Comparison of the Predicting Performance for Fate of Medial Meniscus Posterior Root Tear Based on Treatment Strategies: A Comparison between Logistic Regression, Gradient Boosting, and CNN Algorithms.

Lee, Jae-Ik; Kim, Dong-Hyun; Yoo, Hyun-Jin; Choi, Han-Gyeol; Lee, Yong-Seuk

Lee, Jae-Ik; Kim, Dong-Hyun; Yoo, Hyun-Jin; Choi, Han-Gyeol; Lee, Yong-Seuk.

Afiliação

Lee JI; Department of Orthopaedic Surgery, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam 13620, Korea.
Kim DH; Department of Orthopaedic Surgery, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam 13620, Korea.
Yoo HJ; Department of Orthopaedic Surgery, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam 13620, Korea.
Choi HG; Department of Orthopaedic Surgery, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam 13620, Korea.
Lee YS; Department of Orthopaedic Surgery, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam 13620, Korea.

Diagnostics (Basel) ; 11(7)2021 Jul 07.

Article em En | MEDLINE | ID: mdl-34359308

Palavras-chave

affecting factors; artificial intelligence; knee; medial meniscus root tear; predicting performance

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Diagnostics (Basel) Ano de publicação: 2021 Tipo de documento: Article

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