Bowel ObstructionNCT06481358
Deep Learning-based Artificial Intelligence for the Diagnosis of Small Bowel Obstruction
An observational study of Bowel Obstruction and Artificial Intelligence, run by Nagoya University at 1 site in Japan.
Trial summary
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. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Bowel Obstruction, Artificial Intelligence
- What is being tested?
- Artificial intelligence
- How many people
- 17 participants
- Where
- 1 siteJapan
- Study dates
- 1 Sep 2022to 31 Oct 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: 20 people
Who may be able to join
- Persons with documented consent
Who cannot join
- Persons without documented consent
Trial locations and contacts
1 site in 1 country.
Japan1 site
Nagoya University Graduate School of Medicine
Nagoya, Aichi-ken, 4668560
About this study
Official title: 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.
Read the 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.
- Conditions
- Bowel Obstruction, Artificial Intelligence
- Being tested
- Artificial intelligence
- Medical subject headings
- Intestinal Obstruction
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial intelligenceDiagnostic test
AI extract intestinal region and reconstruct into 3D image.
| Study group | Role | What they receive |
|---|---|---|
| AI group | Participants read CT images with AI. Receives: Diagnostic Test: Artificial intelligence | |
| Manual group | Participants read CT images without AI |
What is being measured
The primary outcome is the main question the study is designed to answer.
The diagnosis of the obstruction site
Accuracy of diagnosis of the obstruction site
Measured: September, 2024
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Jul 2024
- Study start1 Sep 2022
- Primary completion6 Jun 2023
- Study completion31 Oct 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Bowel ObstructionStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Other
- Enrolment
- 17 participants (actual)
Sponsor and investigators
- Lead sponsor
- Nagoya University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Aitaro Takimoto, Nagoya University
- Investigators
- Hieoo Uchida, PhD., Study chair, Nagoya University Graduate School of Medicine, Pediatric Surgery
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06481358
- Sponsor's protocol ID
- 2022-0188
- Status last verified
- Jun 2024
- First submitted
- 25 Jun 2024
- Last updated
- 1 Jul 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 1 Jul 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Study Using Deep Learning-based Artificial Intelligence for the Diagnosis of Small Bowel Obstruction
- ClinicalTrials.gov ID
- NCT06481358
- Acronym / sponsor ID
- 2022-0188
- Conditions
- Bowel Obstruction, Artificial Intelligence
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 17 participants (actual)
- Lead sponsor
- Nagoya UniversityUniversity, hospital or other
- Study start
- 1 Sep 2022 (actual)
- Primary completion
- 6 Jun 2023 (actual)
- Study completion
- 31 Oct 2024 (estimated)
- First posted
- 1 Jul 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Other
- Time perspective
- Other
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| AI group | – | Diagnostic Test: Artificial intelligenceParticipants read CT images with AI. |
| Manual group | – | –Participants read CT images without AI |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- 20 people
Key inclusion criteria (1 listed)
- Persons with documented consent
Key exclusion criteria (1 listed)
- Persons without documented consent
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial intelligenceAI extract intestinal region and reconstruct into 3D image. | Diagnostic test | AI group |
Primary outcome
The diagnosis of the obstruction site
Time frame: September, 2024
Accuracy of diagnosis of the obstruction site
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 1 Jul 2024.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Hieoo Uchida, PhD. study chair
Nagoya University Graduate School of Medicine, Pediatric Surgery
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Bowel Obstruction and Artificial Intelligence, run by Nagoya University at 1 site in Japan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 1 Jul 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Japan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The diagnosis of the obstruction site (time frame: September, 2024). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 1 Jul 2024.