Gender, Smoking History and Age Prediction from Laryngeal Images
Tianxiao Zhang Andrés M. Bur Shannon Kraft Hannah Kavookjian Bryan Renslo Xiangyu Chen Bo Luo Guanghui Wang
Flexible laryngoscopy is commonly performed by otolaryngologists to detect
laryngeal diseases and to recognize potentially malignant lesions. Recently,
researchers have introduced machine learning techniques to facilitate automated
diagnosis using laryngeal images and achieved promising results. Diagnostic
performance can be improved when patients' demographic information is
incorporated into models. However, manual entry of patient data is time
consuming for clinicians. In this study, we made the first endeavor to employ
deep learning models to predict patient demographic information to improve
detector model performance. The overall accuracy for gender, smoking history,
and age was 85.5%, 65.2%, and 75.9%, respectively. We also created a new
laryngoscopic image set for machine learning study and benchmarked the
performance of 8 classical deep learning models based on CNNs and Transformers.
The results can be integrated into current learning models to improve their
performance by incorporating the patient's demographic information.