Artificial Intelligence (AI)MAIQUEENCT07133516
A Multi-center Study on Artificial Intelligence-Based Quantitative Evaluation of Echocardiography
An observational study of Artificial Intelligence (AI) and Artificial Intelligence (AI) in Diagnosis, run by First Hospital of China Medical University at 37 sites in China.
Trial summary
This project aims to collaborate with multiple medical institutions to verify the accuracy, stability, and clinical application value of AI algorithms in echocardiographic quantitative measurement through multi-center clinical research. Specific objectives include: 1. Read more about this study
- Who is it for?
- Ages 18 to 80 yearsHealthy volunteers can take part
- Conditions the study is for
- Artificial Intelligence (AI), Artificial Intelligence (AI) in Diagnosis, Cardiovascular Diseases (CVD)
- How many people
- 1,600 participants
- Where
- 37 sitesChina
- Study dates
- 22 Jul 2025to 31 Jul 2026
- 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 80 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Patients who require cardiac ultrasound examination for various reasons in clinical practice. Ultrasound and clinical diagnosis of patients with normal cardiac structure and function, as well as those with coronary heart disease, valve disease, hypertensive heart disease, atrial fibrillation, heart failure, dilated cardiomyopathy, and hypertrophic cardiomyopathy. A total of 1600 adults were enrolled in multiple centers;
Who may be able to join
- Age ≥18 - 80 years;
- Types of diseases (8 in total, 200 cases each):
- Normal heart
- Coronary heart disease (with segmental thinning and abnormal movement)
- Valve disease (valve stenosis or reflux)
- Hypertensive heart disease
Show all 10Show fewer
- Atrial fibrillation
- Heart failure
- Dilated cardiomyopathy
- Hypertrophic cardiomyopathy
Who cannot join
- Patients with congenital heart disease
- Patients with poor image quality
Trial locations and contacts
37 sites in 1 country. Contact the study team or a site near you to ask about taking part.
37 sites in 1 country
China37 sites, 37 recruiting
- Recruiting
Ansteel Group General Hospital
Anshan, Liaoning
Ying Liu
- Recruiting
Affiliated Hospital of Youjiang Medical University for Nationalities
Baise City, Guangxi
Chengcai Chen
- Recruiting
Baotou No.4 Hospital
Baotou, Inner Mongolia
Yan Jing
- Recruiting
Chaoyang Central Hospital
Chaoyang, Liaoning
Hong Zhou
- Recruiting
Bishan Hospital Affiliated to Chongqing Medical University
Chongqing
Min Lu
- Recruiting
Bishan Hospital Affiliated to Chongqing University of Chinese Medicine (Chongqing Bishan District Hospital of Traditional Chinese Medicine)
Chongqing
Xiaoli Zhang
- Recruiting
Chongqing Hospital of Traditional Chinese Medicine
Chongqing
Yang Yang
- Recruiting
Chongqing People's Hospital
Chongqing
Shigen Zhong
- Recruiting
Fuling Hospital Affiliated to Chongqing University
Chongqing
Zhirong He
- Recruiting
The First Affiliated Hospital of Army Medical University
Chongqing
Yanli Guo
- Recruiting
The Second Affiliated Hospital of Chongqing Medical University
Chongqing
Haitao Ran
- Recruiting
Fushun Central Hospital
Fushun, Liaoning
Wei Wang
- Recruiting
Union Hospital Affiliated to Fujian Medical University
Fuzhou, Fujian
Jinguo Li
- Recruiting
Guilin Hospital of Traditional Chinese Medicine
Guilin, Guangxi
Mingyue Wu
- Recruiting
Haikou People's Hospital
Haikou, Hainan
Zelai Mo
- Recruiting
Huainan First People's Hospital
Huainan, Anhui
Binghui Duan
- Recruiting
Jiangyou 903 Hospital
Jiangyou, Sichuan
Juanjuan Wu
- Recruiting
Linfen People's Hospital
Linfen, Shanxi
Liyan Guo
- Recruiting
The Fourth Affiliated Hospital of Guangxi Medical University / Liuzhou Workers' Hospital
Liuzhou, Guangxi
Bo Hu
- Recruiting
Loudi First People's Hospital
Loudi, Hunan
Weiqun Yin
- Recruiting
Meizhou People's Hospital
Meizhou, Guangdong
Youhong Zhang
- Recruiting
Shanghai Changzheng Hospital
Shanghai
Chun Liang
- Recruiting
Shangrao Central Hospital
Shangrao, Jiangxi
Dongdong Huang
- Recruiting
Shangrao People's Hospital
Shangrao, Jiangxi
Shunping Qiu
- Recruiting
Shantou Central Hospital
Shantou, Guangdong
Liangyu Wang
- Recruiting
Shengjing Hospital of China Medical University
Shenyang, Liaoning
Weidong Ren
- Recruiting
The First Hospital of China Medical University
Shenyang, Liaoning
Chunyan Ma
- Recruiting
Shenzhen Hospital of Fuwai Hospital
Shenzhen, Guangdong
Yong Jiang
- Recruiting
Shenzhen People's Hospital
Shenzhen, Guangdong
Yingying Liu
- Recruiting
The Second Affiliated Hospital of Hebei Medical University
Shijiazhuang, Hebei
Hongning Yin
- Recruiting
The Central Hospital Affiliated to Shandong First Medical University
Tai’an, Shandong
Lihua Jin
- Recruiting
Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Wuhan, Hubei
Mingxing Xie
- Recruiting
The Second Affiliated Hospital of Xi'an Jiaotong University
Xi’an, Shanxi
Qi Zhou
- Recruiting
Qinghai Provincial Hospital of Cardiovascular and Cerebrovascular Diseases
Xining, Qinghai
Renbo Jin
- Recruiting
Yulin First People's Hospital
Yulin, Guangxi
Hongxia Yin
- Recruiting
Hunan Province Directly Affiliated TCM Hospital
Zhuzhou, Hunan
Yibo Zhang
- Recruiting
The Second Affiliated Hospital of Zunyi Medical University
Zunyi, Guizhou
Wei Zeng
About this study
This project aims to collaborate with multiple medical institutions to verify the accuracy, stability, and clinical application value of AI algorithms in echocardiographic quantitative measurement through multi-center clinical research. Specific objectives include:
- Compare the automatic measurement results of AI with the manual measurement data from physicians of different levels, and analyze the measurement deviation and consistency of AI in key parameters such as intracardiac diameter, volume, and function.
- Investigate whether AI-assisted measurement can significantly reduce echocardiogram analysis time and optimize clinical workflows. Through multi-center data validation, establish a standardized reference system for AI ultrasound measurement, promote the promotion and application of AI technology in medical institutions at all levels, and reduce diagnostic differences between different hospitals and physicians.
- Exploring the application of AI in special cases: Assessing the measurement stability of AI algorithms in complex cases (such as cardiomyopathy, valvular disease, coronary heart disease, etc.), and optimizing AI models to meet broader clinical needs.
Read the detailed description
Cardiovascular disease is a major threat to the health of Chinese residents, and echocardiography, as its core diagnostic tool, directly affects clinical decision-making in terms of measurement accuracy and efficiency. However, traditional ultrasound evaluation heavily relies on physician experience, with pain points such as strong subjectivity, time-consuming measurements, and uneven levels of primary diagnosis. There is an urgent need for technological innovation to improve diagnostic standardization. In recent years, artificial intelligence (AI) technology has shown great potential in the field of medical image analysis, which can achieve automated quantitative measurement of cardiac chamber structure and function. However, existing AI models generally have problems such as insufficient multi center validation and limited adaptability to complex cases, which restrict their clinical translation and application.
To overcome these bottlenecks, this project collaborates with multiple medical institutions to conduct clinical research, systematically evaluating the measurement differences between AI algorithms and physicians of different levels, and assessing the accuracy and stability of AI algorithms. The research will focus on verifying the value of AI technology in improving diagnostic consistency, optimizing workflows, and exploring its potential applications in complex cardiovascular diseases. By establishing a standardized evaluation system, this project aims to promote the standardized application of AI ultrasound technology, ultimately achieving the goal of improving diagnosis and treatment efficiency, promoting the sinking of high-quality medical resources, and helping to improve the overall level of cardiovascular disease prevention and treatment.
- Conditions
- Artificial Intelligence (AI), Artificial Intelligence (AI) in Diagnosis, Cardiovascular Diseases (CVD), Echocardiography
- Keywords
- Artificial Intelligence (AI), Artificial Intelligence (AI) in Diagnosis, Cardiovascular Diseases (CVD), Echocardiography
- Medical subject headings
- Cardiovascular Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| AI group | The cardiac chamber size and functional parameters of all 1600 subjects were measured by AI | |
| Senior physician group | The cardiac chamber size and functional parameters of all 1600 subjects were measured by senior physicians at the PI unit | |
| Intermediate physician group | The cardiac chamber size and functional parameters of all 1600 subjects were measured by intermediate physicians at each sub center |
What is being measured
The primary outcome is the main question the study is designed to answer.
The indicators of left ventricular size and function are measured by AI, senior physicians at the PI unit, and intermediate physicians at each sub center respectively (all parameters are measured by Mindray ultrasonic machines on the machine)
Left ventricular end diastolic volume (EDV, ml), end systolic volume (ESV, ml), and ejection fraction (EF, %) (calculated by (EDV-ESV)/EDV*100% ) in the four chamber view and two chamber view, as well as the Simpson biplane left ventricular volume (ml) and ejection fraction (%) results.
Measured: Artificial intelligence and intermediate doctors measurement results of each sub center will be completed within one day after data collection. Senior physicians measurement results of PI unit will be completed within one month after data collection.
The indicator of right ventricular function is measured by AI, senior physicians at the PI unit, and intermediate physicians at each sub center respectively (all parameters are measured by Mindray ultrasonic machines on the machine)
Right ventricular area change rate calculated by (EDA-ESA)/EDA*100%.
Measured: Artificial intelligence and intermediate doctors measurement results of each sub center will be completed within one day after data collection. Senior physicians measurement results of PI unit will be completed within one month after data collection.
The indicators of right ventricular size are measured by AI, senior physicians at the PI unit, and intermediate physicians at each sub center respectively (all parameters are measured by Mindray ultrasonic machines on the machine)
Right ventricular end diastolic area (EDA) and end systolic area (ESA).
Measured: Artificial intelligence and intermediate doctors measurement results of each sub center will be completed within one day after data collection. Senior physicians measurement results of PI unit will be completed within one month after data collection.
Secondary outcomes (2)
Doppler ultrasound measurement indicators by are measured by AI, senior physicians at the PI unit, and intermediate physicians at each sub center respectively (all parameters are measured by Mindray ultrasonic machines on the machine)
Forward blood flow velocity of mitral and tricuspid valve (m/s).
Measured: Artificial intelligence and intermediate doctors measurement results of each sub center will be completed within one day after data collection. Senior physicians measurement results of PI unit will be completed within one month after data collection.
Mitral valve annulus and tricuspid valve annulus displacement are measured by AI, senior physicians at the PI unit, and intermediate physicians at each sub center respectively (all parameters are measured by Mindray ultrasonic machines on the machine)
Mitral valve annulus and tricuspid valve annulus displacement (mm)
Measured: Artificial intelligence and intermediate doctors measurement results of each sub center will be completed within one day after data collection. Senior physicians measurement results of PI unit will be completed within one month after data collection.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Aug 2025
- Study start22 Jul 2025
- Primary completion30 Jun 2026 (estimated)
- Study completion31 Jul 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Artificial Intelligence (AI)Studies 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-control
- Time perspective
- Prospective
- Enrolment
- 1,600 participants (estimated)
Sponsor and investigators
- Lead sponsor
- First Hospital of China Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Chunyan Ma, First Hospital of China Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07133516
- Sponsor's protocol ID
- MAIQUEE STUDY
- Acronym
- MAIQUEE
- Status last verified
- Aug 2025
- First submitted
- 30 Jul 2025
- Last updated
- 21 Aug 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 21 Aug 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Multi-center Study on Artificial Intelligence-Based Quantitative Evaluation of Echocardiography
- ClinicalTrials.gov ID
- NCT07133516
- Acronym / sponsor ID
- MAIQUEE / MAIQUEE STUDY
- Conditions
- Artificial Intelligence (AI), Artificial Intelligence (AI) in Diagnosis, Cardiovascular Diseases (CVD), Echocardiography
- Keywords
- Artificial Intelligence (AI), Artificial Intelligence (AI) in Diagnosis, Cardiovascular Diseases (CVD), Echocardiography
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 1,600 participants (estimated)
- Lead sponsor
- First Hospital of China Medical UniversityUniversity, hospital or other
- Study start
- 22 Jul 2025 (actual)
- Primary completion
- 30 Jun 2026 (estimated)
- Study completion
- 31 Jul 2026 (estimated)
- First posted
- 21 Aug 2025 (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 project aims to collaborate with multiple medical institutions to verify the accuracy, stability, and clinical application value of AI algorithms in echocardiographic quantitative measurement through multi-center clinical research. Specific objectives include:
- Compare the automatic measurement results of AI with the manual measurement data from physicians of different levels, and analyze the measurement deviation and consistency of AI in key parameters such as intracardiac diameter, volume, and function.
- Investigate whether AI-assisted measurement can significantly reduce echocardiogram analysis time and optimize clinical workflows. Through multi-center data validation, establish a standardized reference system for AI ultrasound measurement, promote the promotion and application of AI technology in medical institutions at all levels, and reduce diagnostic differences between different hospitals and physicians.
- Exploring the application of AI in special cases: Assessing the measurement stability of AI algorithms in complex cases (such as cardiomyopathy, valvular disease, coronary heart disease, etc.), and optimizing AI models to meet broader clinical needs.
Detailed description
Cardiovascular disease is a major threat to the health of Chinese residents, and echocardiography, as its core diagnostic tool, directly affects clinical decision-making in terms of measurement accuracy and efficiency. However, traditional ultrasound evaluation heavily relies on physician experience, with pain points such as strong subjectivity, time-consuming measurements, and uneven levels of primary diagnosis. There is an urgent need for technological innovation to improve diagnostic standardization. In recent years, artificial intelligence (AI) technology has shown great potential in the field of medical image analysis, which can achieve automated quantitative measurement of cardiac chamber structure and function. However, existing AI models generally have problems such as insufficient multi center validation and limited adaptability to complex cases, which restrict their clinical translation and application.
To overcome these bottlenecks, this project collaborates with multiple medical institutions to conduct clinical research, systematically evaluating the measurement differences between AI algorithms and physicians of different levels, and assessing the accuracy and stability of AI algorithms. The research will focus on verifying the value of AI technology in improving diagnostic consistency, optimizing workflows, and exploring its potential applications in complex cardiovascular diseases. By establishing a standardized evaluation system, this project aims to promote the standardized application of AI ultrasound technology, ultimately achieving the goal of improving diagnosis and treatment efficiency, promoting the sinking of high-quality medical resources, and helping to improve the overall level of cardiovascular disease prevention and treatment.
Study design
- Observational model
- Case-control
- Time perspective
- Prospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| AI group | – | –The cardiac chamber size and functional parameters of all 1600 subjects were measured by AI |
| Senior physician group | – | –The cardiac chamber size and functional parameters of all 1600 subjects were measured by senior physicians at the PI unit |
| Intermediate physician group | – | –The cardiac chamber size and functional parameters of all 1600 subjects were measured by intermediate physicians at each sub center |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Patients who require cardiac ultrasound examination for various reasons in clinical practice. Ultrasound and clinical diagnosis of patients with normal cardiac structure and function, as well as those with coronary heart disease, valve disease, hypertensive heart disease, atrial fibrillation, heart failure, dilated cardiomyopathy, and hypertrophic cardiomyopathy. A total of 1600 adults were enrolled in multiple centers;
Key inclusion criteria (10 listed)
- Age ≥18 - 80 years;
- Types of diseases (8 in total, 200 cases each):
Key exclusion criteria (2 listed)
- Patients with congenital heart disease
- Patients with poor image quality
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
The indicators of left ventricular size and function are measured by AI, senior physicians at the PI unit, and intermediate physicians at each sub center respectively (all parameters are measured by Mindray ultrasonic machines on the machine)
Time frame: Artificial intelligence and intermediate doctors measurement results of each sub center will be completed within one day after data collection. Senior physicians measurement results of PI unit will be completed within one month after data collection.
Left ventricular end diastolic volume (EDV, ml), end systolic volume (ESV, ml), and ejection fraction (EF, %) (calculated by (EDV-ESV)/EDV*100% ) in the four chamber view and two chamber view, as well as the Simpson biplane left ventricular volume (ml) and ejection fraction (%) results.
The indicator of right ventricular function is measured by AI, senior physicians at the PI unit, and intermediate physicians at each sub center respectively (all parameters are measured by Mindray ultrasonic machines on the machine)
Time frame: Artificial intelligence and intermediate doctors measurement results of each sub center will be completed within one day after data collection. Senior physicians measurement results of PI unit will be completed within one month after data collection.
Right ventricular area change rate calculated by (EDA-ESA)/EDA*100%.
The indicators of right ventricular size are measured by AI, senior physicians at the PI unit, and intermediate physicians at each sub center respectively (all parameters are measured by Mindray ultrasonic machines on the machine)
Time frame: Artificial intelligence and intermediate doctors measurement results of each sub center will be completed within one day after data collection. Senior physicians measurement results of PI unit will be completed within one month after data collection.
Right ventricular end diastolic area (EDA) and end systolic area (ESA).
2 secondary outcomes
Doppler ultrasound measurement indicators by are measured by AI, senior physicians at the PI unit, and intermediate physicians at each sub center respectively (all parameters are measured by Mindray ultrasonic machines on the machine)
Time frame: Artificial intelligence and intermediate doctors measurement results of each sub center will be completed within one day after data collection. Senior physicians measurement results of PI unit will be completed within one month after data collection.
Mitral valve annulus and tricuspid valve annulus displacement are measured by AI, senior physicians at the PI unit, and intermediate physicians at each sub center respectively (all parameters are measured by Mindray ultrasonic machines on the machine)
Time frame: Artificial intelligence and intermediate doctors measurement results of each sub center will be completed within one day after data collection. Senior physicians measurement results of PI unit will be completed within one month after data collection.
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 21 Aug 2025.
- Individual participant dataSharing planned: Undecided
Related trials for Artificial Intelligence (AI)
Recruiting
Longitudinal AI-Assisted Management of Indeterminate Pulmonary Nodules
Pulmonary Nodules, Lung Neoplasms
Recruiting
A Multicenter Randomized Trial of CT-Based AI Decision Support for Early Pneumonia Care
Pneumonia; Pulmonary Infection
Recruiting
Feasibility and Acceptability of an LLM-based Chatbot for Family Caregivers: Evaluation Study
Caregivers
Enrolling by invitationObservational study
Comparing an AI-driven Hearing Test With the Standard Hearing Test in Adults With Hearing Loss and Tinnitus
Sensorineural Hearing Loss, Tinnitus
Not yet recruiting
AI-Powered Sleep Hygiene Mentor for Adults Living With HIV
Sleep Wake Disorders, Sleep Initiation and Maintenance Disorders
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.
37 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Artificial Intelligence (AI) and Artificial Intelligence (AI) in Diagnosis, run by First Hospital of China Medical University at 37 sites 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 10 inclusion and 2 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: looking for participants now, as of the sponsor's last update on 21 Aug 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 37 sites 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?
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 Chunyan Ma the First Hospital of China Medical Univeristy, Ph.D (13998816448, cmu1h_mcy@126.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The indicators of left ventricular size and function are measured by AI, senior physicians at the PI unit, and intermediate physicians at each sub center respectively (all parameters are measured by Mindray ultrasonic machines on the machine) (time frame: Artificial intelligence and intermediate doctors measurement results of each sub center will be completed within one day after data collection. Senior physicians measurement results of PI unit will be completed within one month after data collection.). 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 21 Aug 2025.