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AI Models to Reduce Surgical Complications Through Intraoperative Video Analysis: Protocol for a Prospective Cohort

António Sampaio Soares1, Sophia Bano2, Laura T Castro3

  • 1School of Medicine, University of Lisbon, Av. Prof. Egas Moniz MB, Lisbon, 1649-028, Portugal, 351 963864555.

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|March 2, 2026
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This study develops AI models using intraoperative videos to predict surgical complications, creating an open-source dataset to improve patient safety in minimally invasive surgery.

Keywords:
AIappendectomyartificial intelligencecholecystectomydatasetintraoperative videosurgery

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Area of Science:

  • Artificial Intelligence in Surgery
  • Medical Data Science
  • Minimally Invasive Surgical Techniques

Background:

  • Postoperative complications significantly impact patients and healthcare systems.
  • Current methods lack automated prediction of complications from intraoperative video.
  • Advancements in AI and data storage enable new predictive approaches.

Purpose of the Study:

  • Develop and validate deep learning models for predicting postoperative complications using the Clavien-Dindo scale.
  • Create and share an open-source dataset of intraoperative videos and patient outcomes.
  • Enhance surgical safety and clinical outcomes through AI-driven insights.

Main Methods:

  • Prospective cohort study of 1200 patients undergoing minimally invasive abdominal surgery.
  • Collection of preoperative, intraoperative video, and 30-day postoperative outcome data (Clavien-Dindo scale).
  • Development of deep learning models using supervised learning on annotated surgical videos and surgeon-defined strategies.

Main Results:

  • Data collection ongoing from 2024-2025, with planned outputs including a research protocol, results, and an open-source dataset.
  • The study aims to advance AI-assisted surgery through the development of predictive models.
  • The open-source dataset will facilitate collaboration and innovation in surgical AI.

Conclusions:

  • Creation of an open dataset and deep learning models aims to transform minimally invasive surgery.
  • Providing real-world data will catalyze innovations to enhance surgical safety and predictive capabilities.
  • The project seeks to lead to better clinical outcomes in abdominal surgery.