Item request has been placed! ×
Item request cannot be made. ×
loading  Processing Request

Automated analysis of operative video in surgical training: scoping review

Item request has been placed! ×
Item request cannot be made. ×
loading   Processing Request
  • معلومة اضافية
    • بيانات النشر:
      Oxford University Press (OUP), 2024.
    • الموضوع:
      2024
    • نبذة مختصرة :
      Background There is increasing availability of operative video for use in surgical training. Emerging technologies can now assess video footage and automatically generate metrics that could be harnessed to improve the assessment of operative performance. However, a comprehensive understanding of which technology features are most impactful in surgical training is lacking. The aim of this scoping review was to explore the current use of automated video analytics in surgical training. Methods PubMed, Scopus, the Web of Science, and the Cochrane database were searched, to 29 September 2023, following PRISMA extension for scoping reviews (PRISMA-ScR) guidelines. Search terms included ‘trainee’, ‘video analytics’, and ‘education’. Articles were screened independently by two reviewers to identify studies that applied automated video analytics to trainee-performed operations. Data on the methods of analysis, metrics generated, and application to training were extracted. Results Of the 6736 articles screened, 13 studies were identified. Computer vision tracking was the common method of video analysis. Metrics were described for processes (for example movement of instruments), outcomes (for example intraoperative phase duration), and critical safety elements (for example critical view of safety in laparoscopic cholecystectomy). Automated metrics were able to differentiate between skill levels (for example consultant versus trainee) and correlated with traditional methods of assessment. There was a lack of longitudinal application to training and only one qualitative study reported the experience of trainees using automated video analytics. Conclusion The performance metrics generated from automated video analysis are varied and encompass several domains. Validation of analysis techniques and the metrics generated are a priority for future research, after which evidence demonstrating the impact on training can be established.
    • ISSN:
      2474-9842
    • الرقم المعرف:
      10.1093/bjsopen/zrae124
    • Rights:
      CC BY NC
      URL: http://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.
    • الرقم المعرف:
      edsair.doi.dedup.....a0748eb08936b5cb35d1cbbb220adc52