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Prospective evaluation of deep learning-based detection model for chest radiographs in outpatient respiratory clinic

Prospective evaluation of deep learning-based detection model for chest radiographs in outpatient respiratory clinic

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CRIS
Registry ID
KCT0005466
Enrollment
329
Registered
2020-10-12
Start date
2020-10-13
Completion date
Unknown
Last updated
2021-08-30

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

Konyang University Hospital
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: Gender: both Age: min. 20 years old max. no limit Adult who visit the outpatient clinic of department of Pulmonology to undergo chest X-ray

Exclusion criteria

Exclusion criteria: 1. Those whose diagnostic model cannot be used due to poor quality of images. 2. Those who did not agree to participate in the study. 3. Those who are pregnant.

Design outcomes

Primary

MeasureTime frame
Diagnostic yield of radiologists and physicians for referable chest abnormality;Diagnostic performance comparisons between respiratory medicine physicians in the presence or absence of the AI aid

Secondary

MeasureTime frame
Number of chest CT scans performed or reserved to be performed in each arm;Proportion of the chest CT scans with referable abnormalities;Outpatient clinic follow-up rate or recall rate;Medical source caused by the chest radiograph taken in respiratory medicine;False referral rate of radiologists and physicians for referable chest abnormality;Diagnostic performance of the AI algorithm for referable abnormalities in chest radiographs

Countries

Korea, Republic of

Contacts

Public ContactYoung Jun Cho

Konyang University Hospital

cyj6299@daum.net+82-42-600-9686

Outcome results

None listed

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