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

Pilot studies (non-ehealth) (110) Imaging Informatics (308) Artificial Intelligence (2185) Medical imaging (116) Presbyopia and other Age-related Eye Diseases and Vision Impairments (17) Artificial Intelligence in Diabetes Care and Prevention (55)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/59914, first published .
Assessment of Clinical Metadata on the Accuracy of Retinal Fundus Image Labels in Diabetic Retinopathy in Uganda: Case-Crossover Study Using the Multimodal Database of Retinal Images in Africa

Assessment of Clinical Metadata on the Accuracy of Retinal Fundus Image Labels in Diabetic Retinopathy in Uganda: Case-Crossover Study Using the Multimodal Database of Retinal Images in Africa

Assessment of Clinical Metadata on the Accuracy of Retinal Fundus Image Labels in Diabetic Retinopathy in Uganda: Case-Crossover Study Using the Multimodal Database of Retinal Images in Africa

Authors of this article:

Simon Arunga1 Author Orcid Image ;   Katharine Elise Morley2 Author Orcid Image ;   Teddy Kwaga1 Author Orcid Image ;   Michael Gerard Morley3 Author Orcid Image ;   Luis Filipe Nakayama4, 5 Author Orcid Image ;   Rogers Mwavu6 Author Orcid Image ;   Fred Kaggwa7 Author Orcid Image ;   Julius Ssempiira8 Author Orcid Image ;   Leo Anthony Celi5, 9, 10 Author Orcid Image ;   Jessica E Haberer2 Author Orcid Image ;   Celestino Obua11 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

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  1. Rajput S, Gupta S. 2025 3rd International Conference on Data Science and Information System (ICDSIS). Multimodal Framework for Early Detection and Progression Prediction of Diabetic Retinopathy View