Deep LearningNCT05550012
A New Deep-learning Based Artificial Intelligence Iterative Reconstruction (AIIR) Algorithm in Low-dose Liver CT
A study testing low-dose CT in people with Deep Learning, run by Qianfoshan Hospital at 1 site in China.
Trial summary
CT-enhanced scans are routine imaging modality for the diagnosis and follow-up of liver disease. However, this means that patients will receive more radiation dose. Therefore, it is necessary to reduce the radiation dose received by patients as much as possible. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Deep Learning
- What is being tested?
- low-dose CT
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 100 participants
- Where
- 1 siteChina
- Study dates
- 30 Sep 2022to 30 Apr 2023
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Who may be able to join
- those scheduled for contrast-enhanced liver CT
Who cannot join
- images affected by artifacts (motion or implants)
Trial locations and contacts
1 site in 1 country.
China1 site
Qianfoshan Hospital (The First Affiliated Hospital of Shandong First Medical University)
Jinan, Shandong
Qingshi Zeng, zengqs2021@163.com
About this study
Official title: Evaluation of a New Deep-learning Based Artificial Intelligence Iterative Reconstruction (AIIR) Algorithm in Different Enhancement Phases of Low-dose Liver CT
CT-enhanced scans are routine imaging modality for the diagnosis and follow-up of liver disease. However, this means that patients will receive more radiation dose. Therefore, it is necessary to reduce the radiation dose received by patients as much as possible. Deep learning-based reconstruction algorithms have been introduced to improve image quality recently. For many years, researchers attempt to maintain image quality using an advanced method while reducing radiation dose. Recently, a new deep-learning based iterative reconstruction algorithm, namely artificial intelligence iterative reconstruction (AIIR, United Imaging Healthcare, Shanghai, China) has been introduced. In this study, we evaluate the image and diagnostic qualities of AIIR for low-dose portal vein and delayed phase liver CT with those of a KARL method normally used in standard-dose CT.
Read the detailed description
In our hospital, patients with abdominal pelvic cancer undergo follow-up low-dose CT for the evaluation of treatment plan after clinical treatment or disease progress. The raw-data of low-dose CT were collected retrospectively and reconstructed using KARL and AIIR algorithm. In this study, we evaluate the image and diagnostic qualities of AIIR for low-dose portal vein and delayed phase liver CT with those of a KARL method normally used in standard-dose CT.
- Conditions
- Deep Learning
- Being tested
- low-dose CT
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
low-dose CTOther
those patients undergo low-dose liver CT in portal vein and delayed phase.
| Study group | Role | What they receive |
|---|---|---|
| standard-dose CT | No intervention | those patients undergo standard-dose liver CT in portal vein and delayed phase |
| low-dose CT | Experimental | those patients undergo low-dose liver CT in portal vein and delayed phase Receives: Other: low-dose CT |
What is being measured
The primary outcome is the main question the study is designed to answer.
signal-to-noise ratio (SNR)
Evaluate the image qualities of AIIR for low-dose portal vein and delayed phase liver CT with those of a KARL method normally used in standard-dose CT
Measured: 6 months
contrast to noise ratio (CNR)
Evaluate the image qualities of AIIR for low-dose portal vein and delayed phase liver CT with those of a KARL method normally used in standard-dose CT
Measured: 6 months
diagnostic confidence
Evaluate the diagnostic qualities of AIIR for low-dose portal vein and delayed phase liver CT with those of a KARL method normally used in standard-dose CT
Measured: 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Sep 2022
- Study start30 Sep 2022 (estimated)
- Primary completion30 Mar 2023 (estimated)
- Study completion30 Apr 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Deep LearningStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Qianfoshan Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Qingshi Zeng, Qianfoshan Hospital
- Investigators
- Qingshi Zeng, Study director, Qianfoshan Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05550012
- Sponsor's protocol ID
- LD-SH-2022
- Status last verified
- Sep 2022
- First submitted
- 14 Sep 2022
- Last updated
- 22 Sep 2022
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 22 Sep 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluation of a New Deep-learning Based Artificial Intelligence Iterative Reconstruction (AIIR) Algorithm in Different Enhancement Phases of Low-dose Liver CT
- ClinicalTrials.gov ID
- NCT05550012
- Acronym / sponsor ID
- LD-SH-2022
- Conditions
- Deep Learning
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- Qianfoshan HospitalUniversity, hospital or other
- Study start
- 30 Sep 2022 (estimated)
- Primary completion
- 30 Mar 2023 (estimated)
- Study completion
- 30 Apr 2023 (estimated)
- First posted
- 22 Sep 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
CT-enhanced scans are routine imaging modality for the diagnosis and follow-up of liver disease. However, this means that patients will receive more radiation dose. Therefore, it is necessary to reduce the radiation dose received by patients as much as possible. Deep learning-based reconstruction algorithms have been introduced to improve image quality recently. For many years, researchers attempt to maintain image quality using an advanced method while reducing radiation dose. Recently, a new deep-learning based iterative reconstruction algorithm, namely artificial intelligence iterative reconstruction (AIIR, United Imaging Healthcare, Shanghai, China) has been introduced. In this study, we evaluate the image and diagnostic qualities of AIIR for low-dose portal vein and delayed phase liver CT with those of a KARL method normally used in standard-dose CT.
Detailed description
In our hospital, patients with abdominal pelvic cancer undergo follow-up low-dose CT for the evaluation of treatment plan after clinical treatment or disease progress. The raw-data of low-dose CT were collected retrospectively and reconstructed using KARL and AIIR algorithm. In this study, we evaluate the image and diagnostic qualities of AIIR for low-dose portal vein and delayed phase liver CT with those of a KARL method normally used in standard-dose CT.
Study design
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| standard-dose CT | No intervention | –those patients undergo standard-dose liver CT in portal vein and delayed phase |
| low-dose CT | Experimental | Other: low-dose CTthose patients undergo low-dose liver CT in portal vein and delayed phase |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (1 listed)
- those scheduled for contrast-enhanced liver CT
Key exclusion criteria (1 listed)
- images affected by artifacts (motion or implants)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| low-dose CTthose patients undergo low-dose liver CT in portal vein and delayed phase. | Other | low-dose CT |
Primary outcome
signal-to-noise ratio (SNR)
Time frame: 6 months
Evaluate the image qualities of AIIR for low-dose portal vein and delayed phase liver CT with those of a KARL method normally used in standard-dose CT
contrast to noise ratio (CNR)
Time frame: 6 months
Evaluate the image qualities of AIIR for low-dose portal vein and delayed phase liver CT with those of a KARL method normally used in standard-dose CT
diagnostic confidence
Time frame: 6 months
Evaluate the diagnostic qualities of AIIR for low-dose portal vein and delayed phase liver CT with those of a KARL method normally used in standard-dose CT
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Sep 2022.
Related trials for Deep Learning
RecruitingObservational study
Prospective Observational Cohort Study for Predicting Unruptured Intracranial Aneurysm Rupture Risk Using Blood Pressure Variability and 3D Structural Evolution
Unruptured Intracranial Aneurysm, Intracranial Aneurysm Rupture
Active, not recruitingObservational study
Clinical Application of Artificial Intelligence Methods in Diagnostic Imaging of Pulmonary Nodules
Pulmonary Nodules, Lung Neoplasms
Active, not recruiting
Pai.ACT: AI-Driven ACT Chatbot for Mental Health Triage and Service Evaluation
Autism Spectrum Disorder, Attention Deficit Disorder With Hyperactivity (ADHD)
RecruitingObservational study
Artificial Intelligence-assisted Evaluation of Pulmonary HYpertension
Pulmonary Hypertension, Pulmonary Arterial Hypertension
Not yet recruitingObservational study
Replacing Spatial Filtering With Deep-learning Noise Reduction
Cardiac Disease
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Qingshi Zeng study director
Qianfoshan Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing low-dose CT in people with Deep Learning, run by Qianfoshan Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 22 Sep 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Qingshi Zeng (18560081565, zengqs2021@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
signal-to-noise ratio (SNR) (time frame: 6 months). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 22 Sep 2022.