Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/55825, first published .
Diagnosis of Chronic Kidney Disease Using Retinal Imaging and Urine Dipstick Data: Multimodal Deep Learning Approach

Diagnosis of Chronic Kidney Disease Using Retinal Imaging and Urine Dipstick Data: Multimodal Deep Learning Approach

Diagnosis of Chronic Kidney Disease Using Retinal Imaging and Urine Dipstick Data: Multimodal Deep Learning Approach

Youngmin Bhak   1, 2 * , BS ;   Yu Ho Lee   3 * , MD, PhD ;   Joonhyung Kim   4 * , MD, PhD ;   Kiwon Lee   5 * , MS ;   Daehwan Lee   5 , BS ;   Eun Chan Jang   6, 7 , MPH ;   Eunjeong Jang   6, 7 , BS ;   Christopher Seungkyu Lee   8 , MD, PhD ;   Eun Seok Kang   9 , MD, PhD ;   Sehee Park   10, 11 , MS ;   Hyun Wook Han   6, 7, 12, 13 , MD, PhD ;   Sang Min Nam   6, 7, 14 , MD, PhD

1 Korean Genomics Center (KOGIC), Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea

2 Department of Biomedical Engineering, College of Information and Biotechnology, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea

3 Division of Nephrology, Department of Internal Medicine, CHA Bundang Medical Center, CHA University, Gyeonggi-do, Republic of Korea

4 Department of Ophthalmology, CHA Bundang Medical Center, CHA University, Gyeonggi-do, Republic of Korea

5 Spidercore Inc, Daejeon, Republic of Korea

6 Department of Biomedical Informatics, School of Medicine, CHA University, Seongnam, Republic of Korea

7 Institute for Biomedical Informatics, School of Medicine, CHA University, Seongnam, Republic of Korea

8 Department of Ophthalmology, Institute of Vision Research, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea

9 Division of Endocrinology and Metabolism, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea

10 Department of ICT Safety, Graduate School of Chung-Ang University, Seoul, Republic of Korea

11 IAEC Medical Service, Seoul, Republic of Korea

12 Institute of Basic Medical Sciences, School of Medicine, CHA University, Gyeonggi-do, Republic of Korea

13 Healthcare Big Data Center, CHA Bundang Medical Center, Gyeonggi-do, Republic of Korea

14 Daechi Yonsei Eye Clinic, Seoul, Republic of Korea

*these authors contributed equally

Corresponding Author:

  • Sang Min Nam, MD, PhD
  • Department of Biomedical Informatics
  • School of Medicine
  • CHA University
  • 335 Pangyo-ro
  • Seongnam
  • Republic of Korea
  • Phone: 82 31-881-7964
  • Fax: 82 31-881-7069
  • Email: forustous@gmail.com