Deep learning-based multiparametric analysis of COVID-19 patients from a radiological perspective, a monocentric retrospective study

Deep learning-based multiparametric analysis of COVID-19 patients from a radiological perspective, a monocentric retrospective study - Deep learning-based analysis COVID-19

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

DRKS

Registry ID

DRKS00023844

Enrollment

200

Registered

2023-02-13

Start date

2021-02-15

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

COVID-19 Infection

Interventions

Group 1: Patients with corresponding chest CT imaging in which COVID-19-typical changes could be registered, taking into account the inclusion and exclusion criteria. Subsequently, the image datasets

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: -CT chest examination COVID-19-associated changes -SarsCov2-RT PCR examination

Exclusion criteria

Exclusion criteria: -Missing imaging (CT thorax) -Incomplete examination due to premature termination -Non evaluable examination due to limited image quality

Design outcomes

Primary

Measure	Time frame
Diagnostic validity of CT and imaging biomarkers in the evaluation of COVID-19 infection.	—

Secondary

Measure	Time frame
Identify COVID-19-specific morphologics via artificial intelligence on CT thoracic exams.	—

Countries

Germany

Contacts

Public ContactBastian Sabel

LMU

Bastian.Sabel@med.uni-muenchen.de+4989440076642

Outcome results

None listed

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