Abstract
Meniere’s Disease (MD) is a complex, multifactorial inner ear disorder characterized by episodes of spontaneous vertigo, unilateral fluctuating sensorineural hearing loss, aural fullness, and tinnitus. Although endolymphatic hydrops (EH) is often considered a histopathological hallmark of MD, the 2015 diagnostic guidelines emphasize that its presence is not essential for diagnosis. Magnetic Resonance Imaging (MRI) has emerged as a valuable tool for detecting EH, though it remains in a developmental phase. Recently, artificial intelligence (AI)—a rapidly advancing field that simulates human cognitive processes—has garnered significant attention in the study of MD. This paper reviews the current literature on the application of AI and deep learning in the diagnosis, monitoring, and treatment of Meniere’s Disease. Our review encompasses seven relevant studies sourced from PubMed, Scopus, Web of Science, and ScienceDirect. Among these, four articles focus on the use of MRI to detect and quantify endolymphatic hydrops. We present these findings within the context of a development trajectory, discuss the limitations of current methodologies, and outline potential avenues for future advancements.
Published Version
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