RadiologyNCT06589843
Artificial Intelligent Image Processing and Diagnosis of Pulmonary Vessels in CT
An observational study of Radiology and Vascular Diseases, run by Xin Lou.
Trial summary
In this study, patients with chest pain, lung cancer, pulmonary embolism, and routine inpatient physical examination were selected as the research objects, and the experimental design of retrospective cohort study was adopted to carry out artificial intelligence analysis related to pulmonary vascular diseases in… Read more about this study
- Who is it for?
- Ages 18 to 100 years
- Conditions the study is for
- Radiology, Vascular Diseases
- What is being tested?
- Deep learning imaging enhancement
- How many people
- 15,000 participants
- Where
- No sites listed yet
- Study dates
- 10 Sep 2024to 1 Sep 2029
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 18 to 100 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients with chest pain, lung cancer, pulmonary embolism, and routine inpatient physical examination were studied
Who may be able to join
- Age ≥18≤100 years old Scan the pulmonary artery and its major branches Patients with suspected pulmonary embolism who received CTPA had a set of CTPA and CT scans The image quality meets the requirements of diagnosis and post-processing Patients who completed the examination in accordance with the data collection criteria Clinical data and follow-up were complete
Who cannot join
- Age \<18 years or age \>100 years The image is incomplete or incorrect Pulmonary artery absent or underenhanced Severe motion artifacts or image noise affect evaluation of pulmonary embolism History of aortic reconstruction, replacement, or stent implantation Congenital variations in the whole or important branches of the aorta in adults (e.g. bovine aortic arch, abnormal right subclavian artery) Severe hypovolemia and hemodynamic instability Severe heart failure with low ejection fraction Dialysis patient
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
In this study, patients with chest pain, lung cancer, pulmonary embolism, and routine inpatient physical examination were selected as the research objects, and the experimental design of retrospective cohort study was adopted to carry out artificial intelligence analysis related to pulmonary vascular diseases in patients with multi-dimensional big data. The multi-modal CT acquisition process included plain scan CT(NCCT) and CT pulmonary angiography (CTPA). Ctpa-like image effects can be simulated or reconstructed by non-enhanced plain scan CT images, so that CTPA-like image quality can be obtained without injecting contrast agent. The synthetic CTPA images were further analyzed by artificial intelligence to assist doctors in the intelligent diagnosis of pulmonary vascular diseases.
Read the detailed description
A non-enhanced plain scan CT image simulates or reconstructs an image effect similar to that of CTPA through the following technical solutions:
- Data acquisition: Obtain plain scan CT image data of the examined person, including multiple layers of image slices.
- Image preprocessing: Preprocessing of plain scan CT images, including denoising, enhancing contrast and other steps, to improve image quality and lay the foundation for subsequent processing.
- Vascular segmentation: Advanced image segmentation algorithms, such as the deep learning-based segmentation method, are used to segment the vascular structure from the preprocessed plain scan CT images. The key to this step is to accurately identify and extract vascular areas while reducing interference from non-vascular tissue.
- Blood vessel enhancement: For the segmented blood vessel structure, a specific image enhancement algorithm is used to enhance blood vessels to make them clearer and more continuous.
- Image synthesis: The enhanced vascular image is fused with the original plain scan CT image to generate the final CTPA image. During the synthesis process, the contrast between blood vessels and surrounding tissues can be adjusted as needed to optimize the display effect.
- Post-processing and evaluation: Post-processing of synthesized CTPA images, such as smoothing, artifact removal, etc., and quality assessment to ensure that the images meet the needs of clinical diagnosis.
- Conditions
- Radiology, Vascular Diseases
- Being tested
- Deep learning imaging enhancement
- Medical subject headings
- Vascular Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Deep learning imaging enhancementDiagnostic test
Conventional imaging or down-sampling imaging from CT or MR are enhanced by approved deep learning method.
What is being measured
The primary outcome is the main question the study is designed to answer.
The performance of deep enhanced imaging in lesion detection and diagnosis
The performance of deep enhanced imaging in lesion detection and diagnosis, including imaging quality, accuracy, sensitivity and specificity in lesion detection and imaging diagnosis.
Measured: 2 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Sep 2024
- Study start10 Sep 2024 (estimated)
- Primary completion1 Sep 2029 (estimated)
- Study completion1 Sep 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for RadiologyStudies 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
- Observational
- Observational model
- Case only
- Time perspective
- Prospective
- Enrolment
- 15,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Xin Lou
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Xin Lou, Chinese PLA General Hospital
- Investigators
- Xin Lou, Study chair, Chinese PLA General Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06589843
- Sponsor's protocol ID
- NCCT-CTPA AI
- Status last verified
- Sep 2024
- First submitted
- 6 Sep 2024
- Last updated
- 19 Sep 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 19 Sep 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligent Image Processing and Diagnosis of Pulmonary Vessels in CT
- ClinicalTrials.gov ID
- NCT06589843
- Acronym / sponsor ID
- NCCT-CTPA AI
- Conditions
- Radiology, Vascular Diseases
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 15,000 participants (estimated)
- Lead sponsor
- Xin LouUniversity, hospital or other
- Study start
- 10 Sep 2024 (estimated)
- Primary completion
- 1 Sep 2029 (estimated)
- Study completion
- 1 Sep 2029 (estimated)
- First posted
- 19 Sep 2024 (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.
In this study, patients with chest pain, lung cancer, pulmonary embolism, and routine inpatient physical examination were selected as the research objects, and the experimental design of retrospective cohort study was adopted to carry out artificial intelligence analysis related to pulmonary vascular diseases in patients with multi-dimensional big data. The multi-modal CT acquisition process included plain scan CT(NCCT) and CT pulmonary angiography (CTPA). Ctpa-like image effects can be simulated or reconstructed by non-enhanced plain scan CT images, so that CTPA-like image quality can be obtained without injecting contrast agent. The synthetic CTPA images were further analyzed by artificial intelligence to assist doctors in the intelligent diagnosis of pulmonary vascular diseases.
Detailed description
A non-enhanced plain scan CT image simulates or reconstructs an image effect similar to that of CTPA through the following technical solutions:
- Data acquisition: Obtain plain scan CT image data of the examined person, including multiple layers of image slices.
- Image preprocessing: Preprocessing of plain scan CT images, including denoising, enhancing contrast and other steps, to improve image quality and lay the foundation for subsequent processing.
- Vascular segmentation: Advanced image segmentation algorithms, such as the deep learning-based segmentation method, are used to segment the vascular structure from the preprocessed plain scan CT images. The key to this step is to accurately identify and extract vascular areas while reducing interference from non-vascular tissue.
- Blood vessel enhancement: For the segmented blood vessel structure, a specific image enhancement algorithm is used to enhance blood vessels to make them clearer and more continuous.
- Image synthesis: The enhanced vascular image is fused with the original plain scan CT image to generate the final CTPA image. During the synthesis process, the contrast between blood vessels and surrounding tissues can be adjusted as needed to optimize the display effect.
- Post-processing and evaluation: Post-processing of synthesized CTPA images, such as smoothing, artifact removal, etc., and quality assessment to ensure that the images meet the needs of clinical diagnosis.
Study design
- Observational model
- Case only
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 to 100 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients with chest pain, lung cancer, pulmonary embolism, and routine inpatient physical examination were studied
Key inclusion criteria (1 listed)
- Age ≥18≤100 years old Scan the pulmonary artery and its major branches Patients with suspected pulmonary embolism who received CTPA had a set of CTPA and CT scans The image quality meets the requirements of diagnosis and post-processing Patients who completed the examination in accordance with the data collection criteria Clinical data and follow-up were complete
Key exclusion criteria (1 listed)
- Age \<18 years or age \>100 years The image is incomplete or incorrect Pulmonary artery absent or underenhanced Severe motion artifacts or image noise affect evaluation of pulmonary embolism History of aortic reconstruction, replacement, or stent implantation Congenital variations in the whole or important branches of the aorta in adults (e.g. bovine aortic arch, abnormal right subclavian artery) Severe hypovolemia and hemodynamic instability Severe heart failure with low ejection fraction Dialysis patient
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 |
|---|---|---|
| Deep learning imaging enhancementConventional imaging or down-sampling imaging from CT or MR are enhanced by approved deep learning method. | Diagnostic test | – |
Primary outcome
The performance of deep enhanced imaging in lesion detection and diagnosis
Time frame: 2 year
The performance of deep enhanced imaging in lesion detection and diagnosis, including imaging quality, accuracy, sensitivity and specificity in lesion detection and imaging diagnosis.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 Sep 2024.
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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.
Xin Lou study chair
Chinese PLA General Hospital
Questions about this trial
What is this clinical trial studying?
An observational study of Radiology and Vascular Diseases, run by Xin Lou.
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: opening soon, as of the sponsor's last update on 19 Sep 2024. Status can change between updates, so confirm with the study contact before making plans.
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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Ling Liu (+8618601288132, 1179143043@qq.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The performance of deep enhanced imaging in lesion detection and diagnosis (time frame: 2 year). 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 19 Sep 2024.