Behavior recognition technology based on deep learning used in pediatric behavioral audiometry

This study aims to explore the feasibility and accuracy of deep learning-based pediatric behavioral audiometry. The research provides a dedicated pediatric posture detection dataset, which contains a large number of video clips from children's behavioral hearing tests, encompassing various typical hearing test actions. A detection platform based on this dataset is also constructed, named intelligent diagnostic model of pediatric hearing based on optimized transformer (DoT); further, an estimation model of patient skeletal keypoints based on optimized transformer (POTR) was proposed to estimate human skeleton points. Based on this, the DoT approach was handled to perform posture recognition on videos of children undergoing behavioral hearing tests, thus enabling an automated hearing testing process. Through this platform, children's movements can be monitored and analyzed in real-time, allowing for the assessment of their hearing levels. Moreover, the study establishes decision rules based on specific actions, combining professional knowledge and experience in audiology to evaluate children's hearing levels based on their movement status. Firstly, we gathered image and video data related to posture in the process of conditioned play audiometry to test the hearing of 120 children aged 2.5 to 6 years old. Next, we built and optimized a deep learning model suitable for pediatric posture recognition. Finally, in the deployment and application phase, we deployed the trained pediatric posture recognition model into real-world application environments. We found that for children aged 2.5 - 4 years, the sensitivity of artificial behavior audiometry (0.900) was not as high as that of AI behavior audiometry (0.929), but the specificity of artificial behavior audiometry (0.824) and Area Under Curve (AUC) (0.901) was higher than that of AI behavior audiometry. For children aged 4-6 years, the sensitivity (0.943), specificity (0.947), and AUC (0.924) of artificial behavioral audiometry were higher than those of AI behavioral audiometry. The application of these rules facilitates objective assessment and diagnosis of children's hearing, providing essential foundations for early screening and treatment of children with hearing disorders.Trial Registration: Chinese Clinical Trial Registry: Registration number ChiCTR2100050416.