Heart Valve DiseasesARTEMISNCT07449130
AI Assisted Screening for VHD Using Routine Chest CT Scans
An observational study of Heart Valve Diseases, run by Second Affiliated Hospital, Zhejiang University, School of Medicine at 3 sites in China.
Trial summary
This is a prospective, multicenter study designed to validate a deep learning model for screening valvular heart diseases using routine, non-contrast chest computed tomography (CT) scans. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Heart Valve Diseases
- How many people
- 3,000 participants
- Where
- 3 sitesChina
- Study dates
- 2 Mar 2026to 1 Nov 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 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: People aged 18 and above in any medical context of hospitals.
Who may be able to join
- Age ≥ 18 years.
- Complete electronic health record.
- Non-contrast chest CT performed between Nov 1, 2025 - Nov 1, 2026 in any medical context (including physical exam, outpatient, inpatient, or emergency).
- AI-predicted moderate or severe valvular heart disease, or deemed to require clinical intervention, or selected negative cases from sampling verification.
Who cannot join
- Poor-quality non-contrast chest CT images.
- Incomplete clinical records, involving severe deficiencies in critical diagnostic results, treatment records, imaging data, surgical records, medical history summaries, laboratory test results, or other essential medical information.
- Presence of prosthetic valve implants, including aortic valves (mechanical valves, bioprosthetic valves), mitral valves (transcatheter edge-to-edge repair, bioprosthetic valves, mechanical valves, annuloplasty rings), tricuspid valves (TEER clipping, bioprosthetic valves, mechanical valves, annuloplasty rings), pulmonary valves (bioprosthetic valves), etc.
- Abnormalities or conditions deemed by the investigator to warrant exclusion from the study enrollment.
Trial locations and contacts
3 sites in 1 country. Contact the study team or a site near you to ask about taking part.
China3 sites, 3 recruiting
- Recruiting
The Second Affiliated Hospital of Zhejiang University School of Medicine
Hangzhou, Zhejiang
Xianbao Liu, MD, +86-13857173887, liuxb@zju.edu.cn
- Recruiting
Xinjiang Uygur Autonomous Region People's Hospital
Ürümqi, Xinjiang
Yining Yang, MD, +86-13199860611, yangyn5126@163.com
- Recruiting
Renmin Hospital of Wuhan University
Wuhan, Hubei
Jing Chen, MD, +86-13659840327, 364580342@qq.com
About this study
Official title: Artificial-Intelligence Assisted Opportunistic Screening for Valvular Heart Disease Using Non-contrast Chest CT Scans: A Prospective, Multicenter Study
This is a prospective, multicenter study designed to validate a deep learning model for screening valvular heart diseases using routine, non-contrast chest computed tomography (CT) scans.
The primary objective is to evaluate the model's diagnostic performance, with the sensitivity serving as the primary efficacy endpoint. Secondary endpoints will include other performance metrics such as area under the receiver operating characteristic curve (AUC), specificity, and accuracy, etc.
Read the detailed description
This is a prospective, multicenter study designed to validate a deep learning model for screening valvular heart diseases using routine, non-contrast chest computed tomography (CT) scans from individuals in any medical context within a hospital alliance.
The primary objective is to evaluate the model's diagnostic performance, with the sensitivity serving as the primary efficacy endpoint. Secondary endpoints will include other performance metrics such as area under the receiver operating characteristic curve (AUC), specificity, and accuracy, etc.
Participants from the target populations will undergo a routine non-contrast chest CT scan. The deep learning model will analyze these images in real-time. For those identified by the model as having moderate-to-severe heart valve disease, a confirmatory echocardiogram will be performed immediately. The echocardiogram results will serve as the reference standard for diagnosis. Statistical analyses will be performed to assess the model's performance against this reference, including calculating the 95% confidence interval for the AUC.
As this study only involves standard, low-radiation diagnostic imaging procedures (non-contrast CT and echocardiography) that are part of routine clinical care, it is considered to pose no additional relevant safety risks to participants. The total study duration is estimated to be 12 months.
- Conditions
- Heart Valve Diseases
- Medical subject headings
- Heart Valve Diseases
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity
Sensitivity: Measures the proportion of patients with moderate-to-severe disease that the model correctly identifies.
Measured: 1 year
Secondary outcomes (3)
Area Under the Receiver Operating Characteristic Curve (AUC)
AUC for a model distinguishing valvular disease severity. The AUC value for this model would indicate how well it can correctly identify patients with moderate-to-severe disease from those with normal-to-mild disease.
Measured: 1 year
Accuracy
Accuracy: Measures the overall proportion of all patients that the model correctly classifies into either group.
Measured: 1 year
Specificity
Specificity: Measures the proportion of patients with normal-to-mild disease that the model correctly identifies.
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Mar 2026
- Study start2 Mar 2026
- Primary completion1 Nov 2026 (estimated)
- Study completion1 Nov 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Heart Valve DiseasesStudies of the same condition, open and under way.
- More trials run by Second Affiliated Hospital, Zhejiang University, School of MedicineThe sponsor's other registered studies.
- 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
- 3,000 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07449130
- Sponsor's protocol ID
- 2025-1250
- Acronym
- ARTEMIS
- Status last verified
- Mar 2026
- First submitted
- 27 Feb 2026
- Last updated
- 21 Aug 2026
- 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 21 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial-Intelligence Assisted Opportunistic Screening for Valvular Heart Disease Using Non-contrast Chest CT Scans: A Prospective, Multicenter Study
- ClinicalTrials.gov ID
- NCT07449130
- Acronym / sponsor ID
- ARTEMIS / 2025-1250
- Conditions
- Heart Valve Diseases
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 3,000 participants (estimated)
- Lead sponsor
- Second Affiliated Hospital, Zhejiang University, School of MedicineUniversity, hospital or other
- Study start
- 2 Mar 2026 (actual)
- Primary completion
- 1 Nov 2026 (estimated)
- Study completion
- 1 Nov 2026 (estimated)
- First posted
- 4 Mar 2026 (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 is a prospective, multicenter study designed to validate a deep learning model for screening valvular heart diseases using routine, non-contrast chest computed tomography (CT) scans.
The primary objective is to evaluate the model's diagnostic performance, with the sensitivity serving as the primary efficacy endpoint. Secondary endpoints will include other performance metrics such as area under the receiver operating characteristic curve (AUC), specificity, and accuracy, etc.
Detailed description
This is a prospective, multicenter study designed to validate a deep learning model for screening valvular heart diseases using routine, non-contrast chest computed tomography (CT) scans from individuals in any medical context within a hospital alliance.
The primary objective is to evaluate the model's diagnostic performance, with the sensitivity serving as the primary efficacy endpoint. Secondary endpoints will include other performance metrics such as area under the receiver operating characteristic curve (AUC), specificity, and accuracy, etc.
Participants from the target populations will undergo a routine non-contrast chest CT scan. The deep learning model will analyze these images in real-time. For those identified by the model as having moderate-to-severe heart valve disease, a confirmatory echocardiogram will be performed immediately. The echocardiogram results will serve as the reference standard for diagnosis. Statistical analyses will be performed to assess the model's performance against this reference, including calculating the 95% confidence interval for the AUC.
As this study only involves standard, low-radiation diagnostic imaging procedures (non-contrast CT and echocardiography) that are part of routine clinical care, it is considered to pose no additional relevant safety risks to participants. The total study duration is estimated to be 12 months.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- People aged 18 and above in any medical context of hospitals.
Key inclusion criteria (4 listed)
- Age ≥ 18 years.
- Complete electronic health record.
- Non-contrast chest CT performed between Nov 1, 2025 - Nov 1, 2026 in any medical context (including physical exam, outpatient, inpatient, or emergency).
- AI-predicted moderate or severe valvular heart disease, or deemed to require clinical intervention, or selected negative cases from sampling verification.
Key exclusion criteria (4 listed)
- Poor-quality non-contrast chest CT images.
- Incomplete clinical records, involving severe deficiencies in critical diagnostic results, treatment records, imaging data, surgical records, medical history summaries, laboratory test results, or other essential medical information.
- Presence of prosthetic valve implants, including aortic valves (mechanical valves, bioprosthetic valves), mitral valves (transcatheter edge-to-edge repair, bioprosthetic valves, mechanical valves, annuloplasty rings), tricuspid valves (TEER clipping, bioprosthetic valves, mechanical valves, annuloplasty rings), pulmonary valves (bioprosthetic valves), etc.
- Abnormalities or conditions deemed by the investigator to warrant exclusion from the study enrollment.
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
Sensitivity
Time frame: 1 year
Sensitivity: Measures the proportion of patients with moderate-to-severe disease that the model correctly identifies.
3 secondary outcomes
Area Under the Receiver Operating Characteristic Curve (AUC)
Time frame: 1 year
Accuracy
Time frame: 1 year
Specificity
Time frame: 1 year
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 2026.
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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.
3 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Heart Valve Diseases, run by Second Affiliated Hospital, Zhejiang University, School of Medicine at 3 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 4 inclusion and 4 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 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 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 Jian'an Wang, MD (+86-571-8778-4808, wangjianan111@zju.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity (time frame: 1 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 21 Aug 2026.