Kadkhodamohammadi, A;
Sivanesan Uthraraj, N;
Giataganas, P;
Gras, G;
Kerr, K;
Luengo, I;
Oussedik, S;
(2021)
Towards video-based surgical workflow understanding in open orthopaedic surgery.
Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization
, 9
(3)
pp. 286-293.
10.1080/21681163.2020.1835552.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Towards video-based surgical workflow understanding in open orthopaedic surgery |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1080/21681163.2020.1835552 |
| Publisher version: | https://doi.org/10.1080/21681163.2020.1835552 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Surgical workflow analysis, surgical data science, open surgery, orthopaedics, machine learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10119197 |
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