Deep Learning-based Artificial Intelligence for the Diagnosis of Small Bowel Obstruction

June 25, 2024 updated by: Aitaro Takimoto, Nagoya University

Study Using Deep Learning-based Artificial Intelligence for the Diagnosis of Small Bowel Obstruction

The study will compare the diagnostic accuracy and time to diagnosis of computed tomography images of patients with suspected intestinal obstruction seen in the emergency room by residents and surgeons, with and without artificial intelligence.

Study Overview

Status

Active, not recruiting

Conditions

Intervention / Treatment

Detailed Description

DESIGN: This is an diagnostic study. SETTING: We developed a deep learning-based AI technology to automatically extract the intestinal tract from CT images using 5 200 CT images of 158 patients. The CT images of patients who visited the emergency department and were suspected of small bowel obstruction between June 6 and July 26, 2018, were obtained from two tertiary referral centers, which were used as the test samples. Data analysis was completed in December 2023.

PARTICIPANTS: Residents and surgeons participated in the study. INTERVENTIONS: Residents and surgeons were divided into two groups: one group read using the AI technology, and the other group read without the AI technology.

MAIN OUTCOMES AND MEASURES: Participants indicated whether or not small bowel obstruction and obstruction location. The time for diagnosis was also collected. We applied a hierarchical Bayesian model.

Study Type

Observational

Enrollment (Actual)

17

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Japan
    • Aichi
      • Nagoya, Aichi, Japan, 4668560
        • Nagoya University Graduate School of Medicine

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Probability Sample

Study Population

20 people

Description

Inclusion Criteria:

  • Persons with documented consent

Exclusion Criteria:

  • Persons without documented consent

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
AI group
Participants read CT images with AI.
Diagnostic test: Artificial intelligence
AI extract intestinal region and reconstruct into 3D image.
Manual group
Participants read CT images without AI

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
The diagnosis of the obstruction site
Time Frame: September, 2024
Accuracy of diagnosis of the obstruction site
September, 2024

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Nagoya University

Investigators

  • Study Chair: Hieoo Uchida, PhD., Nagoya University Graduate School of Medicine, Pediatric Surgery

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

September 1, 2022

Primary Completion (Actual)

June 6, 2023

Study Completion (Estimated)

October 31, 2024

Study Registration Dates

First Submitted

June 25, 2024

First Submitted That Met QC Criteria

June 25, 2024

First Posted (Actual)

July 1, 2024

Study Record Updates

Last Update Posted (Actual)

July 1, 2024

Last Update Submitted That Met QC Criteria

June 25, 2024

Last Verified

June 1, 2024

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2022-0188

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

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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