Deep Learning Method Applied to Autonomous Image Diagnosis for Prick Test

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorDr. Elaine’s Clinic (Clínica Dra. Elaine)-
Autor(es): dc.creatorGomes, Ramon Hernany Martins-
Autor(es): dc.creatorPerger, Edson Luiz Pontes-
Autor(es): dc.creatorVasques, Lucas Hecker-
Autor(es): dc.creatorGagete, Elaine-
Autor(es): dc.creatorSimões, Rafael Plana-
Data de aceite: dc.date.accessioned2025-08-21T21:21:08Z-
Data de disponibilização: dc.date.available2025-08-21T21:21:08Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-10-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/life14101256-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/299737-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/299737-
Descrição: dc.descriptionBackground: The skin prick test (SPT) is used to diagnose sensitization to antigens. This study proposes a deep learning approach to infer wheal dimensions, aiming to reduce dependence on human interpretation. Methods: A dataset of SPT images (n = 5844) was used to infer a convolutional neural network for wheal segmentation (ML model). Three methods for inferring wheal dimensions were evaluated: the ML model; the standard protocol (MA1); and approximation of the area as an ellipse using diameters measured by an allergist (MA2). The results were compared with assisted image segmentation (AIS), the most accurate method. Bland–Altman analysis, distribution analyses, and correlation tests were applied to compare the methods. This study also compared the percentage deviation among these methods in determining the area of wheals with regular geometric shapes (n = 150) and with irregular shapes (n = 150). Results: The Bland–Altman analysis showed that the difference between methods was not correlated with the absolute area. The ML model achieved a segmentation accuracy of 85.88% and a strong correlation with the AIS method (ρ = 0.88), outperforming all other methods. Additionally, MA1 showed significant error (13.44 ± 13.95%) for pseudopods. Conclusions: The ML protocol can potentially automate the reading of SPT, offering greater accuracy than the standard protocol.-
Descrição: dc.descriptionDepartment of Bioprocess and Biotechnology School of Agriculture São Paulo State University (UNESP), Avenue Universitária, 3780, SP-
Descrição: dc.descriptionMedical School São Paulo State University (UNESP), Avenue Prof. Mário Rubens Guimarães Montenegro, s/n, SP-
Descrição: dc.descriptionDr. Elaine’s Clinic (Clínica Dra. Elaine), 398 Doutor Rodrigues do Lago, SP-
Descrição: dc.descriptionDepartment of Bioprocess and Biotechnology School of Agriculture São Paulo State University (UNESP), Avenue Universitária, 3780, SP-
Descrição: dc.descriptionMedical School São Paulo State University (UNESP), Avenue Prof. Mário Rubens Guimarães Montenegro, s/n, SP-
Idioma: dc.languageen-
Relação: dc.relationLife-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectdeep learning applied to diagnosis-
Palavras-chave: dc.subjectIgE response-
Palavras-chave: dc.subjectmeasurement of wheal area-
Palavras-chave: dc.subjectprick test-
Palavras-chave: dc.subjectsensitization to antigens-
Título: dc.titleDeep Learning Method Applied to Autonomous Image Diagnosis for Prick Test-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.