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SAGES consensus recommendations on an annotation framework for surgical video.
Meireles, Ozanan R; Rosman, Guy; Altieri, Maria S; Carin, Lawrence; Hager, Gregory; Madani, Amin; Padoy, Nicolas; Pugh, Carla M; Sylla, Patricia; Ward, Thomas M; Hashimoto, Daniel A.
Affiliation
  • Meireles OR; Department of Surgery, Massachusetts General Hospital, 15 Parkman Street, WAC460, Boston, MA, 02114, USA. ozmeireles@mgh.harvard.edu.
  • Rosman G; Department of Surgery, Massachusetts General Hospital, 15 Parkman Street, WAC460, Boston, MA, 02114, USA.
  • Altieri MS; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, USA.
  • Carin L; Department of Surgery, East Carolina University, Greenville, USA.
  • Hager G; Department of Electrical and Computer Engineering, Duke University, Durham, USA.
  • Madani A; Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA.
  • Padoy N; Department of Surgery, University Health Network, Toronto, Canada.
  • Pugh CM; ICube, University of Strasbourg, Strasbourg, France.
  • Sylla P; IHU Strasbourg, Strasbourg, France.
  • Ward TM; Department of Surgery, Stanford University, Stanford, USA.
  • Hashimoto DA; Department of Surgery, Mount Sinai Medical Center, New York, USA.
Surg Endosc ; 35(9): 4918-4929, 2021 09.
Article in En | MEDLINE | ID: mdl-34231065
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Machine Learning Type of study: Guideline / Prognostic_studies Limits: Humans Language: En Journal: Surg Endosc Year: 2021 Document type: Article
Fulltext
Print
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PubMed Links
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Machine Learning Type of study: Guideline / Prognostic_studies Limits: Humans Language: En Journal: Surg Endosc Year: 2021 Document type: Article