Ignite Creation Date:
2024-07-17 @ 11:32 AM
Last Modification Date:
2024-10-26 @ 3:33 PM
Study NCT ID:
NCT06481358
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2024-07-01
First Post:
2024-06-25
Brief Title:
Deep Learning-based Artificial Intelligence for the Diagnosis of Small Bowel Obstruction
Sponsor:
Nagoya University
Organization:
Nagoya University
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:
None
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?:
None