COVID-19 Volumetric Quantification on Computer Tomography Using Computer Aided Diagnostics

Status

Completed

Phases

Study type

Interventional

Source

ClinicalTrials.gov

Registry ID

NCT05282056

Enrollment

200

Registered

2022-03-16

Start date

2022-02-24

Completion date

2022-03-15

Last updated

2022-05-04

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

Conditions

COVID-19

Keywords

CAD, AI, Artificial Intelligence

Brief summary

The aim of the study is to asses the influence of computer aided diagnostic to the process of lung affection quantification on computer tomography in COVID-19 confirmed patients.

Detailed description

The lung involvement of COVID-19 patients has been showed to be correlated to clinical outcomes and became part of the clinical practice. Even though various scores can be used, the affection estimation is usually done on computer tomography, using radiologists's estimation skills which is a highly subjective process. Artificial intelligence is a known objective constant and therefore a potential radiologist complement. This trial aims at studying the effect of using a computer aided diagnostic software integrated in the normal clinical practice of radiologists from Timisoara County Emergency Hospital. It uses the AI-PROBE analysis setup, which turns off the CAD outputs for randomly chosen 50% the cases (control) and then compares the radiological reports for differences between the two arms.

Interventions

DIAGNOSTIC_TESTCAD analysis

CAD shows the radiologist automatically delineated areas of potential COVID-19 affection, together with an overall lung affection percentage.

Study design

Allocation

RANDOMIZED

Intervention model

PARALLEL

Primary purpose

DIAGNOSTIC

Masking

SINGLE (Subject)

Masking description

The random assignment is done automatically by the CAD system and is not visible to the patient. The radiologist obviously sees which cases have CAD analysis and which not.

Intervention model description

CAD outputs are turned off for randomly chosen 50% of patients, which represent the control group. The other 50% are analysed using CAD

Eligibility

Sex/Gender

ALL

Age

16 Years to No maximum

Healthy volunteers

Inclusion criteria

* RT-PCR confirmed patients of COVID-19

Exclusion criteria

* 15 or lower

Design outcomes

Primary

Measure	Time frame	Description
Mean difference of lung affection quantification percentage	At CT acquisition time, up to 2 weeks	The objective measurement of lung affection percentage is measured against pixel level labels. A lower difference mean better outcome.

Countries

Romania

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 11, 2026