Automated measurement of hydrops ratio from MRI in patients with Ménière’s disease using CNN-based segmentation

Young Sang Cho, Kyeongwon Cho, Chae Jung Park, Myung Jin Chung, Jong Hyuk Kim, Kyunga Kim, Yi Kyung Kim, Hyung Jin Kim, Jae Wook Ko, Baek Hwan Cho, Won Ho Chung

Research output: Contribution to journalArticlepeer-review

Abstract

Ménière’s Disease (MD) is difficult to diagnose and evaluate objectively over the course of treatment. Recently, several studies have reported MD diagnoses by MRI-based endolymphatic hydrops (EH) analysis. However, this method is time-consuming and complicated. Therefore, a fast, objective, and accurate evaluation tool is necessary. The purpose of this study was to develop an algorithm that can accurately analyze EH on intravenous (IV) gadolinium (Gd)-enhanced inner-ear MRI using artificial intelligence (AI) with deep learning. In this study, we developed a convolutional neural network (CNN)-based deep-learning model named INHEARIT (INner ear Hydrops Estimation via ARtificial InTelligence) for the automatic segmentation of the cochlea and vestibule, and calculation of the EH ratio in the segmented region. Measurement of the EH ratio was performed manually by a neuro-otologist and neuro-radiologist and by estimation with the INHEARIT model and were highly consistent (intraclass correlation coefficient = 0.971). This is the first study to demonstrate that automated EH ratio measurements are possible, which is important in the current clinical context where the usefulness of IV-Gd inner-ear MRI for MD diagnosis is increasing.

Original languageEnglish
Article number7003
JournalScientific Reports
Volume10
Issue number1
DOIs
StatePublished - 1 Dec 2020

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