AI Models to Reduce Surgical Complications Through Intraoperative Video Analysis: Protocol for a Prospective Cohort Study
Through the creation of an open dataset and the development of state-of-the-art deep learning models, this project seeks to transform the current paradigm in minimally invasive surgery. By providing the surgical AI community with robust, real-world data, the project aspires to catalyze innovations that will enhance surgical safety; refine predictive capabilities; and, ultimately, lead to better clinical outcomes.
Authors
- Ricardo Rocha (ORCID: https://orcid.org/0000-0001-5283-6860)
- Catarina Barata (ORCID: https://orcid.org/0000-0002-6478-8007)
- António Sampaio Soares (ORCID: https://orcid.org/0000-0001-7773-2427)
- Paulo Mira
- D Stoyanov
- Paulo Alves
- Manish Chand
- Laura T Castro
- Margarida Pascoal
- S. Bano
- Joao Costa
Institutions
- Hospital Prof. Dr. Fernando Fonseca (PT)
- Instituto Português de Oncologia Francisco Gentil (PT)
- Hospital de Egas Moniz (PT)
- Instituto Superior Técnico (PT)
- University College London (GB)
Publication Details
- Journal
- JMIR Research Protocols
- Published
- 2026-02-26
- DOI
- https://doi.org/10.2196/62734
- Primary Topic
- Surgical Simulation and Training
- Type
- article
- Field-Weighted Citation Impact
- 0.00