Carotid AtherosclerosisNCT05230576
Intelligent Detection of Carotid Plaque and Its Stability Based on Deep Learning Dynamic Ultrasound Scanning
An observational study of Carotid Atherosclerosis, run by Jia Liu at 1 site in China.
Trial summary
This study intends to build a model through deep learning that can automatically and accurately detect plaques, calculate the lumen stenosis rate and evaluate the stability of plaques based on the carotid transverse axis dynamic ultrasound images and contrast-enhanced ultrasound images, so as to comprehensively… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Carotid Atherosclerosis
- What is being tested?
- Deep learning training cohort and Deep learning validation cohort
- How many people
- 2,000 participants
- Where
- 1 siteChina
- Study dates
- 1 May 2020to 1 Jun 2023
- Phase
- Observational
- 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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Age≥18 years old, gender is not limited, with varying degrees of carotid atherosclerosis.
Who may be able to join
- (1) Age≥18 years old, gender is not limited. (2) Patients who voluntarily participated in this study signed the informed consent.
Who cannot join
- (1) Severe cerebrovascular disease, uncooperative patients, and those who cannot tolerate examination. (2) Wound dressings after neck surgery affects carotid artery ultrasonography. (3) The neck is short and thick, and the probe cannot be put down vertically.
Trial locations and contacts
1 site in 1 country.
China1 site
The Third Affiliated Hospital of Sun Yat-sen University
Guangzhou, Guangdong
About this study
Official title: Deep Learning Model Based on Routine Ultrasound Scanning Video to Help Doctors Improve the Diagnosis of Carotid Plaque
This study intends to build a model through deep learning that can automatically and accurately detect plaques, calculate the lumen stenosis rate and evaluate the stability of plaques based on the carotid transverse axis dynamic ultrasound images and contrast-enhanced ultrasound images, so as to comprehensively evaluate the possibility of carotid plaques. cardiovascular risk. The successful development of this study will automatically simulate and reproduce the whole process of carotid plaque assessment by clinical sonographers. Solve the problem of ultrasonic inspection equipment and experience dependence. It is expected to carry out large-scale population intelligent screening, providing new ideas for early prevention and treatment. Especially in medically underdeveloped remote areas and the lack of experienced sonographers, it has great practical value in clinical health care and can bring greater social and economic benefits.
Read the detailed description
Background:
Carotid plaque is harmful to human health. According to estimates by the World Health Organization, 6.7 million cerebrovascular accidents and strokes occur each year, mainly related to the formation of carotid atherosclerotic plaques. On the one hand, carotid artery plaque can cause carotid artery stenosis or even occlusion, causing cerebral ischemia. Early detection and accurate assessment of carotid plaques are helpful for clinicians to take effective intervention measures, which can significantly reduce the disability rate and fatality rate of stroke.
Carotid CTA and MRA can provide relatively high-resolution and high-quality plaque images, but have cost and scanning limitations that limit their application in daily clinical practice. Ultrasonography has the advantages of non-invasiveness, convenience, low cost, and good repeatability. It is the preferred imaging method for plaque detection, stenosis and plaque stability. Contrast-enhanced ultrasonography (CEUS) can sensitively demonstrate intra-plaque microcirculation perfusion by injecting microbubble contrast agents, and is consistent with histopathological findings, and has been increasingly used clinically to evaluate plaque stability.
However, on the one hand, the limitation of ultrasound examination is that it needs to rely on the level of instruments and operators to improve the accuracy. On the other hand, with the growth of the population base and the aging of society, the traditional medical model has been unable to meet the annual increase in the number of patients. examination needs of patients. Therefore, it is of great significance to develop an integrated AI application platform that can automatically and accurately detect plaque based on ultrasound image data, and evaluate lumen stenosis and plaque stability.
Purpose:
This study intends to build a model based on deep learning to automatically and accurately detect plaque based on the carotid transverse axis dynamic ultrasound image, calculate the lumen stenosis rate, and perform stability assessment, so as to comprehensively evaluate the possible cardiovascular effects of carotid plaque. risk. It will realize the automatic simulation and reproduction of the whole process of assessment of cervical plaque by clinical ultrasound experts.
Study design:
Two-thirds of the enrolled patients and their corresponding carotid artery dynamic scan images and expert diagnosis results were randomly selected as the deep learning training cohort. The carotid artery dynamic scan images and expert diagnosis results of the remaining 1/3 patients were used as a validation cohort to evaluate the overall diagnostic accuracy of the deep learning model
Statistical Analysis:
The sensitivity, specificity, positive predictive value, and negative predictive value of deep learning for detecting plaque, estimating luminal stenosis rate, or predicting plaque stability were calculated by the area under the receiver operating characteristic (ROC) curve (AUROC) to evaluate. Statistical analysis was performed using SPSS 22.0 software.
Quality Control:
Develop standardized and standard carotid ultrasound examination methods and operating procedures, and develop unified image acquisition and storage standards. All operators are rigorously trained in carotid ultrasonography. Two operators with more than 5 years of experience in ultrasound operation were hired as quality control personnel to review all images and exclude unqualified images.
Ultrasound is safe and radiation-free. During the examination, the doctor and the patient were always in a state of communication, and the patient felt less nervous and fearful, with good tolerance and high compliance.
Ethics of the study:
This research will follow the ethical guidelines of the Declaration of Helsinki of the World Medical Congress and the relevant norms and regulations of clinical research. The study will begin after the approval of the ethics committee. Before the start of the study, the investigator should inform the subjects of all relevant contents of the clinical study in easy-to-understand language, and inform the patients that they have the right to withdraw from the study at any time. The study was started only after the patients signed the informed consent voluntarily.
- Conditions
- Carotid Atherosclerosis
- Being tested
- Deep learning training cohort, Deep learning validation cohort
- Medical subject headings
- Carotid Artery Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Deep learning training cohortDiagnostic test
train the deep learning model
Deep learning validation cohortDiagnostic test
evaluate the model
| Study group | Role | What they receive |
|---|---|---|
| Deep learning training cohort | 2/3 of the enrolled patients and their corresponding carotid artery dynamic scan images and expert diagnosis results were randomly selected as the training cohort for deep learning. Receives: Diagnostic Test: Deep learning training cohort | |
| Deep learning validation cohort | The carotid artery dynamic scan images and expert diagnosis results of the remaining 1/3 patients were used as a validation cohort to evaluate the overall diagnostic accuracy of the deep learning model. Receives: Diagnostic Test: Deep learning validation cohort |
What is being measured
The primary outcome is the main question the study is designed to answer.
AI assists junior radiologists to read images, and primary physicians read images independently
Taking the reading results of senior sonographers as the gold standard, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of AI-assisted reading and independent reading by junior physicians for carotid plaque-assisted diagnosis were tested. AUC is evaluated.
Measured: through study completion, an average of 2 years
Assessing the performance of AI model
Taking the reading results of senior sonographers as the gold standard, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of AI independent reading. It was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC).
Measured: through study completion, an average of 2 years
AI estimates the lumen stenosis rate
Taking the reading results of senior sonographers as the gold standard, AI can estimate the sensitivity, specificity, accuracy, positive predictive value and negative predictive value of lumen stenosis rate. It was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC).
Measured: through study completion, an average of 2 years
AI predicts plaque stability.
Taking the reading results of senior sonographers as the gold standard, AI predicts the sensitivity, specificity, accuracy, positive predictive value and negative predictive value of plaque stability. It was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC).
Measured: through study completion, an average of 2 years
Plaque detection by AI model on videos acquired by different types of equipment.
Taking the reading results of senior sonographers as the gold standard, AI detects plaque sensitivity, specificity, accuracy, positive predictive value, and negative predictive value on different ultrasound equipment. Assessed by the area under the receiver operating characteristic (ROC) curve (AUC).
Measured: through study completion, an average of 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Feb 2022
- Study start1 May 2020
- Primary completion1 Jun 2023
- Study completion1 Jun 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (7)Papers the sponsor has linked to this study.
- Other trials for Carotid AtherosclerosisStudies 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 2,000 participants (actual)
Sponsor and investigators
- Lead sponsor
- Jia Liu
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Jia Liu, Sun Yat-sen University
- Investigators
- Jia Liu, Principal investigator, Third Affiliated Hospital, Sun Yat-Sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05230576
- Sponsor's protocol ID
- [2020]02-255-01
- Status last verified
- Sep 2023
- First submitted
- 27 Jan 2022
- Last updated
- 14 Sep 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Abbott AL, Paraskevas KI, Kakkos SK, Golledge J, Eckstein HH, Diaz-Sandoval LJ, Cao L, Fu Q, Wijeratne T, Leung TW, Montero-Baker M, Lee BC, Pircher S, Bosch M, Dennekamp M, Ringleb P. Systematic Review of Guidelines for the Management of Asymptomatic and Symptomatic Carotid Stenosis. Stroke. 2015 Nov;46(11):3288-301. doi: 10.1161/STROKEAHA.115.003390. Epub 2015 Oct 8.
PubMed 26451020Nighoghossian N, Derex L, Douek P. The vulnerable carotid artery plaque: current imaging methods and new perspectives. Stroke. 2005 Dec;36(12):2764-72. doi: 10.1161/01.STR.0000190895.51934.43. Epub 2005 Nov 10.
PubMed 16282537Saba L, Saam T, Jager HR, Yuan C, Hatsukami TS, Saloner D, Wasserman BA, Bonati LH, Wintermark M. Imaging biomarkers of vulnerable carotid plaques for stroke risk prediction and their potential clinical implications. Lancet Neurol. 2019 Jun;18(6):559-572. doi: 10.1016/S1474-4422(19)30035-3. Epub 2019 Apr 4.
PubMed 30954372Rafailidis V, Charitanti A, Tegos T, Destanis E, Chryssogonidis I. Contrast-enhanced ultrasound of the carotid system: a review of the current literature. J Ultrasound. 2017 Feb 9;20(2):97-109. doi: 10.1007/s40477-017-0239-4. eCollection 2017 Jun.
PubMed 28592999Deyama J, Nakamura T, Takishima I, Fujioka D, Kawabata K, Obata JE, Watanabe K, Watanabe Y, Saito Y, Mishina H, Kugiyama K. Contrast-enhanced ultrasound imaging of carotid plaque neovascularization is useful for identifying high-risk patients with coronary artery disease. Circ J. 2013;77(6):1499-507. doi: 10.1253/circj.cj-12-1529. Epub 2013 Mar 22.
PubMed 23519900Varetto G, Gibello L, Castagno C, Quaglino S, Ripepi M, Benintende E, Gattuso A, Garneri P, Zan S, Capaldi G, Bertoldo U, Rispoli P. Use of Contrast-Enhanced Ultrasound in Carotid Atherosclerotic Disease: Limits and Perspectives. Biomed Res Int. 2015;2015:293163. doi: 10.1155/2015/293163. Epub 2015 Jun 21.
PubMed 26180793Staub D, Patel MB, Tibrewala A, Ludden D, Johnson M, Espinosa P, Coll B, Jaeger KA, Feinstein SB. Vasa vasorum and plaque neovascularization on contrast-enhanced carotid ultrasound imaging correlates with cardiovascular disease and past cardiovascular events. Stroke. 2010 Jan;41(1):41-7. doi: 10.1161/STROKEAHA.109.560342. Epub 2009 Nov 12.
PubMed 19910551
Study at a glance
From the registry record, last updated by the sponsor on 14 Sep 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep Learning Model Based on Routine Ultrasound Scanning Video to Help Doctors Improve the Diagnosis of Carotid Plaque
- ClinicalTrials.gov ID
- NCT05230576
- Acronym / sponsor ID
- [2020]02-255-01
- Conditions
- Carotid Atherosclerosis
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 2,000 participants (actual)
- Lead sponsor
- Jia LiuUniversity, hospital or other
- Study start
- 1 May 2020 (actual)
- Primary completion
- 1 Jun 2023 (actual)
- Study completion
- 1 Jun 2023 (actual)
- First posted
- 9 Feb 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.
This study intends to build a model through deep learning that can automatically and accurately detect plaques, calculate the lumen stenosis rate and evaluate the stability of plaques based on the carotid transverse axis dynamic ultrasound images and contrast-enhanced ultrasound images, so as to comprehensively evaluate the possibility of carotid plaques. cardiovascular risk. The successful development of this study will automatically simulate and reproduce the whole process of carotid plaque assessment by clinical sonographers. Solve the problem of ultrasonic inspection equipment and experience dependence. It is expected to carry out large-scale population intelligent screening, providing new ideas for early prevention and treatment. Especially in medically underdeveloped remote areas and the lack of experienced sonographers, it has great practical value in clinical health care and can bring greater social and economic benefits.
Detailed description
Background:
Carotid plaque is harmful to human health. According to estimates by the World Health Organization, 6.7 million cerebrovascular accidents and strokes occur each year, mainly related to the formation of carotid atherosclerotic plaques. On the one hand, carotid artery plaque can cause carotid artery stenosis or even occlusion, causing cerebral ischemia. Early detection and accurate assessment of carotid plaques are helpful for clinicians to take effective intervention measures, which can significantly reduce the disability rate and fatality rate of stroke.
Carotid CTA and MRA can provide relatively high-resolution and high-quality plaque images, but have cost and scanning limitations that limit their application in daily clinical practice. Ultrasonography has the advantages of non-invasiveness, convenience, low cost, and good repeatability. It is the preferred imaging method for plaque detection, stenosis and plaque stability. Contrast-enhanced ultrasonography (CEUS) can sensitively demonstrate intra-plaque microcirculation perfusion by injecting microbubble contrast agents, and is consistent with histopathological findings, and has been increasingly used clinically to evaluate plaque stability.
However, on the one hand, the limitation of ultrasound examination is that it needs to rely on the level of instruments and operators to improve the accuracy. On the other hand, with the growth of the population base and the aging of society, the traditional medical model has been unable to meet the annual increase in the number of patients. examination needs of patients. Therefore, it is of great significance to develop an integrated AI application platform that can automatically and accurately detect plaque based on ultrasound image data, and evaluate lumen stenosis and plaque stability.
Purpose:
This study intends to build a model based on deep learning to automatically and accurately detect plaque based on the carotid transverse axis dynamic ultrasound image, calculate the lumen stenosis rate, and perform stability assessment, so as to comprehensively evaluate the possible cardiovascular effects of carotid plaque. risk. It will realize the automatic simulation and reproduction of the whole process of assessment of cervical plaque by clinical ultrasound experts.
Study design:
Two-thirds of the enrolled patients and their corresponding carotid artery dynamic scan images and expert diagnosis results were randomly selected as the deep learning training cohort. The carotid artery dynamic scan images and expert diagnosis results of the remaining 1/3 patients were used as a validation cohort to evaluate the overall diagnostic accuracy of the deep learning model
Statistical Analysis:
The sensitivity, specificity, positive predictive value, and negative predictive value of deep learning for detecting plaque, estimating luminal stenosis rate, or predicting plaque stability were calculated by the area under the receiver operating characteristic (ROC) curve (AUROC) to evaluate. Statistical analysis was performed using SPSS 22.0 software.
Quality Control:
Develop standardized and standard carotid ultrasound examination methods and operating procedures, and develop unified image acquisition and storage standards. All operators are rigorously trained in carotid ultrasonography. Two operators with more than 5 years of experience in ultrasound operation were hired as quality control personnel to review all images and exclude unqualified images.
Ultrasound is safe and radiation-free. During the examination, the doctor and the patient were always in a state of communication, and the patient felt less nervous and fearful, with good tolerance and high compliance.
Ethics of the study:
This research will follow the ethical guidelines of the Declaration of Helsinki of the World Medical Congress and the relevant norms and regulations of clinical research. The study will begin after the approval of the ethics committee. Before the start of the study, the investigator should inform the subjects of all relevant contents of the clinical study in easy-to-understand language, and inform the patients that they have the right to withdraw from the study at any time. The study was started only after the patients signed the informed consent voluntarily.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Deep learning training cohort | – | Diagnostic Test: Deep learning training cohort2/3 of the enrolled patients and their corresponding carotid artery dynamic scan images and expert diagnosis results were randomly selected as the training cohort for deep learning. |
| Deep learning validation cohort | – | Diagnostic Test: Deep learning validation cohortThe carotid artery dynamic scan images and expert diagnosis results of the remaining 1/3 patients were used as a validation cohort to evaluate the overall diagnostic accuracy of the deep learning model. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Age≥18 years old, gender is not limited, with varying degrees of carotid atherosclerosis.
Key inclusion criteria (1 listed)
- (1) Age≥18 years old, gender is not limited. (2) Patients who voluntarily participated in this study signed the informed consent.
Key exclusion criteria (1 listed)
- (1) Severe cerebrovascular disease, uncooperative patients, and those who cannot tolerate examination. (2) Wound dressings after neck surgery affects carotid artery ultrasonography. (3) The neck is short and thick, and the probe cannot be put down vertically.
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 training cohorttrain the deep learning model | Diagnostic test | Deep learning training cohort |
| Deep learning validation cohortevaluate the model | Diagnostic test | Deep learning validation cohort |
Primary outcome
AI assists junior radiologists to read images, and primary physicians read images independently
Time frame: through study completion, an average of 2 years
Taking the reading results of senior sonographers as the gold standard, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of AI-assisted reading and independent reading by junior physicians for carotid plaque-assisted diagnosis were tested. AUC is evaluated.
Assessing the performance of AI model
Time frame: through study completion, an average of 2 years
Taking the reading results of senior sonographers as the gold standard, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of AI independent reading. It was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC).
AI estimates the lumen stenosis rate
Time frame: through study completion, an average of 2 years
Taking the reading results of senior sonographers as the gold standard, AI can estimate the sensitivity, specificity, accuracy, positive predictive value and negative predictive value of lumen stenosis rate. It was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC).
AI predicts plaque stability.
Time frame: through study completion, an average of 2 years
Taking the reading results of senior sonographers as the gold standard, AI predicts the sensitivity, specificity, accuracy, positive predictive value and negative predictive value of plaque stability. It was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC).
Plaque detection by AI model on videos acquired by different types of equipment.
Time frame: through study completion, an average of 2 years
Taking the reading results of senior sonographers as the gold standard, AI detects plaque sensitivity, specificity, accuracy, positive predictive value, and negative predictive value on different ultrasound equipment. Assessed by the area under the receiver operating characteristic (ROC) curve (AUC).
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 14 Sep 2023.
- Background: PubMed 26451020Abbott AL, Paraskevas KI, Kakkos SK, Golledge J, Eckstein HH, Diaz-Sandoval LJ, Cao L, Fu Q, Wijeratne T, Leung TW, Montero-Baker M, Lee BC, Pircher S, Bosch M, Dennekamp M, Ringleb P. Systematic Review of Guidelines for the Management of Asymptomatic and Symptomatic Carotid Stenosis. Stroke. 2015 Nov;46(11):3288-301. doi: 10.1161/STROKEAHA.115.003390. Epub 2015 Oct 8.
- Background: PubMed 16282537Nighoghossian N, Derex L, Douek P. The vulnerable carotid artery plaque: current imaging methods and new perspectives. Stroke. 2005 Dec;36(12):2764-72. doi: 10.1161/01.STR.0000190895.51934.43. Epub 2005 Nov 10.
- Background: PubMed 30954372Saba L, Saam T, Jager HR, Yuan C, Hatsukami TS, Saloner D, Wasserman BA, Bonati LH, Wintermark M. Imaging biomarkers of vulnerable carotid plaques for stroke risk prediction and their potential clinical implications. Lancet Neurol. 2019 Jun;18(6):559-572. doi: 10.1016/S1474-4422(19)30035-3. Epub 2019 Apr 4.
- Background: PubMed 28592999Rafailidis V, Charitanti A, Tegos T, Destanis E, Chryssogonidis I. Contrast-enhanced ultrasound of the carotid system: a review of the current literature. J Ultrasound. 2017 Feb 9;20(2):97-109. doi: 10.1007/s40477-017-0239-4. eCollection 2017 Jun.
- Background: PubMed 23519900Deyama J, Nakamura T, Takishima I, Fujioka D, Kawabata K, Obata JE, Watanabe K, Watanabe Y, Saito Y, Mishina H, Kugiyama K. Contrast-enhanced ultrasound imaging of carotid plaque neovascularization is useful for identifying high-risk patients with coronary artery disease. Circ J. 2013;77(6):1499-507. doi: 10.1253/circj.cj-12-1529. Epub 2013 Mar 22.
- Background: PubMed 26180793Varetto G, Gibello L, Castagno C, Quaglino S, Ripepi M, Benintende E, Gattuso A, Garneri P, Zan S, Capaldi G, Bertoldo U, Rispoli P. Use of Contrast-Enhanced Ultrasound in Carotid Atherosclerotic Disease: Limits and Perspectives. Biomed Res Int. 2015;2015:293163. doi: 10.1155/2015/293163. Epub 2015 Jun 21.
- Background: PubMed 19910551Staub D, Patel MB, Tibrewala A, Ludden D, Johnson M, Espinosa P, Coll B, Jaeger KA, Feinstein SB. Vasa vasorum and plaque neovascularization on contrast-enhanced carotid ultrasound imaging correlates with cardiovascular disease and past cardiovascular events. Stroke. 2010 Jan;41(1):41-7. doi: 10.1161/STROKEAHA.109.560342. Epub 2009 Nov 12.
- Individual participant dataSharing planned: No
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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. This study is not recruiting at the moment.
Jia Liu principal investigator
Third Affiliated Hospital, Sun Yat-Sen University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Carotid Atherosclerosis, run by Jia Liu 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: finished, as of the sponsor's last update on 14 Sep 2023. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
AI assists junior radiologists to read images, and primary physicians read images independently (time frame: through study completion, an average of 2 years). The study lists 5 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05230576, which also lists 7 linked publications.
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 14 Sep 2023.