Development of an ontology for laparoscopic transabdominal adrenalectomy via a comprehensive modified Delphi survey and its validation on a multicentric pilot data set for surgical training and future video analysis with machine learning algorithms

  • Barbara Seeliger*
  • , Sofia Di Lorenzo
  • , Pier F Alesina
  • , Laurent Brunaud
  • , Costanza Chiapponi
  • , Carmela De Crea
  • , Gianluca Donatini
  • , Maurizio Iacobone
  • , Özer Makay
  • , Radu Mihai
  • , Martina T Mogl
  • , Didier Mutter
  • , Nicolas Padoy
  • , Fausto Palazzo
  • , Oscar Vidal
  • , Francesco Pennestrí
  • , Jacques Marescaux
  • , Michel Vix
  • , Marco Raffaelli
  • *Autore corrispondente per questo lavoro

Risultato della ricerca: Contributo in rivistaArticolo

Abstract

Surgical technique is essential to ensure safe minimally invasive adrenalectomy. Due to the relative rarity of adrenal surgery, it i challenging to ensure adequate exposure in surgical training. Surgical video analysis supports auto-evaluation, expert assessmen and could be a target for automatization. The developed ontology was validated by a European expert consensus and is applicabl across the surgical techniques encountered in all participating centres, with an exemplary demonstration in bi-centric recordings Standardization of adrenalectomy video analysis may foster surgical training and enable machine learning training for automated safety alerts.
Lingua originaleInglese
pagine (da-a)1-3
Numero di pagine3
RivistaBritish Journal of Surgery
Volume111
Numero di pubblicazione6
DOI
Stato di pubblicazionePubblicato - 2024

All Science Journal Classification (ASJC) codes

  • Chirurgia

Keywords

  • ontology for laparoscopic
  • transabdominal adrenalectomy
  • future video analysis with machine

Fingerprint

Entra nei temi di ricerca di 'Development of an ontology for laparoscopic transabdominal adrenalectomy via a comprehensive modified Delphi survey and its validation on a multicentric pilot data set for surgical training and future video analysis with machine learning algorithms'. Insieme formano una fingerprint unica.

Cita questo