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

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.

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.

Computed tomography, Artificial Intelligence, Denoising technique, Ultra-low-dose Computed tomography

Underwent low dose chest CT with 30% lower radiation dose Interventions: Radiation: Low radiation dose CT Other: Image quality analysis

Interventions: Radiation: Low radiation dose CT Image quality Other: Deep-learning based contrast boosting algorithms

Pulmonary condition detection rate on low dose chest CT and ultra dose chest CT with artificial intelligence-based CT denoising solution by blinded reviewers

Signal to Noise, Noise and Edge-rise-distance on a five-point scale (1-5) with a higher score indicates better conspicuity.

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

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