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Development of a deep-learning model for free air detection in abdominal computed tomography images for surgeon support

A study on the identification of free air in abdominal computed tomography in the emergency department

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CRIS
Registry ID
KCT0010818
Enrollment
127
Registered
2025-07-28
Start date
2022-05-01
Completion date
Unknown
Last updated
2025-08-18

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

The Catholic University of Korea, Eunpyeong St. Mary's Hospital
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: Patients who underwent contrast CT, patients with perforated ulcer with free air

Exclusion criteria

Exclusion criteria: Patients who underwent non-contrast CT, patients with perforated ulcer without free air

Design outcomes

Primary

MeasureTime frame
image-level analysis—

Secondary

MeasureTime frame
Dice score, Patient-level analysis—

Countries

Korea, Republic of

Contacts

Public ContactDong Jin Kim

The Catholic University of Korea, Eunpyeong St. Mary's Hospital

djdjap@catholic.ac.kr+82-2-2030-4647

Outcome results

None listed

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