Summary
The pharynx is one of the few areas in the body where blood vessels and immune tissues can readily be observed from outside the body noninvasively. Although prior studies have found that sex could be identified from retinal images using artificial intelligence, it remains unknown as to whether individuals’ sex could also be identified using pharyngeal images. Demographic information and pharyngeal images were collected from patients who visited 64 primary care clinics in Japan for influenza-like symptoms. Authors trained a deep learning-based classification model to predict reported sex, which incorporated a multiple instance convolutional neural network, on 20,319 images from 51 clinics.
Findings: The study demonstrated the potential utility of pharyngeal images as a new modality in medical imaging by identifying reported sex from pharyngeal images using deep learning. Authors discovered that the algorithm focuses on the posterior pharyngeal wall and the uvula. This approach allowed authors to provide quantitative insights into the differences between males and females in the uvula and posterior wall.