Predicting Mohs surgery complexity by applying machine learning to patient demographics and tumor characteristics.

Shoham, Gon; Berl, Ariel; Shir-Az, Ofir; Shabo, Sharon; Shalom, Avshalom

Shoham, Gon; Berl, Ariel; Shir-Az, Ofir; Shabo, Sharon; Shalom, Avshalom.

Afiliação

Shoham G; Department of Plastic Surgery, Meir Medical Center, Kfar Saba, Israel.
Berl A; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
Shir-Az O; The Department of Biomedical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel.
Shabo S; Department of Plastic Surgery, Meir Medical Center, Kfar Saba, Israel.
Shalom A; Department of Plastic Surgery, Meir Medical Center, Kfar Saba, Israel.

Exp Dermatol ; 31(7): 1029-1035, 2022 07.

Article em En | MEDLINE | ID: mdl-35213063

Assuntos

Cirurgia de Mohs; Neoplasias Cutâneas; Humanos; Incidência; Aprendizado de Máquina; Cirurgia de Mohs/métodos; Estudos Retrospectivos; Neoplasias Cutâneas/patologia; Neoplasias Cutâneas/cirurgia

Palavras-chave

Mohs complexity; Mohs micrographic surgery; artificial intelligence; machine learning; skin cancer

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Cutâneas / Cirurgia de Mohs Tipo de estudo: Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Exp Dermatol Assunto da revista: DERMATOLOGIA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Israel

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