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Development of a Deep Learning model for free flap monitoring for intraoral defects

Developing a language model for flap salvage using deep learning

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CRIS
Registry ID
KCT0009962
Enrollment
150
Registered
2024-11-28
Start date
2024-04-03
Completion date
Unknown
Last updated
2024-12-09

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

Yonsei University Health System, Dental Hospital
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: Patients who have undergone a free flap reconstruction for a defect, including intraoral, at a clinic during the eligible period.

Exclusion criteria

Exclusion criteria: Patients for whom the flap was not observed and there is no photograph or record of it, or for whom monitoring was performed and there is no record of it.

Design outcomes

Primary

MeasureTime frame
Diagnosis of vascular compromise in implanted free flaps—

Secondary

MeasureTime frame
Quantitative sensitivity for diagnosing vascular compromise in implanted free flaps—

Countries

Korea, Republic of

Contacts

Public ContactHyounmin Kim

Yonsei University Health System, Dental Hospital

netizen93@yuhs.ac+82-2-2228-3138

Outcome results

None listed

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