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

Artificial Intelligence (1371) Imaging Informatics (193) Machine Learning (1465) Clinical Information and Decision Making (1407) Decision Support for Health Professionals (1186) New Methods (945) Clinical Informatics (1022) 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

Chae Jung Park   1 * , MSc ;   Young Sang Cho   2 * , MD ;   Myung Jin Chung   3 , MD, PhD ;   Yi-Kyung Kim   3 , MD ;   Hyung-Jin Kim   3 , MD, PhD ;   Kyunga Kim   1, 4 , PhD ;   Jae-Wook Ko   5 , MD, PhD ;   Won-Ho Chung   2 * , MD, PhD ;   Baek Hwan Cho   6 , PhD

1 Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea

2 Department of Otorhinolaryngology-Head and Neck Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

3 Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

4 Biomedical Statistics Center, Data Science Research Institute, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea

5 Department of Clinical Pharmacology and Therapeutics, Samsung Medical Center, Seoul, Republic of Korea

6 Department of Medical Device Management and Research, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea

*these authors contributed equally

Corresponding Author:

  • Baek Hwan Cho, PhD
  • Department of Medical Device Management and Research
  • Samsung Advanced Institute for Health Sciences & Technology
  • Sungkyunkwan University
  • 81 Irwon-ro, Gangnam-gu
  • Seoul, 06355
  • Republic of Korea
  • Phone: 82 234100885
  • Email: baekhwan.cho@samsung.com