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.
Study summary
An artificial intelligence-assisted system is trained and validated by collecting nasopharyngolaryngoscopy images from patients.
Eligibility
Sex
ALL
Min age
18 Years
Max age
—
Healthy volunteers
No
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.
Primary outcome measure(s)
performance of lesion detection — 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 — Within 3 months after the completion of prospective data collection The average precision (AP) of the model for recognizing nasopharyngeal and laryngeal anatomic sites
Trial sites (1)
Facility
City
Region
Status
Ruijin Hospital, Shanghai Jiao Tong University School of Medicine
This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
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