This paper is in the following e-collection/theme issue:

Artificial Intelligence (1377) Imaging Informatics (194) Machine Learning (1471) Clinical Information and Decision Making (1411) Decision Support for Health Professionals (1191) New Methods (946) Clinical Informatics (1028) ENT (Ear, Nose, Throat) (29)

Published on in Vol 23, No 9 (2021): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29678, first published .
A Fully Automated Analytic System for Measuring Endolymphatic Hydrops Ratios in Patients With Ménière Disease via Magnetic Resonance Imaging: Deep Learning Model Development Study

A Fully Automated Analytic System for Measuring Endolymphatic Hydrops Ratios in Patients With Ménière Disease via Magnetic Resonance Imaging: Deep Learning Model Development Study

A Fully Automated Analytic System for Measuring Endolymphatic Hydrops Ratios in Patients With Ménière Disease via Magnetic Resonance Imaging: Deep Learning Model Development Study

Authors of this article:

Chae Jung Park1 Author Orcid Image ;   Young Sang Cho2 Author Orcid Image ;   Myung Jin Chung3 Author Orcid Image ;   Yi-Kyung Kim3 Author Orcid Image ;   Hyung-Jin Kim3 Author Orcid Image ;   Kyunga Kim1, 4 Author Orcid Image ;   Jae-Wook Ko5 Author Orcid Image ;   Won-Ho Chung2 Author Orcid Image ;   Baek Hwan Cho6 Author Orcid Image
Article Authors Cited by (10) Tweetations (3) Metrics

Journals

  1. Cho Y, Song B, Cho B, Chung W. The usefulness of inner ear magnetic resonance imaging in patient with Ménière’s disease: A narrative review. Precision and Future Medicine 2022;6(2):138 View
  2. Byun H, Park C, Oh S, Chung M, Cho B, Cho Y. Automatic Prediction of Conductive Hearing Loss Using Video Pneumatic Otoscopy and Deep Learning Algorithm. Ear & Hearing 2022;43(5):1563 View
  3. Fujima N, Kamagata K, Ueda D, Fujita S, Fushimi Y, Yanagawa M, Ito R, Tsuboyama T, Kawamura M, Nakaura T, Yamada A, Nozaki T, Fujioka T, Matsui Y, Hirata K, Tatsugami F, Naganawa S. Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging. Magnetic Resonance in Medical Sciences 2023;22(4):401 View
  4. Petsiou D, Martinos A, Spinos D. Applications of Artificial Intelligence in Temporal Bone Imaging: Advances and Future Challenges. Cureus 2023 View
  5. Quatre R, Schmerber S, Attyé A. Improving rehabilitation of deaf patients by advanced imaging before cochlear implantation. Journal of Neuroradiology 2024;51(2):145 View
  6. Tsilivigkos C, Athanasopoulos M, Micco R, Giotakis A, Mastronikolis N, Mulita F, Verras G, Maroulis I, Giotakis E. Deep Learning Techniques and Imaging in Otorhinolaryngology—A State-of-the-Art Review. Journal of Clinical Medicine 2023;12(22):6973 View
  7. Wu Q, Wang X, Liang G, Luo X, Zhou M, Deng H, Zhang Y, Huang X, Yang Q. Advances in Image‐Based Artificial Intelligence in Otorhinolaryngology–Head and Neck Surgery: A Systematic Review. Otolaryngology–Head and Neck Surgery 2023;169(5):1132 View
  8. Park C, Park Y, Kwak K, Choi S, Kim H, Na D, Seo S, Chun M. Deep learning-based quantification of brain atrophy using 2D T1-weighted MRI for Alzheimer’s disease classification. Frontiers in Aging Neuroscience 2024;16 View
  9. Spinos D, Martinos A, Petsiou D, Mistry N, Garas G. Artificial Intelligence in Temporal Bone Imaging: A Systematic Review. The Laryngoscope 2024 View
  10. Yoo T, Yeo C, Kim M, Oh I, Lee E. Automated volumetric analysis of the inner ear fluid space from hydrops magnetic resonance imaging using 3D neural networks. Scientific Reports 2024;14(1) View