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Decentralized, privacy-preserving surgical video analysis with Swarm Learning.

Oliver Lester Saldanha1,2, Kevin Pfeiffer1, Sebastian Bodenstedt3,4

  • 1Else Kroener Fresenius Center for Digital Health, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.

Medrxiv : the Preprint Server for Health Sciences
|November 19, 2025
PubMed
Summary

Artificial intelligence in surgical video analysis is advancing with weakly supervised deep learning and Swarm Learning. This privacy-preserving method enables multicenter collaboration, achieving performance comparable to centralized learning for appendicitis staging.

Keywords:
Surgical data scienceappendectomydecentralized learningdeep learninglaparoscopic video

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Surgical Video Analysis

Background:

  • Current AI in surgical video analysis is limited by manual annotations and data privacy concerns hindering multicenter collaboration.
  • Lack of patient-level outcome integration restricts the clinical utility of AI models.
  • Data privacy issues impede the sharing of laparoscopic video data.

Purpose of the Study:

  • To develop and evaluate a pipeline integrating weakly supervised deep learning with Swarm Learning for privacy-preserving collaborative surgical video analysis.
  • To address limitations of manual annotations and data privacy in AI-driven surgical video analysis.
  • To assess the performance of decentralized learning for patient-level disease staging tasks in appendicitis.

Main Methods:

  • Developed a pipeline combining weakly supervised deep learning with Swarm Learning, a decentralized approach for collaborative model training without data centralization.
  • Utilized a dataset of 397 laparoscopic appendectomy recordings from six international centers.
  • Evaluated model performance on binary detection of perforated appendicitis, laparoscopic appendicitis grading, and histopathologic inflammation grading.

Main Results:

  • Optimal configurations included 1.0 frames per second sampling and the SurgTempoNet architecture for appendicitis grading.
  • Swarm Learning consistently outperformed single-center training and matched centralized learning performance across all disease staging tasks.
  • User surveys identified hardware failure and limited electronic health record integration as barriers to clinical implementation.

Conclusions:

  • Weakly supervised deep learning allows prediction of patient-level outcomes from surgical videos.
  • Swarm Learning enables privacy-preserving multicenter collaboration, achieving performance comparable to centralized learning.
  • Decentralized learning holds significant potential for advancing collaborative AI in surgical video analysis, particularly when integrated with electronic health records.