The Role of Artificial Intelligence and Automation in the Diagnosis and Management of Otitis Media

Abstract

BACKGROUND: Otitis Media (OM) is one of the most prevalent pediatric infectious diseases often comprising two common diagnoses: acute otitis media (AOM) and otitis media with effusion (OME). The current guidelines on managing OM have emphasized the need to accurately diagnose fluid presence in the ear, and the latest methods for OM diagnosis are often inaccurate or under-utilized. This frequently results in over-usage of antibiotics for AOM or misdiagnosis of middle ear effusion in OME. Emerging technologies including leveraging artificial intelligence (AI) and automation to improve OM diagnosis are likely to play a significant role for clinicians into the future. As a specific example, optical coherence tomography (OCT), capable of imaging body cavities and tissue, has recently been introduced as a promising technology for the accurate diagnosis of OM. This study was conducted to capture the current state of advances in automation and technological innovations in OM diagnosis. METHODS: A qualitative systematic review of PubMed, Scopus, and Google Scholar was conducted to analyze the role of AI and automation in the diagnosis and management of OM. Articles were reviewed for eligibility and 41 studies met inclusion criteria. RESULTS: Across studies, use of AI has shown promising progress toward more accurate diagnosis of AOM. Clinicians can use AI and obtain reliable imaging focused on AOM and OME, aiding in both management and diagnosis. Applications of OCT have provided for high- quality detection, diagnostics, and monitoring prognosis of OM. Smartphone attachments employing acoustic reflectance have recently demonstrated great potential for cost-effective and remote OM diagnosis. Although smartphone otoscopes are available for remote use by parents, there remains risk for discrepancies when compared to data acquired by physicians. CONCLUSION: Through the development of an automated diagnostic algorithm for OM, AI and automation technologies would provide several benefits to OM patients worldwide. This cost-effective approach can account for early detection of OM in pediatric patients, assist parents in deciding whether their children need professional help, and aid clinicians in managing OM with potentially more reliability than current technologies.