Artificial intelligence-based measurement outperforms current methods for colorectal polyp size measurement.

Kwak, Min Seob; Cha, Jae Myung; Jeon, Jung Won; Yoon, Jin Young; Park, Jong Wook

Kwak, Min Seob; Cha, Jae Myung; Jeon, Jung Won; Yoon, Jin Young; Park, Jong Wook.

Afiliação

Kwak MS; Department of Internal Medicine, Kyung Hee University Hospital at Gangdong, Kyung Hee University College of Medicine, Seoul, Korea.
Cha JM; Department of Internal Medicine, Kyung Hee University Hospital at Gangdong, Kyung Hee University College of Medicine, Seoul, Korea.
Jeon JW; Department of Internal Medicine, Kyung Hee University Hospital at Gangdong, Kyung Hee University College of Medicine, Seoul, Korea.
Yoon JY; Department of Internal Medicine, Kyung Hee University Hospital at Gangdong, Kyung Hee University College of Medicine, Seoul, Korea.
Park JW; Department of Internal Medicine, Kyung Hee University Hospital at Gangdong, Kyung Hee University College of Medicine, Seoul, Korea.

Dig Endosc ; 34(6): 1188-1195, 2022 Sep.

Article em En | MEDLINE | ID: mdl-35385184

Assuntos

Pólipos do Colo; Neoplasias Colorretais; Inteligência Artificial; Pólipos do Colo/diagnóstico por imagem; Pólipos do Colo/patologia; Colonoscopia/métodos; Neoplasias Colorretais/diagnóstico por imagem; Neoplasias Colorretais/patologia; Humanos; Reprodutibilidade dos Testes; Instrumentos Cirúrgicos

Palavras-chave

colonoscopy; deep learning; model; polyp size; vessel

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Colorretais / Pólipos do Colo Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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