Considerations on diagnostic accuracy baseline generation for Imaging AI studies illustrated on the CT-based prediction of empyema.

Considerations on diagnostic accuracy baseline generation for Imaging AI studies illustrated on the CT-based prediction of empyema. - DTA-AI

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

DRKS

Registry ID

DRKS00025201

Enrollment

310

Registered

2021-05-19

Start date

2021-05-20

Completion date

Unknown

Last updated

2025-04-07

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

J86

Interventions

Group 1: "Intervention group": patients with empyema "Observation group": patients with pleural effusions (a distinction is also made between parapneumonic, benign and pleural effusions in general)

Eligibility

Sex/Gender

All

Age

18 Years to No maximum

Inclusion criteria

Inclusion criteria: Eligible patients are retrospectively identified based on the presence of a pleural effusion in the radiological report between 01/2016 and 01/2021. All routine 1mm chest CTs obtained at the University Hospital Basel regardless of contrast phase with pathological reports within 7 days.

Exclusion criteria

Exclusion criteria: No pathological finding. CT examinations with other acquisition parameters (e.g. CT chest / abdomen 1.5mm). Denied consent.

Design outcomes

Primary

Measure	Time frame
Sensitivity and specificity for blinded reports, radiological reports and consensus, both for image findings and for the radiological diagnosis of empyema with regard to benign, parapneumonic and pleural effusions in general.	—

Secondary

Measure	Time frame
DOR, negative predictive value, positive predictive value and AUCs/ROC analysis.	—

Countries

Switzerland

Contacts

Public ContactRaphael Sexauer

Universitätsspital Basel

raphael.sexauer@usb.ch0613286584

Outcome results

None listed

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