Radiomic biomarkers for sacroiliitis diagnosis and prediction of spondyloarthritis progression (2020)
- Authors:
- USP affiliated authors: BARBOSA, MARCELLO HENRIQUE NOGUEIRA - FMRP ; MARQUES, PAULO MAZZONCINI DE AZEVEDO - FMRP ; TENORIO, ARIANE PRISCILLA MAGALHÃES - FMRP
- Unidade: FMRP
- DOI: 10.1007/s11548-020-02171-6
- Subjects: REUMATOLOGIA; RESSONÂNCIA MAGNÉTICA; DIAGNÓSTICO POR COMPUTADOR; APRENDIZADO COMPUTACIONAL; BIOMARCADORES
- Keywords: Radiomics; Spondyloarthritis; Magnetic resonance imaging; Sacroiliitis
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher place: Heidelberg
- Date published: 2020
- Source:
- Título do periódico: International Journal of Computer Assisted Radiology and Surgery
- ISSN: 1861-6410
- Volume/Número/Paginação/Ano: v. 15, suppl. 1, p. S127-S129, 2020
- Conference titles: International Congress and Exhibition on Computer Assisted Radiology and Surgery - CARS
- Este periódico é de assinatura
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: bronze
-
ABNT
TENÓRIO, Ariane Priscilla Magalhães et al. Radiomic biomarkers for sacroiliitis diagnosis and prediction of spondyloarthritis progression. International Journal of Computer Assisted Radiology and Surgery. Heidelberg: Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo. Disponível em: https://doi.org/10.1007/s11548-020-02171-6. Acesso em: 23 abr. 2024. , 2020 -
APA
Tenório, A. P. M., Faleiros, M. C., Ferreira-Junior, J. R., Dalto, V. F., Assad, R. L., Nogueira-Barbosa, M. H., & Azevedo-Marques, P. M. de. (2020). Radiomic biomarkers for sacroiliitis diagnosis and prediction of spondyloarthritis progression. International Journal of Computer Assisted Radiology and Surgery. Heidelberg: Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo. doi:10.1007/s11548-020-02171-6 -
NLM
Tenório APM, Faleiros MC, Ferreira-Junior JR, Dalto VF, Assad RL, Nogueira-Barbosa MH, Azevedo-Marques PM de. Radiomic biomarkers for sacroiliitis diagnosis and prediction of spondyloarthritis progression [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2020 ; 15 S127-S129.[citado 2024 abr. 23 ] Available from: https://doi.org/10.1007/s11548-020-02171-6 -
Vancouver
Tenório APM, Faleiros MC, Ferreira-Junior JR, Dalto VF, Assad RL, Nogueira-Barbosa MH, Azevedo-Marques PM de. Radiomic biomarkers for sacroiliitis diagnosis and prediction of spondyloarthritis progression [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2020 ; 15 S127-S129.[citado 2024 abr. 23 ] Available from: https://doi.org/10.1007/s11548-020-02171-6 - Machine learning models to predict axial and peripheral spondyloarthritis: a comparative radiomic study between SPAIR and STIR MRI sequences
- Inteligência artificial, aprendizado de máquina, diagnóstico auxiliado por computador e radiômica: avanços da imagem rumo à medicina de precisão
- Detection of vertebral plateaus in lateral lumbar spinal X-ray images with gabor filters
- Pattern recognition of inflammatory sacroiliitis in magnetic resonance imaging
- Recognition of vertebral compression fractures in magnetic resonance images using statistics of height and width
- Shape, texture and statistical features for classification of benign and malignant vertebral compression fractures in magnetic resonance
- Multi-segmentation and cooperative classification models improve the identification of malignant vertebral compression fractures in MRI
- Classification of vertebral compression fractures in magnetic resonance images using spectral and fractal analysis
- Cooperative strategy for a dynamic ensemble of classification models in clinical applications: the case of MRI vertebral compression fractures
- Pattern recognition of inflammatory sacroiliitis in magnetic resonance imaging
Informações sobre o DOI: 10.1007/s11548-020-02171-6 (Fonte: oaDOI API)
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