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Biomarkers in whole slide images stained by: Hemtoxylin-Eosin: A groundbreaking application using artificial intelligence

dc.contributor.authorBorrecho, Gonçalo
dc.contributor.authorCurado, Mónica
dc.contributor.authorVale, João
dc.contributor.authorVinagre, Tiago
dc.contributor.authorGeraldes, Mariana
dc.contributor.authorAssunção, Teresa
dc.contributor.authorCoelho, Daniel
dc.contributor.authorFerreira, Inês
dc.contributor.authorFerreira, Ana
dc.contributor.authorFernandes, Ana Isabel
dc.contributor.authorFrança, Amélia
dc.contributor.authorMendes, Fernando
dc.contributor.authorMartins, Diana
dc.date.accessioned2026-01-22T09:16:35Z
dc.date.available2026-01-22T09:16:35Z
dc.date.issued2024-03
dc.description.abstractBiomarkers play a fundamental role in the diagnosis, prognosis and prediction of diseases. The study of biomarkers requires the performance of complementary diagnostic tests, which entails high costs and inevitably leads to an increase in response time, which could have a severe impact on the patient’s outcome. The digital transformation in Pathology Laboratories, accompanied by the wide implementation of slide digitalization, has been decisive for the development and application of digital intelligence algorithms in a diagnostic context. The aim of this review is to assess artificial intelligence algorithms for evaluating biomarkers that can be applied to whole slide images stained by hemtoxylin-eosin (WSI-HE) and to understand their advantages and limitations. There are several types of algorithms, some established on the identification and quantification of morphological biomarkers, such as nuclear density, celular heterogeneity, the presence of certain cellular structures, tissue organization and other features. The usage of WSI-HE is enormously promising, as it reveals additional information that is not visually observable but can help or even expand to pathologists capabilities. The identification and validation of morphological biomarkers in WSI-HE still presents challenges, such as the need for large data sets annotated using multimodal data (information from diferente sources, such as histopathological images, clinical data, radiologial information, genomic data, among others), the interpretability of deep learning models, the integration of these biomarkers into clinical practice, among others. The application of algorithms in WSI-HE could represente na importante change in patient management, contributing to timely precision medicine.eng
dc.identifier.citationBorrecho, G., Curado, M., Vale, J., Vinagre, T., Geraldes, M., Assunção, T., Coelho, D., Ferreira, I., Ferreira, A., Fernandes, A. I., França, A., Mendes, F., & Martins, D. (2024). Biomarkers in whole slide images stained by: Hemtoxylin-Eosin: A groundbreaking application using artificial intelligence. Trends in Biomedical Laboratory Sciences - Abstract Book II Congresso BioMedLab, Vol 2 nº1 Supplement, 31. https://biomedlab.pt/wp-content/uploads/2024/03/Abstract-Book_Revista-Vol-2_020324.pdf
dc.identifier.urihttp://hdl.handle.net/10400.22/31592
dc.language.isoeng
dc.peerreviewedyes
dc.publisherAssociação Portuguesa de Ciências Biomédicas Laboratoriais
dc.relation.hasversionhttps://biomedlab.pt/wp-content/uploads/2024/03/Abstract-Book_Revista-Vol-2_020324.pdf
dc.rights.uriN/A
dc.subjectDigital pathology
dc.subjectComputacional pathology
dc.subjectBiomarkers
dc.subjectWhole Slide Image
dc.subjectHemtoxylin-Eosin
dc.titleBiomarkers in whole slide images stained by: Hemtoxylin-Eosin: A groundbreaking application using artificial intelligenceeng
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.conferenceDate2024
oaire.citation.conferencePlaceCoimbra
oaire.citation.issue1 Supplement
oaire.citation.startPage31
oaire.citation.titleTrends in Biomedical Laboratory Sciences - Abstract Book II Congresso BioMedLab
oaire.citation.volume2
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85

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