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dc.contributor.authorBarros, George Oliveira-
dc.contributor.authorSilva, José Nathan Andrade Muller da-
dc.contributor.authorProença, Henrique Machado De Sousa-
dc.contributor.authorAraújo, Stanley Almeida-
dc.contributor.authorWanderley, David Campos-
dc.contributor.authorOliveira, Luciano Rebouças de-
dc.contributor.authorSantos, Washington Luis Conrado dos-
dc.contributor.authorDuarte, Angelo Amancio-
dc.contributor.authorVidal, Flávio de Barros-
dc.date.accessioned2026-10-28T13:49:31Z-
dc.date.available2026-10-28T13:49:31Z-
dc.date.issued2024-09-10-
dc.identifier.citationBARROS, George Oliveira et al. Enhancing podocyte degenerative changes identification with pathologist collaboration: implications for improved diagnosis in kidney diseases. IEEE Journal of Translation Engineering in Heath and Medicine, v. 12, p. 635-642, 2024. DOI: 10.1109/JTEHM.2024.345594. Disponível em: https://ieeexplore.ieee.org/document/10675352. Acesso em: 28 out. 2026.pt_BR
dc.identifier.urihttp://repositorio.unb.br/handle/10482/57323-
dc.language.isoengpt_BR
dc.publisherIEEEpt_BR
dc.rightsAcesso Abertopt_BR
dc.titleEnhancing podocyte degenerative changes identification with pathologist collaboration : implications for improved diagnosis in kidney diseasespt_BR
dc.typeArtigopt_BR
dc.subject.keywordRins - doenças - diagnósticopt_BR
dc.subject.keywordPodocitopatiapt_BR
dc.subject.keywordProcessamento de imagens médicaspt_BR
dc.subject.keywordRedes neurais convolucionaispt_BR
dc.rights.licenseThis work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/pt_BR
dc.identifier.doi10.1109/JTEHM.2024.3455941pt_BR
dc.description.abstract1Podocyte degenerative changes are common in various kidney diseases, and their accurate identification is crucial for pathologists to diagnose and treat such conditions. However, this can be a difficult task, and previous attempts to automate the identification of podocytes have not been entirely successful. To address this issue, this study proposes a novel approach that combines pathologists’ expertise with an automated classifier to enhance the identification of podocytopathies. The study involved building a new dataset of renal glomeruli images, some with and others without podocyte degenerative changes, and developing a convolutional neural network (CNN) based classifier. The results showed that our automated classifier achieved an impressive 90.9% f-score. When the pathologists used as an auxiliary tool to classify a second set of images, the medical group’s average performance increased significantly, from 91.4±12.5% to 96.1±2.9% of f-score. Fleiss’ kappa agreement among the pathologists also increased from 0.59 to 0.83. Conclusion: These findings suggest that automating this task can bring benefits for pathologists to correctly identify images of glomeruli with podocyte degeneration, leading to improved individual accuracy while raising agreement in diagnosing podocytopathies. This approach could have significant implications for the diagnosis and treatment of kidney diseases.pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-0838-047Xpt_BR
dc.identifier.orcidhttps://orcid.org/0000-0001-9952-6755pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0001-9996-4405pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0003-1201-9449pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0001-7183-8853pt_BR
dc.contributor.affiliationInstituto Federal Goianopt_BR
dc.contributor.affiliationUniversity of Brasilia, Department of Computer Sciencept_BR
dc.contributor.affiliationFederal University of Bahia, Department of Pathologypt_BR
dc.contributor.affiliationOswaldo Ramos Foundation, Department of Pathology, Kidney and Hypertension Hospital, São Paulopt_BR
dc.contributor.affiliationFederal University of Minas Gerais, Institute of Nephropathology, Center for Electron Microscopypt_BR
dc.contributor.affiliationFederal University of Minas Gerais, Institute of Nephropathology, Center for Electron Microscopypt_BR
dc.contributor.affiliationFederal University of Bahia, Intelligent Vision Research Labpt_BR
dc.contributor.affiliationOswaldo Cruz Foundation, Gonçalo Moniz Institutept_BR
dc.contributor.affiliationState University of Feira de Santana, Department of Technologypt_BR
dc.contributor.affiliationUniversity of Brasilia, Department of Computer Sciencept_BR
dc.description.unidadeInstituto de Ciências Exatas (IE)pt_BR
dc.description.unidadeDepartamento de Ciência da Computação (IE CIC)pt_BR
dc.description.ppgPrograma de Pós-Graduação em Informáticapt_BR
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