Towards video-based surgical workflow understanding in open orthopaedic surgery

Safe and efficient surgical training and workflow management play a critical role in clinical competency and ultimately, patient outcomes. Video data in minimally invasive surgery (MIS) have enabled opportunities for vision-based artificial intelligence (AI) systems to improve surgical skills traini...

Full description

Saved in:
Bibliographic Details
Published in:Computer methods in biomechanics and biomedical engineering. 2021-05, Vol.9 (3), p.286-293
Main Authors: Kadkhodamohammadi, Abdolrahim, Sivanesan Uthraraj, Nachappa, Giataganas, Petros, Gras, Gauthier, Kerr, Karen, Luengo, Imanol, Oussedik, Sam, Stoyanov, Danail
Format: Article
Language:English
Subjects:
machine learning
open surgery
orthopaedics
surgical data science
Surgical workflow analysis
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Safe and efficient surgical training and workflow management play a critical role in clinical competency and ultimately, patient outcomes. Video data in minimally invasive surgery (MIS) have enabled opportunities for vision-based artificial intelligence (AI) systems to improve surgical skills training and assurance through post-operative video analysis and development of real-time computer-assisted interventions (CAI). Despite the availability of mounted cameras for the operating room (OR), similar capabilities are much more complex to develop for recording open surgery procedures, which has resulted in a shortage of exemplar video-based training materials. In this paper, we present a potential solution to record open surgical procedures using head-mounted cameras. Recorded videos were anonymised to remove patient and staff identifiable information using a machine learning algorithm that achieves state-of-the-art results on the OR Face dataset. We then propose a CNN-LSTM-based model to automatically segment videos into different surgical phases, which has never been previously demonstrated in open procedures. The redacted videos, along with the automatically predicted phases, are then available for surgeons and their teams for post-operative review and analysis. To our knowledge, this is the first demonstration of the feasibility of deploying camera recording systems and developing machine learning-based workflow analysis solutions for open surgery, particularly in orthopaedics.
ISSN:2168-1163
2168-1171
DOI:10.1080/21681163.2020.1835552