Effectiveness of Ultra-low-dose Chest CT With AI Based Denoising Solution
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 |
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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
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
- 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
- Contrast media dose [Within 2 weeks after data collection]
Administered contrast media dose in each patient
Secondary Outcome Measures
- 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
Inclusion Criteria:
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Patients aged over 18-year-old
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Patients undergoing CT Chest for all purpose
Exclusion Criteria:
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Age less than 18 years
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Any suspicion of pregnancy
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History of thoracic surgery or placement of the metallic device in the thorax
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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
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- IMC20220515-01