for abnormal lesion detection
Measured: Within 3 months after the completion of prospective data collection
performance of anatomic site recognition
The average precision (AP) of the model for recognizing nasopharyngeal and laryngeal anatomic sites
Measured: Within 3 months after the completion of prospective data collection
Secondary outcomes (1)
Comparison of diagnostic performance between the model and physicians
Differences in sensitivity, specificity, and overall accuracy between the AI model and endoscopists with different years of experience
Measured: Within 3 months after the completion of prospective data collection
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Jan 2026
- Study start12 Dec 2025
- Primary completion31 Dec 2026 (estimated)
- Study completion31 Mar 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Nasopharyngeal NeoplasmsStudies of the same condition, open and under way.
- More trials run by Ruijin HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Other
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Ruijin Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07326358
- Sponsor's protocol ID
- 2025-811
- Status last verified
- Dec 2025
- First submitted
- 26 Dec 2025
- Last updated
- 8 Jan 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 8 Jan 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of an Artificial Intelligence System for Anatomic Site Recognition and Lesion Detection Based on Electronic Nasopharyngolaryngoscopic Images: A Prospective Multicenter Study
- ClinicalTrials.gov ID
- NCT07326358
- Acronym / sponsor ID
- 2025-811
- Conditions
- Nasopharyngeal Neoplasms, Laryngeal Disease
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- Ruijin HospitalUniversity, hospital or other
- Study start
- 12 Dec 2025 (actual)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 31 Mar 2027 (estimated)
- First posted
- 8 Jan 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Other
- Time perspective
- Other
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Model training and validation cohorts | – | Other: DiagnosticA deep learning model is trained using the training dataset and validated with the internal validation set. |
| Prospective test cohort | – | Other: DiagnosticPatients 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Image data of patients who underwent nasopharyngolaryngoscopy and met the research requirements were collected from various sub-centers nationwide.
Key inclusion criteria (4 listed)
- Age ≥ 18 years;
- Underwent standard electronic nasopharyngolaryngoscopy;
- Patients who underwent biopsy sampling have a clear pathological diagnosis;
- Signed a written informed consent form.
Key exclusion criteria (2 listed)
- Image quality is substandard with severe motion artifacts;
- Lesion images are unclear and incomplete.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| DiagnosticThe deep learning model is trained using the training dataset and tested with the internal validation set. | Other | Model training and validation cohorts |
| DiagnosticThe prospective dataset is used for the comparative testing of the model and physicians. | Other | Prospective test cohort |
Primary outcome
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
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
1 secondary outcome
Comparison of diagnostic performance between the model and physicians
Time frame: Within 3 months after the completion of prospective data collection
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Jan 2026.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Nasopharyngeal Neoplasms and Laryngeal Disease, run by Ruijin Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 8 Jan 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Bin Ye, MD PhD (+8615216616895, aydyebin@126.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
performance of lesion detection (time frame: Within 3 months after the completion of prospective data collection). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 8 Jan 2026.