ArticleScientific reports2020
Automated measurement of hydrops ratio from MRI in patients with Ménière's disease using CNN-based segmentation.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Deep Learning-Based Inner Ear Subregion Segmentation in 3D T2-Weighted MRI Using Label-Preserving Data Augmentation.NMR in biomedicine · 2026Article
- Image-based deep learning in otology: a state-of-the-art review.Frontiers in neurology · 2026Review
- [Magnetic resonance imaging research progress of hydropic ear disease].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2025Review
- [Artificial intelligence applications in Ménière's disease].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2025Review
- Applications of Machine Learning in Meniere's Disease Assessment Based on Pure-Tone Audiometry.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2025Article
- Evaluation of the diagnostic value of magnetic resonance imaging combined with ultrasound and mammography for breast cancer using array spatial sensitivity encoding technique.Frontiers in oncology · 2025Article
- Application of Artificial Intelligence in Otology: Past, Present, and Future.Journal of clinical medicine · 2024Article
- Deep Learning Techniques and Imaging in Otorhinolaryngology-A State-of-the-Art Review.Journal of clinical medicine · 2023Review
- Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2023Review
- Research and application progress in deep learning in otology.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2023Article
- [Characteristics of wideband tympanometry in patients with Ménière's disease based on neutral network].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2022Article
- Application of high resolution computed tomography image assisted classification model of middle ear diseases based on 3D-convolutional neural network.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2022Article
- Article
- Correlation of clinical parameters with endolymphatic hydrops on MRI in Meniere's disease.Frontiers in neurology · 2022Article
- IE-Vnet: Deep Learning-Based Segmentation of the Inner Ear's Total Fluid Space.Frontiers in neurology · 2022Article
- A non-invasive, automated diagnosis of Menière's disease using radiomics and machine learning on conventional magnetic resonance imaging: A multicentric, case-controlled feasibility study.La Radiologia medica · 2022Article
- Deep learning models for screening of high myopia using optical coherence tomography.Scientific reports · 2021Article
- A Fully Automated Analytic System for Measuring Endolymphatic Hydrops Ratios in Patients With Ménière Disease via Magnetic Resonance Imaging: Deep Learning Model Development Study.Journal of medical Internet research · 2021Article
- Validation of inner ear MRI in patients with Ménière's disease by comparing endolymphatic hydrops from histopathologic specimens.Scientific reports · 2021Article
- Deep learning-based automated and universal bubble detection and mask extraction in complex two-phase flows.Scientific reports · 2021Article
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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