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Untangling surgical gesture analysis-are we even speaking the same language? a systematic review
Rikke Groth Olsen1,2,3, Annarita Ghosh Andersen4,5, Andrew J Hung6
1Copenhagen Academy for Medical Education and Simulation (CAMES), Center for HR & Education, the Capital Region of Denmark, Ryesgade 53B, 2100, Copenhagen, Denmark. rikke.groth.olsen.01@regionh.dk.
Surgical Endoscopy
|July 31, 2025
Summary
Surgical gesture analysis shows promise for assessing surgeon skill in minimally invasive surgery. However, inconsistent reporting and definitions hinder AI model development and clinical application.
Area of Science:
- Medical Informatics
- Surgical Technology
- Artificial Intelligence in Medicine
Background:
- Surgeons' technical skill directly impacts patient outcomes, necessitating improved surgical quality assessment methods.
- Surgical gesture analysis, leveraging AI models, offers a novel approach to evaluating surgical performance.
- This systematic review assesses the evidence for surgical gesture use in minimally invasive surgery.
Purpose of the Study:
- To systematically review the current evidence on surgical gesture analysis in minimally invasive surgery.
- To understand how surgical gestures have been defined and applied in existing literature.
- To identify gaps and challenges in the field.
Main Methods:
- A systematic literature review was conducted by searching four electronic databases.
- Studies on minimally invasive surgery assessed with surgical gestures were identified.
- Quality and risk of bias were assessed using established criteria (Joanna Briggs Institute, QUADAS-2).
Main Results:
- 75 studies were included, categorized by engineering (59), educational (24), and clinical (4) use.
- Surgical gestures were primarily used for assessing surgeon experience and providing feedback.
- Few studies (4) explored predicting patient outcomes, showing potential over traditional clinical features.
Conclusions:
- Surgical gesture analysis holds potential for competency and quality assessment in surgery.
- Lack of consensus on terminology, methodology, and data granularity impedes AI model development.
- Further research requires multi-disciplinary collaboration to advance the field, especially for outcome prediction.
