Detecting representative characteristics of different genders using intraoral photographs: a deep learning model with interpretation of gradient-weighted class activation mapping

Abstract Background Sexual dimorphism is obvious not only in the overall architecture of human body, but also in intraoral details. Many studies have found a correlation between gender and morphometric features of teeth, such as mesio-distal diameter, buccal-lingual diameter and height. However, it’s still difficult to detect gender through the observation of intraoral photographs, with accuracy around 50%. The purpose of this study was to explore the possibility of automatically telling gender from intraoral photographs by deep neural network, and to provide a novel angle for individual oral treatment. Methods A deep learning model based on R-net was proposed, using the largest dataset (10,000 intraoral images) to support the automatic detection of gender. In order to reverse analyze the classification basis of neural network, Gradient-weighted Class Activation Mapping (Grad-CAM) was used in the second step, exploring anatomical factors associated with gender recognizability. The simulated modification of images based on features suggested was then conducted to verify the importance of characteristics between two genders. Precision (specificity), recall (sensitivity) and receiver operating characteristic (ROC) curves were used to evaluate the performance of our network. Chi-square test was used to evaluate intergroup difference. A value of p