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

Sponsor
Intermed Hospital (Other)
Overall Status
Not yet recruiting
CT.gov ID
NCT05398887
Collaborator
(none)
200
2
3.5

Study Details

Study Description

Brief 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.

Condition or Disease Intervention/Treatment Phase
  • Radiation: Low radiation dose CT
  • Radiation: Underwent ultra dose chest CT
  • Other: Artificial Intelligence based model
N/A

Detailed Description

Considering lung cancer-related public health challenges, a reliable lung cancer screening method for high-risk cohorts in Mongolia is needed. Thus, our study aims to assess the detection rate of pulmonary conditions, percentage of ionizing radiation dose reduction, and state of image quality of ULDCT coupling with artificial intelligence based CT denoising technique among various patient groups.

Study Design

Study Type:
Interventional
Anticipated Enrollment :
200 participants
Allocation:
Randomized
Intervention Model:
Parallel Assignment
Masking:
Quadruple (Participant, Care Provider, Investigator, Outcomes Assessor)
Primary Purpose:
Diagnostic
Official Title:
Utilization and Effectiveness of Ultra-low-dose Chest Computed Tomography Using Innovative CT Denoising Solution Based on Deep Learning Technology
Anticipated Study Start Date :
Jun 15, 2022
Anticipated Primary Completion Date :
Sep 1, 2022
Anticipated Study Completion Date :
Oct 1, 2022

Arms and Interventions

Arm Intervention/Treatment
Active Comparator: Low dose Chest CT scan

Underwent low dose chest CT with 30% lower radiation dose Interventions: Radiation: Low radiation dose CT Other: Image quality analysis

Radiation: Low radiation dose CT
Underwent low dose chest CT with 30% lower radiation dose

Experimental: Ultra low dose CT scan with Artificial Intelligence

Interventions: Radiation: Low radiation dose CT Image quality Other: Deep-learning based contrast boosting algorithms

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

Outcome Measures

Primary Outcome Measures

  1. Detection rate of pulmonary conditions [Within 2 weeks after data collection]

    Pulmonary condition detection rate on low dose chest CT and ultra dose chest CT with artificial intelligence-based CT denoising solution by blinded reviewers

  2. Contrast media dose [Within 2 weeks after data collection]

    Administered contrast media dose in each patient

Secondary Outcome Measures

  1. Image contrast [Within 2 weeks after data collection]

    Signal to Noise, Noise and Edge-rise-distance on a five-point scale (1-5) with a higher score indicates better conspicuity.

Eligibility Criteria

Criteria

Ages Eligible for Study:
18 Years and Older
Sexes Eligible for Study:
All
Accepts Healthy Volunteers:
No
Inclusion Criteria:
  • Patients aged over 18-year-old

  • Patients undergoing CT Chest for all purpose

Exclusion Criteria:
  • Age less than 18 years

  • Any suspicion of pregnancy

  • History of thoracic surgery or placement of the metallic device in the thorax

  • An inability to hold respiration during CT

Contacts and Locations

Locations

No locations specified.

Sponsors and Collaborators

  • Intermed Hospital

Investigators

  • Study Chair: Khulan Khurelsukh, M.D, MSc, Intermed Hospital
  • Principal Investigator: Delgerekh Sainjargal, M.D, MSc, Intermed Hospital
  • Principal Investigator: Bayarbaatar Bold, M.D, Intermed Hospital

Study Documents (Full-Text)

None provided.

More Information

Publications

Responsible Party:
Bayarbaatar Bold, Principal Investigator, Bayarbaatar Bold, Diagnostic Radiologist, M.D, Intermed Hospital, Intermed Hospital
ClinicalTrials.gov Identifier:
NCT05398887
Other Study ID Numbers:
  • IMC20220515-01
First Posted:
Jun 1, 2022
Last Update Posted:
Jun 1, 2022
Last Verified:
May 1, 2022
Individual Participant Data (IPD) Sharing Statement:
No
Plan to Share IPD:
No
Studies a U.S. FDA-regulated Drug Product:
No
Studies a U.S. FDA-regulated Device Product:
No
Product Manufactured in and Exported from the U.S.:
No
Keywords provided by Bayarbaatar Bold, Principal Investigator, Bayarbaatar Bold, Diagnostic Radiologist, M.D, Intermed Hospital, Intermed Hospital
Additional relevant MeSH terms:

Study Results

No Results Posted as of Jun 1, 2022