Gender, Smoking History and Age Prediction from Laryngeal Images
This work addresses the time-consuming manual data entry for clinicians in otolaryngology by automating demographic prediction to enhance diagnostic models, though it is incremental as it builds on existing machine learning techniques in medical imaging.
The study tackled the problem of manually entering patient demographic data for laryngeal disease diagnosis by using deep learning to predict gender, smoking history, and age from laryngeal images, achieving accuracies of 85.5%, 65.2%, and 75.9%, respectively.
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.