SleeveData: Development of a Standard Dataset for Postoperative Outcomes Following Laparoscopic Sleeve Gastrectomy
- Conditions
- Obesity with an body mass index over 40
- Registration Number
- DRKS00033088
- Lead Sponsor
- niversitätsmedizin Mannheim
- Brief Summary
Not available
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- Pending
- Sex
- All
- Target Recruitment
- 300
Inclusion Criteria
Scheduled for LSG
- Written informed consent
Exclusion Criteria
- Previous bariatric or major upper gastrointestinal surgery
- Language barriers or impaired mental state
- Unable to attend follow-up examinations
Study & Design
- Study Type
- observational
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method To develop and validate a predictive algorithm using machine learning techniques that analyzes annotated surgical videos of laparoscopic sleeve gastrectomy to determine the likelihood of patients developing postoperative dysphagia, reflux symptoms, and to evaluate the degree of weight loss 12 months after LSG.
- Secondary Outcome Measures
Name Time Method •To identify and analyze the correlation between specific intraoperative events or techniques (as captured in surgical videos) and the development of postoperative symptoms, including dysphagia and reflux, as well as the extent of weight loss.<br>•To create and utilize a comprehensive dataset that combines surgical video annotations with clinical parameters (including postoperative symptomatology and weight loss metrics) for advanced research in bariatric surgery outcomes.<br>•To assess the feasibility and effectiveness of machine learning algorithms in predicting surgical outcomes, focusing on both immediate postoperative complications (like dysphagia and reflux) and long-term outcomes (such as weight loss), based on intraoperative video data.