Coronary Artery DiseaseAIGatekeeperNCT06178900
Evaluating AI-Gatekeeper Software in Coronary Artery Stenosis Screening: A Multicenter RCT
A study testing Assisted by the AI-Gatekeeper software group in people with Coronary Artery Disease and Diagnosis, run by INFINITT Healthcare at 5 sites in South Korea.
Trial summary
The purpose of this study is to determine the efficacy, safety, and cost-effectiveness of AI-Gatekeeper software to assist clinicians in the diagnosis of coronary artery disease by predicting coronary artery stenosis (≥50%) from a multimodal AI technology that integrates clinical risk factors and baseline blood tests… Read more about this study
- Who is it for?
- Ages 19 to 80 years
- Conditions the study is for
- Coronary Artery Disease, Diagnosis
- What is being tested?
- Assisted by the AI-Gatekeeper software group
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 450 participants
- Where
- 5 sitesSouth Korea
- Study dates
- 1 Mar 2024to 28 Feb 2025
- 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 19 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- A patient with symptoms such as chest pain suggestive of coronary artery disease, who underwent routine evaluations including blood tests, electrocardiogram, chest X-ray, and echocardiography
- Low to Intermediate risk of pretest probabilities of obstructive CAD
- Voluntarily agreed to participate in this clinical trial and signed the written consent form
Who cannot join
- Acute chest pain (in patients who have not been ruled out for ACS)
- Previously diagnosed and treated coronary artery disease (myocardial infarction, PCI, CABG)
- Patients with a life expectancy of less than 2 years due to conditions other than heart disease
- Those who have not consented to the protocol
- Participated in a drug or medical device clinical trial within the last 3 months
- Pregnant or lactating women
Show all 14Show fewer
- Allergic to iodine preparations
- Serum creatine level greater than 1.5 mg/dL or eGFR less than 30 mL/min
- Baseline irregular and uncontrolled heart rhythm
- Heart rate greater than 100 beats/minute
- Systolic blood pressure of 90 mm Hg or less
- Contraindications to beta blockers or nitroglycerin
- Patients with complex congenital heart disease
- Body mass index greater than or equal to 35
Trial locations and contacts
5 sites in 1 country.
5 sites in 1 country
South Korea5 sites
Soonchunhyang University Bucheon Hospital
Bucheon-si, Gyeonggi-do, 16995
Catholic Kwandong University International St. Mary's Hospital
Incheon
Seoul National University Bundang Hospital
Seongnam-si, Gyeonggi-do, 16995
Hanyang University Seoul Hospital
Seoul
Yongin Severance Hospitall, Yonsei University College of Medicine
Yongin, Gyeonggi-do, 16995
About this study
Official title: A Prospective, Multicenter, Open-label, Randomized, Comparative Clinical Trial to Verify the Effectiveness, Safety, and Cost-effectiveness of AI-Gatekeeper, a Multimodal AI Software, in Assisting the Screening of Coronary Artery Stenosis
The purpose of this study is to determine the efficacy, safety, and cost-effectiveness of AI-Gatekeeper software to assist clinicians in the diagnosis of coronary artery disease by predicting coronary artery stenosis (≥50%) from a multimodal AI technology that integrates clinical risk factors and baseline blood tests, including chest X-ray, electrocardiogram, and echocardiogram, in patients with suspected coronary artery disease (coronary stenosis).
Read the detailed description
Coronary artery disease (CAD) is a leading cause of global mortality, accounting for over 50% of heart disease-related deaths. Initial evaluations for CAD typically involve chest X-rays, electrocardiograms (ECG), risk factor assessments, and basic blood tests. However, these primary tests can't conclusively diagnose CAD. When CAD is suspected, coronary CTA (CCTA) or invasive coronary angiography (ICA) is performed, determining the need for procedures like stenting or revascularization.
Interestingly, over 50% of patients undergoing CCTA or ICA don't require treatment, as CAD is either absent or not severe enough. This leads to unnecessary procedures and significant healthcare costs. For instance, in the U.S., the cost of unnecessary ICAs reaches billions annually, with similar trends in South Korea.
AI-Gatekeeper software assists clinicians in diagnosing coronary artery disease by predicting coronary artery stenosis (≥50%) using multimodal AI technology. It integrates clinical risk factors and baseline blood tests, including chest X-ray, electrocardiogram, and echocardiogram, in patients with suspected coronary artery disease The purpose of this study is to determine the efficacy, safety, and cost-effectiveness of the AI-Gatekeeper software in a prospective, multicenter, randomized control trial.
- Conditions
- Coronary Artery Disease, Diagnosis
- Being tested
- Assisted by the AI-Gatekeeper software group
- Keywords
- Coronary Artery Disease, Artificial Intelligence, Gatekeeper
- Medical subject headings
- Coronary Artery Disease, Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Assisted by the AI-Gatekeeper software groupDiagnostic test
The group will be received a AI-Gatekeeper software report on the probability of having coronary artery stenosis (≥50%) based on the routine test.
| Study group | Role | What they receive |
|---|---|---|
| Assisted by the AI-Gatekeeper software group | Experimental | After a baseline examination (chest X-ray, electrocardiogram, echocardiogram, clinical risk factors and blood test), the AI-Gatekeeper software will be used to guide clinical care. Receives: Diagnostic Test: Assisted by the AI-Gatekeeper software group |
| Usual care group | No intervention | The usual care group will be managed based on established guidelines. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Unnecessary utilization of advanced cardiac imaging
Confirmation of non-obstructive coronary artery disease (diameter stenosis ≤50%) by advanced cardiac imaging (CCTA or ICA)
Measured: 24 weeks
Secondary outcomes (5)
MACE (major adverse cardiovascular events)
All-cause death, non-fatal MI, stroke, admission due to acute coronary artery syndrome
Measured: 24 weeks
Proportion of subjects classified as positive by the AI-Gatekeeper model analysis who are diagnosed with coronary artery stenosis (≥50%)
This measure assesses the accuracy of the AI-Gatekeeper model in identifying patients with coronary artery stenosis of 50% or greater among those it classifies as positive.
Measured: 24 weeks
Proportion of subjects identified as negative by the AI-Gatekeeper model who are confirmed to have non-significant stenosis (<50%)
This measure assesses the accuracy of the AI-Gatekeeper model in identifying patients with non-significant coronary artery stenosis (<50%) among those it classifies as negative.
Measured: 24 weeks
Comparison of changes in angina symptom score
This comparison evaluates the variation in patients' angina symptoms severity and frequency as measured by the Seattle Angina Questionnaire (SAQ), a standardized tool for assessing chest pain related to heart conditions.
Measured: 24 weeks
Comparison of total healthcare costs
This analysis focuses on comparing the overall financial impact of different healthcare interventions or treatments. It encompasses all associated expenses, from diagnostic procedures to treatment and follow-up care, providing a comprehensive assessment of the economic burden on the healthcare system.
Measured: 24 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Dec 2023
- Study start1 Mar 2024
- Primary completion6 Sep 2024
- Study completion28 Feb 2025
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 Coronary Artery DiseaseStudies 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
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 450 participants (actual)
Sponsor and investigators
- Lead sponsor
- INFINITT Healthcare
- Sponsor type
- Industry (pharma, biotech, devices)
- Collaborators
- Korea Medical Device Development Fund
- Responsible party
- Sponsor
- Investigators
- In Hyun Jung, MD, PhD, Principal investigator, Yongin Severance Hospital, Yonsei University College of Medicine
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06178900
- Sponsor's protocol ID
- AI-Gatekeeper Pro
- Acronym
- AIGatekeeper
- Status last verified
- Aug 2026
- First submitted
- 14 Dec 2023
- Last updated
- 31 Aug 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Writing Committee Members; Gulati M, Levy PD, Mukherjee D, Amsterdam E, Bhatt DL, Birtcher KK, Blankstein R, Boyd J, Bullock-Palmer RP, Conejo T, Diercks DB, Gentile F, Greenwood JP, Hess EP, Hollenberg SM, Jaber WA, Jneid H, Joglar JA, Morrow DA, O'Connor RE, Ross MA, Shaw LJ. 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. J Am Coll Cardiol. 2021 Nov 30;78(22):e187-e285. doi: 10.1016/j.jacc.2021.07.053. Epub 2021 Oct 28.
PubMed 34756653Genders TS, Steyerberg EW, Hunink MG, Nieman K, Galema TW, Mollet NR, de Feyter PJ, Krestin GP, Alkadhi H, Leschka S, Desbiolles L, Meijs MF, Cramer MJ, Knuuti J, Kajander S, Bogaert J, Goetschalckx K, Cademartiri F, Maffei E, Martini C, Seitun S, Aldrovandi A, Wildermuth S, Stinn B, Fornaro J, Feuchtner G, De Zordo T, Auer T, Plank F, Friedrich G, Pugliese F, Petersen SE, Davies LC, Schoepf UJ, Rowe GW, van Mieghem CA, van Driessche L, Sinitsyn V, Gopalan D, Nikolaou K, Bamberg F, Cury RC, Battle J, Maurovich-Horvat P, Bartykowszki A, Merkely B, Becker D, Hadamitzky M, Hausleiter J, Dewey M, Zimmermann E, Laule M. Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohorts. BMJ. 2012 Jun 12;344:e3485. doi: 10.1136/bmj.e3485.
PubMed 22692650Renker M, Schoepf UJ, Wang R, Meinel FG, Rier JD, Bayer RR 2nd, Mollmann H, Hamm CW, Steinberg DH, Baumann S. Comparison of diagnostic value of a novel noninvasive coronary computed tomography angiography method versus standard coronary angiography for assessing fractional flow reserve. Am J Cardiol. 2014 Nov 1;114(9):1303-8. doi: 10.1016/j.amjcard.2014.07.064. Epub 2014 Aug 12.
PubMed 25205628Kamel PI, Yi PH, Sair HI, Lin CT. Prediction of Coronary Artery Calcium and Cardiovascular Risk on Chest Radiographs Using Deep Learning. Radiol Cardiothorac Imaging. 2021 Jun 17;3(3):e200486. doi: 10.1148/ryct.2021200486. eCollection 2021 Jun.
PubMed 34235441Kwon JM, Lee SY, Jeon KH, Lee Y, Kim KH, Park J, Oh BH, Lee MM. Deep Learning-Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography. J Am Heart Assoc. 2020 Apr 7;9(7):e014717. doi: 10.1161/JAHA.119.014717. Epub 2020 Mar 21.
PubMed 32200712Min JK, Dunning A, Lin FY, Achenbach S, Al-Mallah MH, Berman DS, Budoff MJ, Cademartiri F, Callister TQ, Chang HJ, Cheng V, Chinnaiyan KM, Chow B, Delago A, Hadamitzky M, Hausleiter J, Karlsberg RP, Kaufmann P, Maffei E, Nasir K, Pencina MJ, Raff GL, Shaw LJ, Villines TC. Rationale and design of the CONFIRM (COronary CT Angiography EvaluatioN For Clinical Outcomes: An InteRnational Multicenter) Registry. J Cardiovasc Comput Tomogr. 2011 Mar-Apr;5(2):84-92. doi: 10.1016/j.jcct.2011.01.007. Epub 2011 Feb 1.
PubMed 21477786Kim J, Lee SY, Cha BH, Lee W, Ryu J, Chung YH, Kim D, Lim SH, Kang TS, Park BE, Lee MY, Cho S. Machine learning models of clinically relevant biomarkers for the prediction of stable obstructive coronary artery disease. Front Cardiovasc Med. 2022 Jul 19;9:933803. doi: 10.3389/fcvm.2022.933803. eCollection 2022.
PubMed 35928935
Study at a glance
From the registry record, last updated by the sponsor on 31 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Prospective, Multicenter, Open-label, Randomized, Comparative Clinical Trial to Verify the Effectiveness, Safety, and Cost-effectiveness of AI-Gatekeeper, a Multimodal AI Software, in Assisting the Screening of Coronary Artery Stenosis
- ClinicalTrials.gov ID
- NCT06178900
- Acronym / sponsor ID
- AIGatekeeper / AI-Gatekeeper Pro
- Conditions
- Coronary Artery Disease, Diagnosis
- Keywords
- Coronary Artery Disease, Artificial Intelligence, Gatekeeper
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 450 participants (actual)
- Lead sponsor
- INFINITT HealthcareIndustry (pharma, biotech, devices)
- Collaborators
- Korea Medical Device Development Fund
- Study start
- 1 Mar 2024 (actual)
- Primary completion
- 6 Sep 2024 (actual)
- Study completion
- 28 Feb 2025 (actual)
- First posted
- 21 Dec 2023 (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.
The purpose of this study is to determine the efficacy, safety, and cost-effectiveness of AI-Gatekeeper software to assist clinicians in the diagnosis of coronary artery disease by predicting coronary artery stenosis (≥50%) from a multimodal AI technology that integrates clinical risk factors and baseline blood tests, including chest X-ray, electrocardiogram, and echocardiogram, in patients with suspected coronary artery disease (coronary stenosis).
Detailed description
Coronary artery disease (CAD) is a leading cause of global mortality, accounting for over 50% of heart disease-related deaths. Initial evaluations for CAD typically involve chest X-rays, electrocardiograms (ECG), risk factor assessments, and basic blood tests. However, these primary tests can't conclusively diagnose CAD. When CAD is suspected, coronary CTA (CCTA) or invasive coronary angiography (ICA) is performed, determining the need for procedures like stenting or revascularization.
Interestingly, over 50% of patients undergoing CCTA or ICA don't require treatment, as CAD is either absent or not severe enough. This leads to unnecessary procedures and significant healthcare costs. For instance, in the U.S., the cost of unnecessary ICAs reaches billions annually, with similar trends in South Korea.
AI-Gatekeeper software assists clinicians in diagnosing coronary artery disease by predicting coronary artery stenosis (≥50%) using multimodal AI technology. It integrates clinical risk factors and baseline blood tests, including chest X-ray, electrocardiogram, and echocardiogram, in patients with suspected coronary artery disease The purpose of this study is to determine the efficacy, safety, and cost-effectiveness of the AI-Gatekeeper software in a prospective, multicenter, randomized control trial.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Assisted by the AI-Gatekeeper software group | Experimental | Diagnostic Test: Assisted by the AI-Gatekeeper software groupAfter a baseline examination (chest X-ray, electrocardiogram, echocardiogram, clinical risk factors and blood test), the AI-Gatekeeper software will be used to guide clinical care. |
| Usual care group | No intervention | –The usual care group will be managed based on established guidelines. |
Who may qualify
- Ages
- 19 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- A patient with symptoms such as chest pain suggestive of coronary artery disease, who underwent routine evaluations including blood tests, electrocardiogram, chest X-ray, and echocardiography
- Low to Intermediate risk of pretest probabilities of obstructive CAD
- Voluntarily agreed to participate in this clinical trial and signed the written consent form
Key exclusion criteria (14 listed)
- Acute chest pain (in patients who have not been ruled out for ACS)
- Previously diagnosed and treated coronary artery disease (myocardial infarction, PCI, CABG)
- Patients with a life expectancy of less than 2 years due to conditions other than heart disease
- Those who have not consented to the protocol
- Participated in a drug or medical device clinical trial within the last 3 months
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 |
|---|---|---|
| Assisted by the AI-Gatekeeper software groupThe group will be received a AI-Gatekeeper software report on the probability of having coronary artery stenosis (≥50%) based on the routine test. | Diagnostic test | Assisted by the AI-Gatekeeper software group |
Primary outcome
Unnecessary utilization of advanced cardiac imaging
Time frame: 24 weeks
Confirmation of non-obstructive coronary artery disease (diameter stenosis ≤50%) by advanced cardiac imaging (CCTA or ICA)
5 secondary outcomes
MACE (major adverse cardiovascular events)
Time frame: 24 weeks
Proportion of subjects classified as positive by the AI-Gatekeeper model analysis who are diagnosed with coronary artery stenosis (≥50%)
Time frame: 24 weeks
Proportion of subjects identified as negative by the AI-Gatekeeper model who are confirmed to have non-significant stenosis (<50%)
Time frame: 24 weeks
Comparison of changes in angina symptom score
Time frame: 24 weeks
Comparison of total healthcare costs
Time frame: 24 weeks
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 31 Aug 2026.
- Background: PubMed 34756653Writing Committee Members; Gulati M, Levy PD, Mukherjee D, Amsterdam E, Bhatt DL, Birtcher KK, Blankstein R, Boyd J, Bullock-Palmer RP, Conejo T, Diercks DB, Gentile F, Greenwood JP, Hess EP, Hollenberg SM, Jaber WA, Jneid H, Joglar JA, Morrow DA, O'Connor RE, Ross MA, Shaw LJ. 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. J Am Coll Cardiol. 2021 Nov 30;78(22):e187-e285. doi: 10.1016/j.jacc.2021.07.053. Epub 2021 Oct 28.
- Background: PubMed 22692650Genders TS, Steyerberg EW, Hunink MG, Nieman K, Galema TW, Mollet NR, de Feyter PJ, Krestin GP, Alkadhi H, Leschka S, Desbiolles L, Meijs MF, Cramer MJ, Knuuti J, Kajander S, Bogaert J, Goetschalckx K, Cademartiri F, Maffei E, Martini C, Seitun S, Aldrovandi A, Wildermuth S, Stinn B, Fornaro J, Feuchtner G, De Zordo T, Auer T, Plank F, Friedrich G, Pugliese F, Petersen SE, Davies LC, Schoepf UJ, Rowe GW, van Mieghem CA, van Driessche L, Sinitsyn V, Gopalan D, Nikolaou K, Bamberg F, Cury RC, Battle J, Maurovich-Horvat P, Bartykowszki A, Merkely B, Becker D, Hadamitzky M, Hausleiter J, Dewey M, Zimmermann E, Laule M. Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohorts. BMJ. 2012 Jun 12;344:e3485. doi: 10.1136/bmj.e3485.
- Background: PubMed 25205628Renker M, Schoepf UJ, Wang R, Meinel FG, Rier JD, Bayer RR 2nd, Mollmann H, Hamm CW, Steinberg DH, Baumann S. Comparison of diagnostic value of a novel noninvasive coronary computed tomography angiography method versus standard coronary angiography for assessing fractional flow reserve. Am J Cardiol. 2014 Nov 1;114(9):1303-8. doi: 10.1016/j.amjcard.2014.07.064. Epub 2014 Aug 12.
- Background: PubMed 34235441Kamel PI, Yi PH, Sair HI, Lin CT. Prediction of Coronary Artery Calcium and Cardiovascular Risk on Chest Radiographs Using Deep Learning. Radiol Cardiothorac Imaging. 2021 Jun 17;3(3):e200486. doi: 10.1148/ryct.2021200486. eCollection 2021 Jun.
- Background: PubMed 32200712Kwon JM, Lee SY, Jeon KH, Lee Y, Kim KH, Park J, Oh BH, Lee MM. Deep Learning-Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography. J Am Heart Assoc. 2020 Apr 7;9(7):e014717. doi: 10.1161/JAHA.119.014717. Epub 2020 Mar 21.
- Background: PubMed 21477786Min JK, Dunning A, Lin FY, Achenbach S, Al-Mallah MH, Berman DS, Budoff MJ, Cademartiri F, Callister TQ, Chang HJ, Cheng V, Chinnaiyan KM, Chow B, Delago A, Hadamitzky M, Hausleiter J, Karlsberg RP, Kaufmann P, Maffei E, Nasir K, Pencina MJ, Raff GL, Shaw LJ, Villines TC. Rationale and design of the CONFIRM (COronary CT Angiography EvaluatioN For Clinical Outcomes: An InteRnational Multicenter) Registry. J Cardiovasc Comput Tomogr. 2011 Mar-Apr;5(2):84-92. doi: 10.1016/j.jcct.2011.01.007. Epub 2011 Feb 1.
- Background: PubMed 35928935Kim J, Lee SY, Cha BH, Lee W, Ryu J, Chung YH, Kim D, Lim SH, Kang TS, Park BE, Lee MY, Cho S. Machine learning models of clinically relevant biomarkers for the prediction of stable obstructive coronary artery disease. Front Cardiovasc Med. 2022 Jul 19;9:933803. doi: 10.3389/fcvm.2022.933803. eCollection 2022.
- 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.
In Hyun Jung, MD, PhD principal investigator
Yongin Severance Hospital, Yonsei University College of Medicine
5 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Assisted by the AI-Gatekeeper software group in people with Coronary Artery Disease and Diagnosis, run by INFINITT Healthcare at 5 sites in South Korea.
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 3 inclusion and 14 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 31 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 5 sites in South Korea. 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?
Unnecessary utilization of advanced cardiac imaging (time frame: 24 weeks). 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 NCT06178900, 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 31 Aug 2026.