eprintid: 10119197
rev_number: 19
eprint_status: archive
userid: 608
dir: disk0/10/11/91/97
datestamp: 2021-01-22 16:19:29
lastmod: 2022-02-15 18:48:00
status_changed: 2021-01-22 16:19:29
type: article
metadata_visibility: show
creators_name: Kadkhodamohammadi, A
creators_name: Sivanesan Uthraraj, N
creators_name: Giataganas, P
creators_name: Gras, G
creators_name: Kerr, K
creators_name: Luengo, I
creators_name: Oussedik, S
creators_name: Stoyanov, D
title: Towards video-based surgical workflow understanding in open orthopaedic surgery
ispublished: pub
subjects: UCH
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
keywords: Surgical workflow analysis, surgical data science, open surgery, orthopaedics, machine learning
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
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.
date: 2021
date_type: published
official_url: https://doi.org/10.1080/21681163.2020.1835552
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1838340
doi: 10.1080/21681163.2020.1835552
lyricists_name: Stoyanov, Danail
lyricists_id: DSTOY26
actors_name: Stoyanov, Danail
actors_id: DSTOY26
actors_role: owner
full_text_status: public
publication: Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization
volume: 9
number: 3
pagerange: 286-293
citation:        Kadkhodamohammadi, A;    Sivanesan Uthraraj, N;    Giataganas, P;    Gras, G;    Kerr, K;    Luengo, I;    Oussedik, S;           Kadkhodamohammadi, A;  Sivanesan Uthraraj, N;  Giataganas, P;  Gras, G;  Kerr, K;  Luengo, I;  Oussedik, S;  Stoyanov, D;   - view fewer <#>    (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 <https://doi.org/10.1080/21681163.2020.1835552>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10119197/1/aecai2020_Towards_Video_based_Surgical_Workflow_Understanding_in_Open_Orthopedic_Surgery%20submitted.pdf