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.

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.

Image data of patients who underwent nasopharyngolaryngoscopy and met the research requirements were collected from various sub-centers nationwide.