Endoshare: A Publicly Available, Surgeons-Friendly Solution to De-Identify and Manage Surgical Videos
Not on the map. Why?
Abstract (arXiv)
Video-based assessment and surgical data science can advance surgical training, research, and quality improvement, yet adoption remains limited by heterogeneous recording formats and privacy concerns linked to video sharing. This work develops, evaluates, and publicly releases Endoshare, a surgeon-friendly application that merges, standardizes, and de-identifies endoscopic videos. Development followed an iterative, user-centered software life cycle. In the analysis phase, an internal survey of four clinicians and four computer scientists, based on 10 usability heuristics, identified early requirements and guided a cross-platform, privacy-by-design architecture. Prototype testing reported high usability for clinicians (4.68 +/- 0.40 out of 5) and for computer scientists (4.03 +/- 0.51 out of 5), with the lowest score (4.00 +/- 0.93 out of 5) relating to label clarity, prompting interface refinement to streamline case selection, video merging, automated out-of-body removal, and filename pseudonymization. In the testing phase, ten surgeons completed an external survey combining the same heuristics with Technology Acceptance Model constructs, reporting high perceived usefulness (5.07 +/- 1.75 out of 7), ease of use (5.15 +/- 1.71 out of 7), heuristic usability (4.38 +/- 0.48 out of 5), and strong recommendation likelihood (9.20 +/- 0.79 out of 10). A performance assessment across different hardware and configurations showed that processing time increased proportionally with video duration and was consistently lower in fast mode. Endoshare is a publicly available solution to manage surgical videos, with potential to support training, research, and quality improvement. Compliance certification and broader interoperability validation are needed to establish it as a reliable tool for surgical video management. The software is available at https://camma-public.github.io/Endoshare
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 4 of its 36 references that are in the map. First posted on arXiv 23 Oct 2025.
Builds on
4 of 36 references.
References · 4
- 2023Preserving Privacy in Surgical Video Analysis Using Artificial Intelligence: A Deep Learning Classifier to Identify Out-of-Body Scenes in Endoscopic Videoscited by 8
- 2016Prerequisites for International Exchanges of Health Information: Comparison of Australian, Austrian, Finnish, Swiss, and US Privacy Policiescited by 1
- 2023A Comparative Audit of Privacy Policies from Healthcare Organizations in USA, UK and Indiacited by 1
- 2025Surgeons Awareness, Expectations, and Involvement with Artificial Intelligence: a Survey Pre and Post the GPT Eracited by 1
Built on by
Citing papers per year: 2026: 2.
2 papers.