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Deep learning reconstructed ultralow dose chest CT

Comparison of Reconstructed Ultralow dose Chest CT via Deep Learning Algorithm and Low dose Chest CT

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CRIS
Registry ID
KCT0005662
Enrollment
220
Registered
2020-12-04
Start date
2020-12-14
Completion date
Unknown
Last updated
2021-01-06

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 Hospital
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: - Adult patients who visited the institution during the study period and assigned to take low dose chest CT for clinical purpose - Who agreed to participate in the study

Exclusion criteria

Exclusion criteria: - patients' withdrawal - when the quality of the CT image is severely deteriorated due to artifacts

Design outcomes

Primary

MeasureTime frame
image noise at axillary fat—

Secondary

MeasureTime frame
image nose measured at other sites (lung parenchyma, aorta, pulmonary artery, trachea);signal-to-noise ratio;edge-rise-distance;skewness (heart, liver);diagnostic accuracy;nodule measurement variability;emphysema index (measured by Aview, Coreline Soft);radiation dose (CTDIvol, mGy)—

Countries

Korea, Republic of

Contacts

Public ContactJu Nam

Seoul National University Hospital

Outcome results

None listed

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