Digital 3D Reconstruction Predicts Small Bowel Length (DTDRPSBL-BS)
Digital Three-dimensional Reconstruction for Predicting Small Bowel Length in Bariatric Surgery
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Anticipated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: fan Li, PhD
- Phone Number: 68729350
- Email: levinecq@163.com
Study Locations
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Chongqing, China, 400042
- Recruiting
- Daping Hospital
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Contact:
- fan Li, PhD
- Phone Number: 68729350
- Email: levinecq@163.com
-
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Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
Inclusion Criteria:
- Patients eligible for surgical treatment of T2DM were selected based on the Chinese Guidelines for the Surgical Treatment of Obesity and Type 2 Diabetes (2019 )
Exclusion Criteria:
- Adhesions, peritonitis, and patients who have had bowel resection (small intestine, colon or rectum) that hinder the measurement of the entire intestinal length
- Non-weight loss surgery patients whose incision is less than 6 cm are not suitable for measuring the length of the small intestine.
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
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Open/laparoscopic surgery
Small bowel length measurement by laparoscopy or laparotomy
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|
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3D reconstruction
Small bowel length measurement by 3D digital model
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Accuracy of digital 3D reconstruction for predicting small bowel length
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What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Validation of the accuracy of the predicted length of the small intestine
Time Frame: 2 years
|
The accuracy of the 3D reconstruction method was judged by comparing the length of the small intestine measured by the open/laparoscopic surgery with the length of the small intestine calculated by the preoperative CT 3D reconstruction.
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2 years
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Building prediction formulas through machine deep learning
Time Frame: 2 years
|
Through robotic deep learning, the small intestine is automatically segmented, and the small intestine is reconstructed in three dimensions to calculate the length of the small intestine.
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2 years
|
Collaborators and Investigators
Sponsor
Sponsor
Investigators
Investigators
- Study Director: fan Li, PhD, Army medical universtiy daping hospital
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Anticipated)
Primary Completion
Study Completion (Anticipated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Keywords
Other Study ID Numbers
Other Study ID Numbers
- 3DRABL-BS
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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