Artificial Intelligence-driven Advancements in Otitis Media Diagnosis: A Systematic Review

Otitis Media (OM), predominantly affecting children, is a significant global health issue, with an estimated 360 million pediatric cases yearly worldwide. OM causes mild and moderate conductive hearing loss which can be disabling for young children, particularly during the first three years of life when brain growth is rapid, resulting in poor speech and language development, poor communication skills, and increased vulnerability on entering school. OM therefore contributes to the global burden of all-cause hearing loss. This systematic review seeks to provide a comprehensive evaluation of pre-trained Artificial Intelligence (AI) models, including both classical Machine Learning (ML) and Deep Learning (DL), in the context of OM. This review proposes six research questions, and it summarizes the body of research across multiple domains, including the diversity and quantity of source material for training and testing models, including otoscopy images, videos, and tympanometry, and the methods used to assess quality and effectiveness in real-time settings. In addition, the review aims to provide insight into the impact and potential of AI in improving OM diagnosis and cast light on the existing challenges, such as model interpretability, limited medical expert involvement, and the need for knowledge discovery and unanswered questions, including the evolving landscape of OM diagnosis within this domain. The findings of this systematic review emphasize the importance of developing more interpretable AI models that incorporate both still images of the tympanic membrane and video recordings (with multiple frames) to maximize sensitivity and specificity of the model. In addition, collaboration with consumers and medical professionals in multiple specialties (general practice, pediatricians, audiologists and ear, nose, throat (ENT) surgeons) is needed to ensure applicability and confidence of these diagnostic digital support systems in real-world healthcare settings.