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      Adoption of routine surgical video recording: a nationwide freedom of information act request across England and Wales

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          Summary

          Background

          Surgical video contains data with significant potential to improve surgical outcome assessment, quality assurance, education, and research. Current utilisation of surgical video recording is unknown and related policies/governance structures are unclear.

          Methods

          A nationwide Freedom of Information (FOI) request concerning surgical video recording, technology, consent, access, and governance was sent to all acute National Health Service (NHS) trusts/boards in England/Wales between 20th February and 20th March 2023.

          Findings

          140/144 (97.2%) trusts/boards in England/Wales responded to the FOI request. Surgical procedures were routinely recorded in 22 trusts/boards. The median estimate of consultant surgeons routinely recording their procedures was 20%. Surgical video was stored on internal systems (n = 27), third-party products (n = 29), and both (n = 9). 32/140 (22.9%) trusts/boards ask for consent to record procedures as part of routine care. Consent for recording included non-clinical purposes in 55/140 (39.3%) trusts/boards. Policies for surgeon/patient access to surgical video were available in 48/140 (34.3%) and 32/140 (22.9%) trusts/boards, respectively. Surgical video was used for non-clinical purposes in 64/140 (45.7%) trusts/boards. Governance policies covering surgical video recording, use, and/or storage were available from 59/140 (42.1%) trusts/boards.

          Interpretation

          There is significant heterogeneity in surgical video recording practices in England and Wales. A minority of trusts/boards routinely record surgical procedures, with large variation in recording/storage practices indicating scope for NHS-wide coordination. Revision of surgical video consent, accessibility, and governance policies should be prioritised by trusts/boards to protect key stakeholders. Increased availability of surgical video is essential for patients and surgeons to maximally benefit from the ongoing digital transformation of surgery.

          Funding

          KL is supported by an NIHR Academic Clinical Fellowship and acknowledges infrastructure support for this research from the doi 10.13039/501100000272, National Institute for Health Research; (NIHR) doi 10.13039/501100013342, Imperial Biomedical Research Centre; (BRC).

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          Most cited references45

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          Artificial Intelligence in Surgery

          The aim of this review was to summarize major topics in artificial intelligence (AI), including their applications and limitations in surgery. This paper reviews the key capabilities of AI to help surgeons understand and critically evaluate new AI applications and to contribute to new developments.
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            Surgical skill and complication rates after bariatric surgery.

            Clinical outcomes after many complex surgical procedures vary widely across hospitals and surgeons. Although it has been assumed that the proficiency of the operating surgeon is an important factor underlying such variation, empirical data are lacking on the relationships between technical skill and postoperative outcomes. We conducted a study involving 20 bariatric surgeons in Michigan who participated in a statewide collaborative improvement program. Each surgeon submitted a single representative videotape of himself or herself performing a laparoscopic gastric bypass. Each videotape was rated in various domains of technical skill on a scale of 1 to 5 (with higher scores indicating more advanced skill) by at least 10 peer surgeons who were unaware of the identity of the operating surgeon. We then assessed relationships between these skill ratings and risk-adjusted complication rates, using data from a prospective, externally audited, clinical-outcomes registry involving 10,343 patients. Mean summary ratings of technical skill ranged from 2.6 to 4.8 across the 20 surgeons. The bottom quartile of surgical skill, as compared with the top quartile, was associated with higher complication rates (14.5% vs. 5.2%, P<0.001) and higher mortality (0.26% vs. 0.05%, P=0.01). The lowest quartile of skill was also associated with longer operations (137 minutes vs. 98 minutes, P<0.001) and higher rates of reoperation (3.4% vs. 1.6%, P=0.01) and readmission (6.3% vs. 2.7%) (P<0.001). The technical skill of practicing bariatric surgeons varied widely, and greater skill was associated with fewer postoperative complications and lower rates of reoperation, readmission, and visits to the emergency department. Although these findings are preliminary, they suggest that peer rating of operative skill may be an effective strategy for assessing a surgeon's proficiency.
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              EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos.

              Surgical workflow recognition has numerous potential medical applications, such as the automatic indexing of surgical video databases and the optimization of real-time operating room scheduling, among others. As a result, surgical phase recognition has been studied in the context of several kinds of surgeries, such as cataract, neurological, and laparoscopic surgeries. In the literature, two types of features are typically used to perform this task: visual features and tool usage signals. However, the used visual features are mostly handcrafted. Furthermore, the tool usage signals are usually collected via a manual annotation process or by using additional equipment. In this paper, we propose a novel method for phase recognition that uses a convolutional neural network (CNN) to automatically learn features from cholecystectomy videos and that relies uniquely on visual information. In previous studies, it has been shown that the tool usage signals can provide valuable information in performing the phase recognition task. Thus, we present a novel CNN architecture, called EndoNet, that is designed to carry out the phase recognition and tool presence detection tasks in a multi-task manner. To the best of our knowledge, this is the first work proposing to use a CNN for multiple recognition tasks on laparoscopic videos. Experimental comparisons to other methods show that EndoNet yields state-of-the-art results for both tasks.
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                Author and article information

                Contributors
                Journal
                eClinicalMedicine
                EClinicalMedicine
                eClinicalMedicine
                Elsevier
                2589-5370
                22 March 2024
                April 2024
                22 March 2024
                : 70
                : 102545
                Affiliations
                [1]Department of Surgery and Cancer, Imperial College London, UK
                Author notes
                []Corresponding author. Department of Surgery and Cancer, Imperial College London, 10th Floor, Queen Elizabeth Queen Mother Wing, St Mary’s Hospital, London, W2 1NY, UK. a.yiu@ 123456imperial.ac.uk
                Article
                S2589-5370(24)00124-X 102545
                10.1016/j.eclinm.2024.102545
                11056472
                38685926
                a7469153-b32b-4e71-9cd0-a57863ac03f0
                © 2024 The Authors

                This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

                History
                : 30 January 2024
                : 28 February 2024
                : 28 February 2024
                Categories
                Articles

                minimally invasive surgery,surgical video,operative recordings,data governance

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