Ignite Creation Date:
2025-12-25 @ 12:14 AM
Ignite Modification Date:
2026-01-03 @ 1:47 PM
Study NCT ID:
NCT06481358
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2024-07-01
First Post:
2024-06-25
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Deep Learning-based Artificial Intelligence for the Diagnosis of Small Bowel Obstruction
Sponsor:
Nagoya University
Organization:
Study Overview
Official Title:
Study Using Deep Learning-based Artificial Intelligence for the Diagnosis of Small Bowel Obstruction
Status:
ACTIVE_NOT_RECRUITING
Status Verified Date:
2024-06
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief 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.
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.
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 Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: