Digital 3D Reconstruction Predicts Small Bowel Length

Daping Hospital and the Research Institute of Surgery of the Third Military Medical University100 enrolled

Overview

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 Type

OBSERVATIONAL

Enrollment

100

Conditions

Small Intestine Length Measurement

Interventions

Eligibility

Sex: ALLMin age: 16 YearsMax age: 65 YearsHealthy volunteers:

Medical Language ↔ Plain English

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.

Outcomes

Primary Outcomes

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.

Time frame: 2 years

Secondary Outcomes

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.