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