Decoding surgical proficiency and complexity: a machine learning framework for robotic herniorrhaphy
Objective performance indicators poorly predict case complexity independently, yet their temporal evolution reveals surgical skill acquisition. The concurrent stabilization of OPI stochasticity and progression to more complex cases demonstrates that surgical proficiency and complexity assessment are interdependent phenomena, establishing digital metrics as tools for understanding the dynamic relationship between surgeon learning and case difficulty.
Authors
- Fahri Gökçal (ORCID: https://orcid.org/0000-0003-0832-7991)
- Abeselom Fanta (ORCID: https://orcid.org/0000-0003-0592-861X)
- Thomas H. Shin (ORCID: https://orcid.org/0000-0003-2076-270X)
- Omar Yusef Kudsi (ORCID: https://orcid.org/0000-0001-6723-0909)
- Mallory C. Shields (ORCID: https://orcid.org/0000-0002-3331-9910)
- Cigdem Benlice
Institutions
- Brigham and Women's Hospital (US)
- Harvard University (US)
- Thomas Jefferson University (US)
- Kent Hastanesi (TR)
- Intuitive Surgical (United States) (US)
- Acıbadem Adana Hospital (TR)
- University of Virginia (US)
- Istanbul University (TR)
Publication Details
- Journal
- Surgical Endoscopy
- Published
- 2025-12-02
- DOI
- https://doi.org/10.1007/s00464-025-12412-x
- Primary Topic
- Surgical Simulation and Training
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Intuitive Foundation