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Deep learning using computed tomography to identify high-risk patients for acute small bowel obstruction

Deep learning using computed tomography to identify high-risk patients for acute small bowel obstruction: development and validation of a prediction model : A retrospective cohort study

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CRIS
Registry ID
KCT0008330
Enrollment
600
Registered
2023-04-03
Start date
2023-02-12
Completion date
Unknown
Last updated
2023-05-02

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

None listed

Interventions

None listed

Sponsors

Ajou University
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: The inclusion criteria were: (1) Older than 18 years, (2) CT image findings with SBO, (3) Mechanism of obstruction caused by the adhesion. Before this process, we identified normal subjects (n = 1000) without any abnormal abdominal CT findings during the health screenings conducted in 2019.

Exclusion criteria

Exclusion criteria: Patients under the age of 18, intestinal obstruction caused by inflammatory bowel disease, intestinal obstruction caused by cancer, intestinal obstruction caused by cancer, intestinal disintegration caused by duodenum or colon obstruction, and intestinal obstruction within two weeks of abdominal surgery. In addition to the above findings, clinical cases that are not considered to be intestinal obstruction due to small intestine obstruction are excluded.

Design outcomes

Primary

MeasureTime frame
AUROC—

Secondary

MeasureTime frame
sensitivity, specificity, accuracy—

Countries

Korea, Republic of

Contacts

Public ContactHo-Jung.Shin

Ajou University Hospital

agnusdei13@gmail.com+82-31-219-7856

Outcome results

None listed

Source: CRIS (via WHO ICTRP) · Data processed: Feb 4, 2026