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

May 30, 2022 updated by: Bayarbaatar Bold, Intermed Hospital

Utilization and Effectiveness of Ultra-low-dose Chest Computed Tomography Using Innovative CT Denoising Solution Based on Deep Learning Technology

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.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

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 Type

Interventional

Enrollment (Anticipated)

200

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (ADULT, OLDER_ADULT)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Description

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: DIAGNOSTIC
  • Allocation: RANDOMIZED
  • Interventional Model: PARALLEL
  • Masking: QUADRUPLE

Arms and Interventions

Participant Group / 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

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Detection rate of pulmonary conditions
Time Frame: 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
Within 2 weeks after data collection
Contrast media dose
Time Frame: Within 2 weeks after data collection
Administered contrast media dose in each patient
Within 2 weeks after data collection

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Image contrast
Time Frame: 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.
Within 2 weeks after data collection

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

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

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (ANTICIPATED)

June 15, 2022

Primary Completion (ANTICIPATED)

September 1, 2022

Study Completion (ANTICIPATED)

October 1, 2022

Study Registration Dates

First Submitted

May 25, 2022

First Submitted That Met QC Criteria

May 30, 2022

First Posted (ACTUAL)

June 1, 2022

Study Record Updates

Last Update Posted (ACTUAL)

June 1, 2022

Last Update Submitted That Met QC Criteria

May 30, 2022

Last Verified

May 1, 2022

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • IMC20220515-01

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

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

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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