Digital 3D Reconstruction Predicts Small Bowel Length (DTDRPSBL-BS)

Digital Three-dimensional Reconstruction for Predicting Small Bowel Length in Bariatric Surgery

The prevalence of type 2 diabetes mellitus (T2DM) has been increasing annually worldwide, and the prevalence of diabetes has reached 11.6% in China. Laparoscopic Roux-en-Y gastric bypass (RYGB) is still widely accepted as a valid surgery in the treatment of obesity and T2DM. But still, there is no consensus on the ideal of the gastric bypass limb lengths. Reported lengths of biliopancreatic limb (BPL) and alimentary limb (AL) varied widely from 10-250 to 35-250 cm, and anatomical data show that the length of small intestine varies greatly among adults. Choosing the same small bowel bypass length for different individuals obviously cannot achieve the expected weight loss effect, and individuals with too short small intestine can cause severe malnutrition complications and even life-threatening conditions. Therefore, measurement of small bowel length is one of the prerequisites for performing precise RYGB. Intraoperative measurement of small bowel length can increase the operative time and the risk of surgical complications such as intestinal perforation. So, predicting the total length of the small intestine is very important for accurately performing bariatric surgery and avoiding the risk of surgical complications. In this study, we propose to perform 3D segmentation and reconstruction of the small intestine by acquiring abdominal CT data through digital technology, and predict the small intestine length by 3D digital measurement of the small intestine, and verify the digital measurement data by performing digital measurement data. Establish a small bowel length prediction model for bariatric surgery to develop a more accurate and personalized gastric bypass surgery plan for patients to obtain weight loss and glucose control.

Study Overview

• Status: Recruiting
• Conditions: Small Intestine Length Measurement
• Intervention / Treatment: Diagnostic test: Small intestine length measurement method

• Study Type: Observational
• Enrollment (Anticipated): 100

Contacts and Locations

• Study Contact: Name: fan Li, PhD; Phone Number: 68729350; Email: levinecq@163.com

Study Locations

• China
  • Chongqing, China, 400042
    • Recruiting
    • Daping Hospital
    • Contact: fan Li, PhD; Phone Number: 68729350; Email: levinecq@163.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 16 years to 65 years (Child, Adult, Older Adult)
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Patients who underwent bariatric surgery in our hospital

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?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Open/laparoscopic surgery; Small bowel length measurement by laparoscopy or laparotomy
3D reconstruction; Small bowel length measurement by 3D digital model	Diagnostic test: Small intestine length measurement method; Accuracy of digital 3D reconstruction for predicting small bowel length

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Validation of the accuracy of the predicted length of the small intestine	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.	2 years

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Building prediction formulas through machine deep learning	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.	2 years

Collaborators and Investigators

• Sponsor: Daping Hospital and the Research Institute of Surgery of the Third Military Medical University
• Investigators: Study Director: fan Li, PhD, Army medical universtiy daping hospital

Study record dates

Study Major Dates

• Study Start (Actual): October 2, 2019
• Primary Completion (Anticipated): October 2, 2023
• Study Completion (Anticipated): October 2, 2023

Study Registration Dates

• First Submitted: April 14, 2022
• First Submitted That Met QC Criteria: April 14, 2022
• First Posted (Actual): April 21, 2022

Study Record Updates

• Last Update Posted (Actual): July 20, 2022
• Last Update Submitted That Met QC Criteria: July 17, 2022
• Last Verified: July 1, 2022

More Information

Terms related to this study

• Keywords: bariatric surgery; Type 2 diabetes mellitus; Roux-en-y gastric bypass; Three-Dimensional reconstruction
• Other Study ID Numbers: 3DRABL-BS

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No