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

Study Overview
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Brief Summary
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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
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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.

Official Title
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Utilization and Effectiveness of Ultra-low-dose Chest Computed Tomography Using Innovative CT Denoising Solution Based on Deep Learning Technology

Conditions
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Lung Diseases

Intervention / Treatment
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* Radiation: Low radiation dose CT
* Radiation: Underwent ultra dose chest CT
* Other: Artificial Intelligence based model


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Diagnostic
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Quadruple


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Active Comparator: Low dose Chest CT scan<br>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<br>* Underwent low dose chest CT with 30% lower radiation dose<br>|
| Experimental: Ultra low dose CT scan with Artificial Intelligence<br>Interventions: Radiation: Low radiation dose CT Image quality Other: Deep-learning based contrast boosting algorithms | Radiation: Underwent ultra dose chest CT<br>* Underwent ultra dose chest CT with 90% lower radiation dose<br>Other: Artificial Intelligence based model<br>* Deep-learning based contrast boosting algorithms<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 | Within 2 weeks after data collection | 
| Contrast media dose | Administered contrast media dose in each patient | Within 2 weeks after data collection | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Image contrast | Signal to Noise, Noise and Edge-rise-distance on a five-point scale (1-5) with a higher score indicates better conspicuity. | Within 2 weeks after data collection | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Denoising technique, Ultra-low-dose Computed tomography, Computed tomography, Artificial Intelligence