Development and Validation of a Deep Learning Model to Quantify Glomerulosclerosis in Kidney Biopsy Specimens.

Marsh, Jon N; Liu, Ta-Chiang; Wilson, Parker C; Swamidass, S Joshua; Gaut, Joseph P

Marsh, Jon N; Liu, Ta-Chiang; Wilson, Parker C; Swamidass, S Joshua; Gaut, Joseph P.

Afiliação

Marsh JN; Department of Pathology and Immunology, Washington University School of Medicine in St Louis, St Louis, Missouri.
Liu TC; Institute for Informatics (I 2 ), Washington University School of Medicine in St Louis, St Louis, Missouri.
Wilson PC; Department of Pathology and Immunology, Washington University School of Medicine in St Louis, St Louis, Missouri.
Swamidass SJ; Department of Pathology and Immunology, Washington University School of Medicine in St Louis, St Louis, Missouri.
Gaut JP; Department of Pathology and Immunology, Washington University School of Medicine in St Louis, St Louis, Missouri.

JAMA Netw Open ; 4(1): e2030939, 2021 01 04.

Article em En | MEDLINE | ID: mdl-33471115

Assuntos

Biópsia/métodos; Aprendizado Profundo; Diagnóstico por Computador/métodos; Glomerulonefrite; Rim/patologia; Algoritmos; Glomerulonefrite/diagnóstico; Glomerulonefrite/patologia; Humanos; Patologistas; Reprodutibilidade dos Testes

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Texto completo: 1 Coleções: 01-internacional Temas: Geral Base de dados: MEDLINE Assunto principal: Biópsia / Diagnóstico por Computador / Aprendizado Profundo / Glomerulonefrite / Rim Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Revista: JAMA Netw Open Ano de publicação: 2021 Tipo de documento: Article

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