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

AI Language Models in Health Care (162) Clinical Information and Decision Making (1804) Artificial Intelligence (1987) Imaging Informatics (280) Clinical Informatics (1253) New Methods (1097) Nephrology/Renal Disorders (90)

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

Authors of this article:

Youngmin Bhak1, 2 Author Orcid Image ;   Yu Ho Lee3 Author Orcid Image ;   Joonhyung Kim4 Author Orcid Image ;   Kiwon Lee5 Author Orcid Image ;   Daehwan Lee5 Author Orcid Image ;   Eun Chan Jang6, 7 Author Orcid Image ;   Eunjeong Jang6, 7 Author Orcid Image ;   Christopher Seungkyu Lee8 Author Orcid Image ;   Eun Seok Kang9 Author Orcid Image ;   Sehee Park10, 11 Author Orcid Image ;   Hyun Wook Han6, 7, 12, 13 Author Orcid Image ;   Sang Min Nam6, 7, 14 Author Orcid Image
Article Authors Cited by (2) Tweetations (1) Metrics

Journals

  1. Dahiya N, Prakash D, Kundu S, Kuttan S, Suwalka I, Ayadi M, Dubale M, Hashmi A. Optimised RFO tuned RF-DETR model for precision urine microscopy for renal and systemic disease diagnosis. Scientific Reports 2025;15(1) View
  2. Yuan T, Wang H, Kang T, Wu W, Ou S. Advancements in the non-invasive diagnosis of renal fibrosis. Frontiers in Medicine 2025;12 View