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Total Small Bowel Length Measurement Using Computed Tomography and Magnetic Resonance Imaging in Obese Patients (SBOM-AI)

Primary Purpose

Obesity, Bariatric Surgery Candidate

Status
Not yet recruiting
Phase
Not Applicable
Locations
Study Type
Interventional
Intervention
Measurement of the total small bowel length using CT scan and MRI with 3D reconstruction and AI tool
Sponsored by
University of Roma La Sapienza
About
Eligibility
Locations
Arms
Outcomes
Full info

About this trial

This is an interventional diagnostic trial for Obesity focused on measuring obesity, artificial intelligence, computed tomography, bariatric surgery, small bowel lenght, metabolic surgery, magnetic resonance

Eligibility Criteria

18 Years - 80 Years (Adult, Older Adult)All SexesDoes not accept healthy volunteers

Inclusion Criteria: BMI > 35 kg/m2 and at least one obesity-related comorbidity BMI > 40 kg/m2 failure of at least six months of dietary and/or medical treatment of obesity indication for intervention validated after multidisciplinary evaluation in a specific board meeting

Sites / Locations

    Arms of the Study

    Arm 1

    Arm Type

    Experimental

    Arm Label

    Artificial intelligence training cohort and validation cohort

    Arm Description

    Three high-volume Italian centers will enroll 195 obese patients who are candidates for metabolic surgery for obesity. Part of them will be established a training cohort (total = 105 patients), used to set up the AI-based method of TSBL measurement. The other 90 patients (30 for each center) will represent the validation cohort.

    Outcomes

    Primary Outcome Measures

    Concordance between AI-based total small bowel length measure and laparoscopic total small bowel length measure
    the main outcome is to set up and validate a reliable and reproducible automated method using preoperative radiological imaging to measure the TSBL in patients candidates for laparoscopic bariatric/metabolic surgery. The results of AI measurement will be compared with those of laparoscopic measurement to examine the level of concordance

    Secondary Outcome Measures

    Full Information

    First Posted
    September 13, 2023
    Last Updated
    September 26, 2023
    Sponsor
    University of Roma La Sapienza
    Collaborators
    University of Padova, Federico II University
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    1. Study Identification

    Unique Protocol Identification Number
    NCT06065917
    Brief Title
    Total Small Bowel Length Measurement Using Computed Tomography and Magnetic Resonance Imaging in Obese Patients
    Acronym
    SBOM-AI
    Official Title
    Set up and Validation of Total Small Bowel Length Measurement Using Computed Tomography and Magnetic Resonance Imaging With 3D Reconstruction and Artificial Intelligence Tool in Obese Patients Candidates to Metabolic Surgery
    Study Type
    Interventional

    2. Study Status

    Record Verification Date
    September 2023
    Overall Recruitment Status
    Not yet recruiting
    Study Start Date
    October 2023 (Anticipated)
    Primary Completion Date
    November 2025 (Anticipated)
    Study Completion Date
    February 2026 (Anticipated)

    3. Sponsor/Collaborators

    Responsible Party, by Official Title
    Principal Investigator
    Name of the Sponsor
    University of Roma La Sapienza
    Collaborators
    University of Padova, Federico II University

    4. Oversight

    Studies a U.S. FDA-regulated Drug Product
    No
    Studies a U.S. FDA-regulated Device Product
    No
    Data Monitoring Committee
    No

    5. Study Description

    Brief Summary
    The aim of the study is to set up and validate a reliable and reproducible automated method using preoperative radiological imaging to measure the TSBL in patients undergoing laparoscopic bariatric/metabolic surgery.
    Detailed Description
    The total length of the small intestine (TSBL) represents a crucial parameter for obtaining a safe and successful minimally invasive surgery in metabolic/bariatric bypass surgery. Nowadays, the standard of small intestine measurement is the intraoperative measurement. Laparoscopy represents the standard approach for baratric/metabolic, making the TSBL measurement time-consuming and risky in case of intestinal lesions. An accurate and effective non-invasive preoperative measurement of the TSBL will allow to evaluate the variability of the TSBL, which affects the surgical strategy. Cross-sectional imaging could play an important role in this setting thanks to the possibility of measuring in a non-invasive way the TSBL. Some studies performed with both Computed Tomography (CT) and Magnetic Resonance (MR) report promising results. However, they are limited by the small size of the sample, the lack of standardized technique and the lack of an automatic method based on Artificial Intelligence (AI). The evaluation of a reliable preoperative method to measure TSBL using cross-sectional imaging will potentially reduce intraoperative complications and insufficient long-term weight loss or nutritional deficiencies. In this scenario a possible solution could be the implementation of analysis method through the development of an AI algorithm capable of automatically segmenting the small intestine. The PRIMARY END POINT of this study is to set up and validate a reliable and reproductible automatic method to measure the TSBL in patients candidates for laparoscopic bariatric/metabolic surgery, based on preoperative radiological imaging The main phases of the project will be: evaluate the feasibility of preoperative CT and MRI-base measurement of the TSBL in a large cohort of obese patients and compare radiological measurement with intraoperative laparoscopic measurement (method of elongation) as a reference standard (1). Evaluate the more accurate cross-sectional imaging between CT and MRI to measure the length of the small intestine. Build an AI tool that can automatically measure TSBL on transversal slice imaging. Three high-volume Italian centers will enroll 195 obese patients who are candidates for metabolic surgery for obesity. Part of them will be established training cohort (total = 105 patients), used to set up the AI-based method of TSBL measurement. The other 90 patients (30 for each center) will represent the validation cohort.

    6. Conditions and Keywords

    Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
    Obesity, Bariatric Surgery Candidate
    Keywords
    obesity, artificial intelligence, computed tomography, bariatric surgery, small bowel lenght, metabolic surgery, magnetic resonance

    7. Study Design

    Primary Purpose
    Diagnostic
    Study Phase
    Not Applicable
    Interventional Study Model
    Single Group Assignment
    Masking
    None (Open Label)
    Allocation
    N/A
    Enrollment
    195 (Anticipated)

    8. Arms, Groups, and Interventions

    Arm Title
    Artificial intelligence training cohort and validation cohort
    Arm Type
    Experimental
    Arm Description
    Three high-volume Italian centers will enroll 195 obese patients who are candidates for metabolic surgery for obesity. Part of them will be established a training cohort (total = 105 patients), used to set up the AI-based method of TSBL measurement. The other 90 patients (30 for each center) will represent the validation cohort.
    Intervention Type
    Diagnostic Test
    Intervention Name(s)
    Measurement of the total small bowel length using CT scan and MRI with 3D reconstruction and AI tool
    Intervention Description
    The intervention consists in performing CT and MR imaging with small bowel length measurement before bariatric/metabolic surgery in obese patients. Then, during surgery the patients will undergo laparoscopic stretched small bowel measurement as the reference gold standard method to measure the small bowel length. The imaging of the training cohort will be used to trained an AI to set up an automatic method of small bowel length measurement via the analysis of CT and MRI imaging.
    Primary Outcome Measure Information:
    Title
    Concordance between AI-based total small bowel length measure and laparoscopic total small bowel length measure
    Description
    the main outcome is to set up and validate a reliable and reproducible automated method using preoperative radiological imaging to measure the TSBL in patients candidates for laparoscopic bariatric/metabolic surgery. The results of AI measurement will be compared with those of laparoscopic measurement to examine the level of concordance
    Time Frame
    1 month

    10. Eligibility

    Sex
    All
    Minimum Age & Unit of Time
    18 Years
    Maximum Age & Unit of Time
    80 Years
    Accepts Healthy Volunteers
    No
    Eligibility Criteria
    Inclusion Criteria: BMI > 35 kg/m2 and at least one obesity-related comorbidity BMI > 40 kg/m2 failure of at least six months of dietary and/or medical treatment of obesity indication for intervention validated after multidisciplinary evaluation in a specific board meeting
    Central Contact Person:
    First Name & Middle Initial & Last Name or Official Title & Degree
    Niccolò Petrucciani
    Phone
    3496311476
    Email
    niccolo.petrucciani@uniroma1.it

    12. IPD Sharing Statement

    Plan to Share IPD
    No

    Learn more about this trial

    Total Small Bowel Length Measurement Using Computed Tomography and Magnetic Resonance Imaging in Obese Patients

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