TY - JOUR
T1 - Deep learning for surgical phase recognition using endoscopic videos
AU - Guédon, Annetje C.P.
AU - Meij, Senna E.P.
AU - Osman, Karim N.M.M.H.
AU - Kloosterman, Helena A.
AU - van Stralen, Karlijn J.
AU - Grimbergen, Matthijs C.M.
AU - Eijsbouts, Quirijn A.J.
AU - van den Dobbelsteen, John J.
AU - Twinanda, Andru P.
N1 - Acccepted Author Manuscript
PY - 2020
Y1 - 2020
N2 - perating room planning is a complex task as pre-operative estimations of
procedure duration have a limited accuracy. This is due to large
variations in the course of procedures. Therefore, information about the
progress of procedures is essential to adapt the daily operating room
schedule accordingly. This information should ideally be objective,
automatically retrievable and in real-time. Recordings made during
endoscopic surgeries are a potential source of progress information. A
trained observer is able to recognize the ongoing surgical phase from
watching these videos. The introduction of deep learning techniques
brought up opportunities to automatically retrieve information from
surgical videos. The aim of this study was to apply state-of-the art
deep learning techniques on a new set of endoscopic videos to
automatically recognize the progress of a procedure, and to assess the
feasibility of the approach in terms of performance, scalability and
practical considerations.
AB - perating room planning is a complex task as pre-operative estimations of
procedure duration have a limited accuracy. This is due to large
variations in the course of procedures. Therefore, information about the
progress of procedures is essential to adapt the daily operating room
schedule accordingly. This information should ideally be objective,
automatically retrievable and in real-time. Recordings made during
endoscopic surgeries are a potential source of progress information. A
trained observer is able to recognize the ongoing surgical phase from
watching these videos. The introduction of deep learning techniques
brought up opportunities to automatically retrieve information from
surgical videos. The aim of this study was to apply state-of-the art
deep learning techniques on a new set of endoscopic videos to
automatically recognize the progress of a procedure, and to assess the
feasibility of the approach in terms of performance, scalability and
practical considerations.
KW - Automatic recognition
KW - Deep learning
KW - Endoscopic videos
KW - Surgical phase
UR - http://www.scopus.com/inward/record.url?scp=85096567420&partnerID=8YFLogxK
U2 - 10.1007/s00464-020-08110-5
DO - 10.1007/s00464-020-08110-5
M3 - Article
AN - SCOPUS:85096567420
SN - 0930-2794
VL - 35 (2021)
SP - 6150
EP - 6157
JO - Surgical Endoscopy
JF - Surgical Endoscopy
IS - 11
ER -