Automated measurement of hydrops ratio from MRI in patients with Ménière’s disease using CNN-based segmentation
| Autor: | Kyunga Kim, Jong Hyuk Kim, Myung Jin Chung, Baek Hwan Cho, Yikyung Kim, Jae-Wook Ko, Kyeongwon Cho, Hyung-Jin Kim, Chae Jung Park, Won-Ho Chung, Young Sang Cho |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Intraclass correlation
Computer science lcsh:Medicine Gadolinium Context (language use) Convolutional neural network Article 030218 nuclear medicine & medical imaging 03 medical and health sciences Magnetic resonance imaging 0302 clinical medicine Artificial Intelligence Inner ear medicine Humans Segmentation Medical diagnosis Endolymphatic hydrops lcsh:Science Meniere Disease Multidisciplinary business.industry Deep learning lcsh:R Pattern recognition medicine.disease lcsh:Q Artificial intelligence business Algorithms Neurological disorders 030217 neurology & neurosurgery Meniere's disease |
| Zdroj: | Scientific Reports, Vol 10, Iss 1, Pp 1-10 (2020) Scientific Reports |
| ISSN: | 2045-2322 |
| DOI: | 10.1038/s41598-020-63887-8 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|