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Ultra-low Dose CT imaging with a Deep Learning Algorithm in Body Composition Analysis

Ultra-low Dose CT imaging with a Deep Learning Algorithm in Body Composition Analysis

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CRIS
Registry ID
KCT0007446
Enrollment
100
Registered
2022-06-28
Start date
2022-07-01
Completion date
Unknown
Last updated
2022-09-20

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

Seoul National University Bundang Hospital
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: Adult male and female of age 20 to 65. Volunteers who reviewed and signed the informed consent form.

Exclusion criteria

Exclusion criteria: Pregnant, or potentially pregnant women Those having underlying disease Intellectual disability hampering understanding of the procedure Metalic prosthesis at the scan area

Design outcomes

Primary

MeasureTime frame
Intraclass correlation of body composition measurements made at the L3 vertebral body level (muscle area, visceral fat area, subcutaneous fat area), between low-dose CT image aided by artificial intelligence and full-dose CT image.—

Secondary

MeasureTime frame
Intraclass correlation of body composition measurements (muscle area, visceral fat area, subcutaneous fat area) between low-dose CT image unaided by artificial intelligence and full-dose CT image.—

Countries

Korea, Republic of

Contacts

Public ContactHae Young Kim

Seoul National University Bundang Hospital

qkfmrp860329@@gmail.com+82-31-787-7637

Outcome results

None listed

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