Fernando Korn Malerbi
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Explore the profile of Fernando Korn Malerbi including associated specialties, affiliations and a list of published articles.
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51
Citations
239
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Recent Articles
1.
Wu C, Restrepo D, Nakayama L, Ribeiro L, Shuai Z, Barboza N, et al.
Sci Data
. 2025 Feb;
12(1):323.
PMID: 39987104
This paper introduces mBRSET, the first publicly available diabetic retinopathy retina dataset captured using handheld retinal cameras in real-life, high-burden scenarios, comprising 5,164 images from 1,291 patients of diverse backgrounds....
2.
Malerbi F, Nakayama L, Prado P, Yamanaka F, Melo G, Regatieri C, et al.
Ann Transl Med
. 2024 Nov;
12(5):89.
PMID: 39507460
Background: The opaqueness of artificial intelligence (AI) algorithms decision processes limit their application in healthcare. Our objective was to explore discrepancies in heatmaps originated from slightly different retinal images from...
3.
Nakayama L, Restrepo D, Matos J, Ribeiro L, Malerbi F, Celi L, et al.
PLOS Digit Health
. 2024 Jul;
3(7):e0000454.
PMID: 38991014
Introduction: The Brazilian Multilabel Ophthalmological Dataset (BRSET) addresses the scarcity of publicly available ophthalmological datasets in Latin America. BRSET comprises 16,266 color fundus retinal photos from 8,524 Brazilian patients, aiming...
4.
Nakayama L, Ribeiro L, Malerbi F, Regatieri C
Front Ophthalmol (Lausanne)
. 2024 Jul;
2:898181.
PMID: 38983555
No abstract available.
5.
Melo G, Nakayama L, Cardoso V, Dos Santos L, Malerbi F
Ophthalmol Retina
. 2024 May;
8(11):1083-1092.
PMID: 38750937
Purpose: Diabetic retinopathy (DR) is a leading cause of preventable blindness, particularly in underserved regions where access to ophthalmic care is limited. This study presents a proof of concept for...
6.
Ribeiro L, Nakayama L, Malerbi F, Regatieri C
Sci Rep
. 2024 May;
14(1):10395.
PMID: 38710726
To assess the feasibility of code-free deep learning (CFDL) platforms in the prediction of binary outcomes from fundus images in ophthalmology, evaluating two distinct online-based platforms (Google Vertex and Amazon...
7.
Barbieri V, Nakayama L, Barbieri G, Silva S, Karmouche D, Shinzato M, et al.
Arq Bras Oftalmol
. 2024 Apr;
87(4):e2023.
PMID: 38656030
Purpose: Timely screening and treatment are essential for preventing diabetic retinopathy blindness. Improving screening workflows can reduce waiting times for specialist evaluation and thus enhance patient outcomes. This study assessed...
8.
Malerbi F, Mezzomo Ventura B, Fischer M, Penha F
J Diabetes Sci Technol
. 2024 Feb;
18(3):750-751.
PMID: 38404014
During an artificial intelligence (AI)-assisted diabetic retinopathy screening event, we performed a survey on patients´ perceptions on AI. Respondents were individuals with diabetes, mostly followed in primary healthcare with a...
9.
Nakayama L, Restrepo D, Matos J, Ribeiro L, Malerbi F, Celi L, et al.
medRxiv
. 2024 Feb;
PMID: 38343827
Introduction: The Brazilian Multilabel Ophthalmological Dataset (BRSET) addresses the scarcity of publicly available ophthalmological datasets in Latin America. BRSET comprises 16,266 color fundus retinal photos from 8,524 Brazilian patients, aiming...
10.
Nakayama L, Ribeiro L, Novaes F, Ayumi Miyawaki I, Miyawaki A, de Oliveira J, et al.
Ann Med
. 2023 Sep;
55(2):2258149.
PMID: 37734417
Purpose: This study aims to compare artificial intelligence (AI) systems applied in diabetic retinopathy (DR) teleophthalmology screening, currently deployed systems, fairness initiatives and the challenges for implementation. Methods: The review...