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Digital 3D Reconstruction Predicts Small Bowel Length

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

Status
UNKNOWN
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT05338567
Acronym
DTDRPSBL-BS
Enrollment
100
Registered
2022-04-21
Start date
2019-10-02
Completion date
2023-10-02
Last updated
2022-07-20

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Small Intestine Length Measurement

Keywords

bariatric surgery, Roux-en-y gastric bypass, Type 2 diabetes mellitus, Three-Dimensional reconstruction

Brief summary

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.

Interventions

DIAGNOSTIC_TESTSmall intestine length measurement method

Accuracy of digital 3D reconstruction for predicting small bowel length

Sponsors

Daping Hospital and the Research Institute of Surgery of the Third Military Medical University
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
RETROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
16 Years to 65 Years
Healthy volunteers
Yes

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.

Design outcomes

Primary

MeasureTime frameDescription
Validation of the accuracy of the predicted length of the small intestine2 yearsThe 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.

Secondary

MeasureTime frameDescription
Building prediction formulas through machine deep learning2 yearsThrough 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.

Countries

China

Contacts

Primary Contactfan Li, PhD
levinecq@163.com68729350

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026