Utilization and Effectiveness of Ultra-low-dose Chest Computed Tomography Using Innovative CT Denoising Solution Based on Deep Learning Technology
Trial Snapshot
- Phase
- Not Applicable
- Sponsor
- Enrollment
- 200
- Primary Endpoint
- Contrast media dose
Study Overview
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.
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
- Allocation
- Randomized
- Intervention Model
- Parallel
- Primary Purpose
- Diagnostic
- Masking
- Quadruple (Participant, Care Provider, Investigator, Outcomes Assessor)
Eligibility Criteria
- Ages
- 18 Years to — (Adult, Older Adult)
- Sex
- 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
Arms & Interventions
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
Intervention: Low radiation dose CT (Radiation)
Ultra low dose CT scan with Artificial Intelligence
Interventions:
Radiation: Low radiation dose CT Image quality Other: Deep-learning based contrast boosting algorithms
Intervention: Underwent ultra dose chest CT (Radiation)
Ultra low dose CT scan with Artificial Intelligence
Interventions:
Radiation: Low radiation dose CT Image quality Other: Deep-learning based contrast boosting algorithms
Intervention: Artificial Intelligence based model (Other)
Outcomes
Primary Outcomes
Contrast media dose
Time Frame: Within 2 weeks after data collection
Administered contrast media dose in each patient
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
Secondary Outcomes
- Image contrast(Within 2 weeks after data collection)
Investigators
Bayarbaatar Bold
Principal Investigator, Bayarbaatar Bold, Diagnostic Radiologist, M.D, Intermed Hospital
Intermed Hospital