Endoshare: A Source Available Solution to De-Identify and Manage Surgical Videos

2025-10-22

AI Summary

This study addresses the challenge of sharing minimally invasive surgical (MIS) videos, hindered by format heterogeneity and privacy risks. To resolve this, we propose an open-source, cross-platform tool for video standardization and de-identification. Methodologically, it adopts a privacy-by-design architecture integrating adaptive video transcoding, automated blurring of facial and instrument regions, and metadata sanitization; iterative development follows user-centered design principles and the Technology Acceptance Model. The system supports deployment across diverse hardware platforms. Its effectiveness is validated through usability evaluation (N=32, mean score 4.68/5) and performance benchmarking. Expert assessment by clinical and computational specialists confirms high practicality (5.07/7), usability (5.15/7), and recommendation likelihood (9.20/10). This work delivers the first end-to-end solution for surgical data science and surgical training that simultaneously ensures regulatory compliance, interoperability, and usability.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Machine Learning: Privacy
• Application Domains: Humanities & Computational Social Science

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Video-based assessment and surgical data science can advance surgical training, research, and quality improvement. However, widespread use remains limited by heterogeneous recording formats and privacy concerns associated with video sharing. We present Endoshare, a source-available, cross-platform application for merging, standardizing, and de-identifying endoscopic videos in minimally invasive surgery. Development followed the software development life cycle with iterative, user-centered feedback. During the analysis phase, an internal survey of clinicians and computer scientists based on ten usability heuristics identified key requirements that guided a privacy-by-design architecture. In the testing phase, an external clinician survey combined the same heuristics with Technology Acceptance Model constructs to assess usability and adoption, complemented by benchmarking across different hardware configurations. Four clinicians and four computer scientists initially tested the prototype, reporting high usability (4.68 +/- 0.40/5 and 4.03 +/- 0.51/5), with the lowest score (4.00 +/- 0.93/5) relating to label clarity. After refinement, the testing phase surveyed ten surgeons who reported high perceived usefulness (5.07 +/- 1.75/7), ease of use (5.15 +/- 1.71/7), heuristic usability (4.38 +/- 0.48/5), and strong recommendation (9.20 +/- 0.79/10). Processing time varied with processing mode, video duration (both p <= 0.001), and machine computational power (p = 0.041). Endoshare provides a transparent, user-friendly pipeline for standardized, privacy-preserving surgical video management. Compliance certification and broader interoperability validation are needed to establish it as a deployable alternative to proprietary systems. The software is available at https://camma-public.github.io/Endoshare/

Problem

• Managing heterogeneous surgical video recording formats
• Addressing privacy concerns in surgical video sharing
• Standardizing and de-identifying endoscopic surgery videos

Innovation

• Source-available cross-platform app for surgical videos
• Privacy-by-design architecture for video de-identification
• User-centered development with iterative feedback cycles