Retinal fundus imaging is a powerful tool for disease screening and diagnosis in opthalmology. With the advent of machine learning and artificial intelligence, in particular modern computer vision classification algorithms, there is broad scope for technology to improve accuracy, increase accessibility and reduce cost in these processes. In this paper we present the first deep learning model trained on the first Brazilian multi-label opthalmological datatset. We train a multi-label classifier using over 16,000 clinically-labelled fundus images. Across a range of 13 retinal diseases, we obtain frequency-weighted AUC and F1 scores of 0.92 and 0.70 respectively. Our work establishes a baseline model on this new dataset and furthermore demonstrates the applicability and power of artificial intelligence approaches to retinal fundus disease diagnosis in under-represented populations.Competing Interest StatementDAC reports personal fees from Oxford University Innovation, personal fees from BioBeats, personal fees from Sensyne Health, outside the submitted work.Funding StatementThis work was supported by the Wellcome Trust/University of Oxford Medical and Life Sciences Translational Fund (Award: 0009350), and the Oxford National Institute for Health and Care Research (NIHR) Biomedical Research Centre (BRC). JY is a Marie Sklodowska-Curie Fellow, under the European Union’s Horizon 2020 research and innovation programme (Grant agreement: 955681, "MOIRA"). DAC was supported by a Royal Academy of Engineering Research Chair, an NIHR Research Professorship, the InnoHK Hong Kong Centre for Cerebro-cardiovascular Health Engineering (COCHE), and the Pandemic Sciences Institute at the University of Oxford. The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the manuscript. The views expressed in this publication are those of the authors and not necessarily those of the funders.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:This project was approved by the Sao Paulo Federal University - UNIFESP institutional review board (CAAE 33842220.7.0000.5505). The requirement for individual consent was waived. In this dataset, all images were anonymized with all identifiable patient information removed.I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesThe dataset is available on PhysioNet at https://physionet.org/content/brazilian-ophthalmological/1.0.0/. In order to access the data, users are required to be a credentialed user and complete various training modules on the subject of data ethics, fair-use and safety. https://physionet.org/content/brazilian-ophthalmological/1.0.0/
arXiv preprint, 2026
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