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Effectiveness of Ultra-low-dose Chest CT With AI Based Denoising Solution

NCT05398887 · Status: UNKNOWN · Phase: NA · Type: INTERVENTIONAL · Enrollment: 200

Last updated 2022-06-01

No results posted yet for this study

Summary

The main objective of the study is to evaluate the detection rate of pulmonary conditions, percentage of ionizing radiation dose reduction, and state of image quality of ULDCT coupling with innovative vendor-neutral CT denoising solution based on deep learning technology.

Conditions

  • Lung Diseases

Interventions

RADIATION

Low radiation dose CT

Underwent low dose chest CT with 30% lower radiation dose

RADIATION

Underwent ultra dose chest CT

Underwent ultra dose chest CT with 90% lower radiation dose

OTHER

Artificial Intelligence based model

Deep-learning based contrast boosting algorithms

Sponsors & Collaborators

  • Intermed Hospital

    lead OTHER

Principal Investigators

  • Khulan Khurelsukh, M.D, MSc · Intermed Hospital

  • Delgerekh Sainjargal, M.D, MSc · Intermed Hospital

  • Bayarbaatar Bold, M.D · Intermed Hospital

Study Design

Allocation
RANDOMIZED
Purpose
DIAGNOSTIC
Masking
QUADRUPLE
Model
PARALLEL

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2022-06-15
Primary Completion
2022-09-01
Completion
2022-10-01

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05398887 on ClinicalTrials.gov