Real-time AI detection of surgical smoke during endoscopic laparoscopic procedures
Development of AI-Based Approaches for Automated Real-Time Detection of Surgical Smoke Using Endoscopic Image and Video Data
University Hospital Tuebingen · NCT07397000
This pilot will test whether an AI system can detect surgical smoke in real time from endoscopic video during laparoscopic procedures that use high-frequency surgical devices.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 30 (estimated) |
| Ages | 18 Years and up |
| Sex | Female |
| Sponsor | University Hospital Tuebingen (other) |
| Locations | 1 site (Tübingen) |
| Trial ID | NCT07397000 on ClinicalTrials.gov |
What this trial studies
This is a prospective, monocentric observational pilot that will collect endoscopic video and device activation logs to build a training dataset for an AI model. Women undergoing laparoscopic procedures with anticipated smoke generation from high-frequency (HF) surgical devices and use of a Karl Storz S‑Pilot smoke evacuation system will be enrolled. The S‑Pilot activation by clinical staff will serve as the reference standard, and model performance will be measured on held-out test data with a target F1 score ≥ 0.8. No experimental interventions will be applied; the study records intraoperative data to train and validate the computer-assisted detection algorithm.
Who should consider this trial
Good fit: Adult women (≥18) scheduled for laparoscopic surgery using HF devices and a Karl Storz S‑Pilot system who can give informed consent are ideal candidates.
Not a fit: Patients who do not undergo HF electrosurgery, cannot have the S‑Pilot device used, or cannot provide informed consent are unlikely to benefit.
Why it matters
Potential benefit: If successful, the AI could give immediate alerts when smoke appears, helping staff reduce exposure and improve visualization during laparoscopic surgery.
How similar studies have performed: Related AI work in endoscopic image analysis and surgical workflow recognition has shown promise, but real-time automated detection of surgical smoke is novel and only sparsely reported.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * \- Age ≥ 18 years * Written consent after receiving information * Indication for surgical treatment using HF surgery Exclusion Criteria: * \- Expected lack of compliance by the patient or inability of the patient to understand the meaning and purpose of the clinical trial * Lack of patient consent * S-Pilot cannot be used
Where this trial is running
Tübingen
- Department of Women's Health, University Hospital — Tübingen, Germany (RECRUITING)
Study contacts
- Principal investigator: Prof. Dr. Bernhard Krämer — Deparment of Women's Health, University Hospital Tübingen
- Study coordinator: Prof. Dr. Bernhard Krämer
- Email: bernhard.kraemer@med.uni-tuebingen.de
- Phone: +497071 2982211
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.
Conditions: Detection of Surgical Smoke Gas Using AI