Effectiveness of Ultra-low-dose Chest CT With AI Based Denoising Solution
- Conditions
- Lung Diseases
- Interventions
- Radiation: Low radiation dose CTRadiation: Underwent ultra dose chest CTOther: Artificial Intelligence based model
- Registration Number
- NCT05398887
- Lead Sponsor
- Intermed Hospital
- 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.
Recruitment & Eligibility
- Status
- UNKNOWN
- Sex
- All
- Target Recruitment
- 200
- Patients aged over 18-year-old
- Patients undergoing CT Chest for all purpose
- 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 & Design
- Study Type
- INTERVENTIONAL
- Study Design
- PARALLEL
- Arm && Interventions
Group Intervention Description Low dose Chest CT scan Low radiation dose CT Underwent low dose chest CT with 30% lower radiation dose Interventions: Radiation: Low radiation dose CT Other: Image quality analysis Ultra low dose CT scan with Artificial Intelligence Underwent ultra dose chest CT Interventions: Radiation: Low radiation dose CT Image quality Other: Deep-learning based contrast boosting algorithms Ultra low dose CT scan with Artificial Intelligence Artificial Intelligence based model Interventions: Radiation: Low radiation dose CT Image quality Other: Deep-learning based contrast boosting algorithms
- Primary Outcome Measures
Name Time Method 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
Name Time Method 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.