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.
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
Allocation
RANDOMIZED
Purpose
DIAGNOSTIC
Masking
QUADRUPLE
Enrollment
200
Underwent low dose chest CT with 30% lower radiation dose
Underwent ultra dose chest CT with 90% lower radiation dose
Deep-learning based contrast boosting algorithms
Detection rate of pulmonary conditions
Pulmonary condition detection rate on low dose chest CT and ultra dose chest CT with artificial intelligence-based CT denoising solution by blinded reviewers
Time frame: Within 2 weeks after data collection
Contrast media dose
Administered contrast media dose in each patient
Time frame: Within 2 weeks after data collection
Image contrast
Signal to Noise, Noise and Edge-rise-distance on a five-point scale (1-5) with a higher score indicates better conspicuity.
Time frame: Within 2 weeks after data collection
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