The usefulness of inner ear magnetic resonance imaging in patient with Ménière’s disease: A narrative review

Abstract

Ménière’s disease (MD) is a multifactorial disorder with typical symptoms of recurrent vertigo, tinnitus, fluctuating hearing loss, and sensations of ear fullness. This disease greatly reduces the quality of life for patients. Unfortunately, it is difficult to diagnose and predictthe prognosis using only diagnostic methods, including audiometry. Therefore, since the mid-2000s, various efforts have been made to directly identify endolymphatic hydrops (EH), a histologic hallmark of MD, through magnetic resonance imaging (MRI) of the inner ear. Various studies have revealed significant correlation among degree of EH on inner ear MRI, patient symptoms, and test results. Although there are some limitations, inner ear MRI is expected to be widely used for differential diagnosis of MD, recurrent low-frequency hearing loss, non-specific vertigo, and vestibular migraine. In addition, as an automated analysis system of EH using the convolutional neural network algorithm has been developed,the usefulness of inner ear MRI is increasing. This algorithm can generate results that are highly consistent with those generated by manual calculation and can do so more quickly. Although there are some limitations to be overcome, inner ear MRI is expected to be widely used for differential diagnosis of various EH-related diseases in the not-too-distantfuture.

Keywords

• artificial intelligence
• deep learning
• magnetic resonance imaging
• meniere disease
• vertigo