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

Status

UNKNOWN

Phases

Study type

Interventional

Source

ClinicalTrials.gov

Registry ID

NCT05398887

Enrollment

200

Registered

2022-06-01

Start date

2022-06-15

Completion date

2022-10-01

Last updated

2022-06-01

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Lung Diseases

Keywords

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

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.

Interventions

RADIATIONLow radiation dose CT

Underwent low dose chest CT with 30% lower radiation dose

RADIATIONUnderwent ultra dose chest CT

Underwent ultra dose chest CT with 90% lower radiation dose

OTHERArtificial Intelligence based model

Deep-learning based contrast boosting algorithms

Study design

Allocation

RANDOMIZED

Intervention model

PARALLEL

Primary purpose

DIAGNOSTIC

Masking

QUADRUPLE (Subject, Caregiver, Investigator, Outcomes Assessor)

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

Healthy volunteers

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

Design outcomes

Primary

Measure	Time frame	Description
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

Measure	Time frame	Description
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.

Contacts

Primary ContactBayarbaatar Bold, M.D

bayarbaatar99@gmail.com976-99063486

Backup ContactKhulan Khurelsukh, M.D, MSc

khulan.kh@intermed.mn976-88010440

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026