Artificial Intelligence-Based Diagnostic Methods for Otitis Media: A Review Paper

Otitis media (OM) is a common inflammatory condition, particularly in children, and poses significant diagnostic and treatment challenges. It also significantly affects adults, especially those with atopic conditions. OM can lead to complications such as hearing loss and speech delays. The advent of electronic health records, big data, and artificial intelligence (AI) offers transformative opportunities in OM diagnosis. AI plays a crucial role in enabling early detection and enhancing diagnostic precision through advanced image analysis and predictive modeling. This review paper explores modern techniques used for the diagnosis of OM, with an emphasis on both traditional and machine learning approaches. A wide range of studies has been evaluated, demonstrating the application of AI in improving diagnostic accuracy and treatment planning. Notably, AI approaches—particularly deep neural networks—have shown remarkable success in otoscopy image analysis. Additionally, recently developed hybrid models that combine multiple techniques have outperformed individual approaches. Despite these advancements, challenges remain, including limited dataset standardization and issues with image quality.