Clinical trial NCT05398887: Effectiveness of Ultra-low-dose Chest CT With AI Based Denoising Solution
Official title: Utilization and Effectiveness of Ultra-low-dose Chest Computed Tomography Using Innovative CT Denoising Solution Based on Deep Learning Technology
Study type: INTERVENTIONAL
Overall status: UNKNOWN
Phase: NA
Conditions: Lung Diseases
Keywords: Computed tomography
Artificial Intelligence
Denoising technique
Ultra-low-dose Computed tomography
Lead sponsor: Intermed Hospital
Sponsor class: OTHER
First posted: 2022-06-01
Enrollment: 200
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: DIAGNOSTIC

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.

Eligibility criteria: 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

Primary outcomes:
measure: Detection rate of pulmonary conditions
timeFrame: Within 2 weeks after data collection
measure: Contrast media dose
timeFrame: Within 2 weeks after data collection
Secondary outcomes:
measure: Image contrast
timeFrame: Within 2 weeks after data collection
Other outcomes:
Locations:
Study officials:
affiliation: Intermed Hospital
name: Khulan Khurelsukh, M.D, MSc
role: STUDY_CHAIR
affiliation: Intermed Hospital
name: Delgerekh Sainjargal, M.D, MSc
role: PRINCIPAL_INVESTIGATOR
affiliation: Intermed Hospital
name: Bayarbaatar Bold, M.D
role: PRINCIPAL_INVESTIGATOR
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