Machine learning model for predicting the optimal depth of tracheal tube insertion in pediatric patients: A retrospective cohort study

• Jae-Geum Shim,

  Roles Conceptualization, Data curation, Methodology, Software, Visualization, Writing – original draft

  Affiliation Department of Anesthesiology and Pain Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea

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• Kyoung-Ho Ryu,

  Roles Data curation, Project administration

  Affiliation Department of Anesthesiology and Pain Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea

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• Sung Hyun Lee,

  Roles Data curation, Formal analysis

  Affiliation Department of Anesthesiology and Pain Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea

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• Eun-Ah Cho,

  Roles Data curation, Investigation

  Affiliation Department of Anesthesiology and Pain Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea

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• Sungho Lee,

  Roles Data curation

  Affiliation Department of Anesthesiology and Pain Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea

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• Jin Hee Ahn

  Roles Conceptualization, Data curation, Resources, Writing – original draft, Writing – review & editing

  * E-mail: blatt.ahn@samsung.com

  Affiliation Department of Anesthesiology and Pain Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea

  https://orcid.org/0000-0002-5861-1328

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