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Flexible laryngoscopy is commonly performed by otolaryngologists to detectlaryngeal diseases and to recognize potentially malignant lesions. Recently,researchers have introduced machine learning techniques to facilitate automateddiagnosis using laryngeal images and achieved promising results. Diagnosticperformance can be improved when patients' demographic information isincorporated into models. However, manual entry of patient data is timeconsuming for clinicians. In this study, we made the first endeavor to employdeep learning models to predict patient demographic information to improvedetector model performance. The overall accuracy for gender, smoking history,and age was 85.5%, 65.2%, and 75.9%, respectively. We also created a newlaryngoscopic image set for machine learning study and benchmarked theperformance of 8 classical deep learning models based on CNNs and Transformers.The results can be integrated into current learning models to improve theirperformance by incorporating the patient's demographic information.

Gender, Smoking History and Age Prediction from Laryngeal Images