Cardiac AmyloidosisNCT05139797
Artificial Intelligence Guided Echocardiographic Screening of Rare Diseases (EchoNet-Screening)
An observational study of Cardiac Amyloidosis, run by Cedars-Sinai Medical Center at 1 site in United States.
Trial summary
Despite rapidly advancing developments in targeted therapeutics and genetic sequencing, persistent limits in the accuracy and throughput of clinical phenotyping has led to a widening gap between the potential and the actual benefits realized by precision medicine. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Cardiac Amyloidosis
- What is being tested?
- EchoNet-LVH screening for cardiac amyloidosis
- How many people
- 300 participants
- Where
- 1 siteUnited States
- Study dates
- 18 Nov 2021to 1 Jun 2027
- 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
- No healthy volunteers
- Adult, Older adult
Study population: Patients who have a high suspicion for cardiac amyloidosis by AI algorithm
Who may be able to join
- Patients who have a high suspicion for cardiac amyloidosis by AI algorithm
Who cannot join
- Patients who decline to be seen at specialty clinic
- Patients who have passed away
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
Cedars-Sinai Medical Centre (Los Angeles)
Los Angeles, California, 90048
Lily Stern, MD, (310) 248-8300, lily.stern@cshs.org
About this study
Official title: Artificial Intelligence Guided Echocardiographic Screening of Rare Diseases
Despite rapidly advancing developments in targeted therapeutics and genetic sequencing, persistent limits in the accuracy and throughput of clinical phenotyping has led to a widening gap between the potential and the actual benefits realized by precision medicine.
Recent advances in machine learning and image processing techniques have shown that machine learning models can identify features unrecognized by human experts and more precisely/accurately assess common measurements made in clinical practice.
The investigators have developed an algorithm, termed EchoNet-LVH, to identify cardiac hypertrophy and identify patients who would benefit from additional screening for cardiac amyloidosis and will prospectively evaluate its accuracy in identifying patients whom would benefit from additional screening for cardiac amyloidosis.
Read the detailed description
Despite rapidly advancing developments in targeted therapeutics and genetic sequencing, persistent limits in the accuracy and throughput of clinical phenotyping has led to a widening gap between the potential and the actual benefits realized by precision medicine. This conundrum is exemplified by current approaches to assessing morphologic alterations of the heart. If reliably identified, certain cardiac diseases (e.g. cardiac amyloidosis and hypertrophic cardiomyopathy) could avoid misdiagnosis and receive efficient treatment initiation with specific targeted therapies. The ability to reliably distinguish between cardiac disease types of similar morphology but different etiology would also enhance specificity for linking genetic risk variants and determining mechanisms
Recent advances in machine learning and image processing techniques have shown that machine learning models can identify features unrecognized by human experts and more precisely/accurately assess common measurements made in clinical practice. In echocardiography, this ability for precision measurement and detection is important in both disease screening as well as diagnosis of cardiovascular disease.
Echocardiography is routinely and frequently used for diagnosis and prognostication in routine clinical care, however there is often subjectivity in interpretation and heterogeneity in application. Human attention is fatigable and has heterogenous interpretation between providers. AI guided disease screening workflows have been proposed for rare diseases such as cardiac amyloidosis and other diseases with relatively low prevalence but significant human impact with targeted therapies when detected early. This is an area particularly suitable for AI as there are multiple mimics where diseases like hypertrophic cardiomyopathy, cardiac amyloidosis, aortic stenosis, and other phenotypes might visually be similar but can be distinguished by AI algorithms. The investigators have developed an algorithm, termed EchoNet-LVH, to identify cardiac hypertrophy and identify patients who would benefit from additional screening for cardiac amyloidosis, hypertrophic cardiomyopathy and other diseases. E
- Conditions
- Cardiac Amyloidosis
- Being tested
- EchoNet-LVH screening for cardiac amyloidosis
- Keywords
- Echocardiogram, Artificial Intelligence
- Medical subject headings
- Amyloid Neuropathies, Familial
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
EchoNet-LVH screening for cardiac amyloidosisOther
An AI algorithm identifies LVH, low voltage, and high suspicion for cardiac amyloidosis. The intervention is the suspicion score. Patients with high suspicion score will be referred to specialty clinic for standard of care evaluation, screening, and treatment as determined by physicians.
| Study group | Role | What they receive |
|---|---|---|
| Artificial Intelligence Screening for Cardiac Amyloidosis | An artificial intelligence algorithm will produce a probability of cardiac amyloidosis that will trigger referral to specialty clinic for further evaluation. Receives: Other: EchoNet-LVH screening for cardiac amyloidosis |
What is being measured
The primary outcome is the main question the study is designed to answer.
Number of New Diagnoses of Cardiac Amyloidosis Found
From chart review, identification of patients who have a downstream diagnosis of cardiac amyloidosis
Measured: 6 months
Secondary outcomes (2)
Number of New Diagnoses of TTR Amyloidosis Found
From chart review, identification of patients who have a downstream diagnosis of TTR amyloidosis
Measured: 6 months
Number of New Diagnoses of AL Amyloidosis Found
From chart review, identification of patients who have a downstream diagnosis of AL amyloidosis
Measured: 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Dec 2021
- Study start18 Nov 2021
- Primary completion1 Jan 2027 (estimated)
- Study completion1 Jun 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Cardiac AmyloidosisStudies of the same condition, open and under way.
- More trials run by Cedars-Sinai Medical CenterThe 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
- 300 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Cedars-Sinai Medical Center
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Lily Stern, Cedars-Sinai Medical Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05139797
- Sponsor's protocol ID
- STUDY00001720
- Status last verified
- Jul 2026
- First submitted
- 18 Nov 2021
- Last updated
- 22 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 22 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence Guided Echocardiographic Screening of Rare Diseases
- ClinicalTrials.gov ID
- NCT05139797
- Acronym / sponsor ID
- STUDY00001720
- Conditions
- Cardiac Amyloidosis
- Keywords
- Echocardiogram, Artificial Intelligence
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 300 participants (estimated)
- Lead sponsor
- Cedars-Sinai Medical CenterUniversity, hospital or other
- Study start
- 18 Nov 2021 (actual)
- Primary completion
- 1 Jan 2027 (estimated)
- Study completion
- 1 Jun 2027 (estimated)
- First posted
- 1 Dec 2021 (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.
Despite rapidly advancing developments in targeted therapeutics and genetic sequencing, persistent limits in the accuracy and throughput of clinical phenotyping has led to a widening gap between the potential and the actual benefits realized by precision medicine.
Recent advances in machine learning and image processing techniques have shown that machine learning models can identify features unrecognized by human experts and more precisely/accurately assess common measurements made in clinical practice.
The investigators have developed an algorithm, termed EchoNet-LVH, to identify cardiac hypertrophy and identify patients who would benefit from additional screening for cardiac amyloidosis and will prospectively evaluate its accuracy in identifying patients whom would benefit from additional screening for cardiac amyloidosis.
Detailed description
Despite rapidly advancing developments in targeted therapeutics and genetic sequencing, persistent limits in the accuracy and throughput of clinical phenotyping has led to a widening gap between the potential and the actual benefits realized by precision medicine. This conundrum is exemplified by current approaches to assessing morphologic alterations of the heart. If reliably identified, certain cardiac diseases (e.g. cardiac amyloidosis and hypertrophic cardiomyopathy) could avoid misdiagnosis and receive efficient treatment initiation with specific targeted therapies. The ability to reliably distinguish between cardiac disease types of similar morphology but different etiology would also enhance specificity for linking genetic risk variants and determining mechanisms
Recent advances in machine learning and image processing techniques have shown that machine learning models can identify features unrecognized by human experts and more precisely/accurately assess common measurements made in clinical practice. In echocardiography, this ability for precision measurement and detection is important in both disease screening as well as diagnosis of cardiovascular disease.
Echocardiography is routinely and frequently used for diagnosis and prognostication in routine clinical care, however there is often subjectivity in interpretation and heterogeneity in application. Human attention is fatigable and has heterogenous interpretation between providers. AI guided disease screening workflows have been proposed for rare diseases such as cardiac amyloidosis and other diseases with relatively low prevalence but significant human impact with targeted therapies when detected early. This is an area particularly suitable for AI as there are multiple mimics where diseases like hypertrophic cardiomyopathy, cardiac amyloidosis, aortic stenosis, and other phenotypes might visually be similar but can be distinguished by AI algorithms. The investigators have developed an algorithm, termed EchoNet-LVH, to identify cardiac hypertrophy and identify patients who would benefit from additional screening for cardiac amyloidosis, hypertrophic cardiomyopathy and other diseases. E
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Artificial Intelligence Screening for Cardiac Amyloidosis | – | Other: EchoNet-LVH screening for cardiac amyloidosisAn artificial intelligence algorithm will produce a probability of cardiac amyloidosis that will trigger referral to specialty clinic for further evaluation. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients who have a high suspicion for cardiac amyloidosis by AI algorithm
Key inclusion criteria (1 listed)
- Patients who have a high suspicion for cardiac amyloidosis by AI algorithm
Key exclusion criteria (2 listed)
- Patients who decline to be seen at specialty clinic
- Patients who have passed away
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 |
|---|---|---|
| EchoNet-LVH screening for cardiac amyloidosisAn AI algorithm identifies LVH, low voltage, and high suspicion for cardiac amyloidosis. The intervention is the suspicion score. Patients with high suspicion score will be referred to specialty clinic for standard of care evaluation, screening, and treatment as determined by physicians. | Other | Artificial Intelligence Screening for Cardiac Amyloidosis |
Primary outcome
Number of New Diagnoses of Cardiac Amyloidosis Found
Time frame: 6 months
From chart review, identification of patients who have a downstream diagnosis of cardiac amyloidosis
2 secondary outcomes
Number of New Diagnoses of TTR Amyloidosis Found
Time frame: 6 months
Number of New Diagnoses of AL Amyloidosis Found
Time frame: 6 months
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 22 Jul 2026.
- Related InfoLink provided by the sponsor.
- Individual participant dataSharing planned: No
Related trials for Cardiac Amyloidosis
Active, not recruitingObservational study
A Retrospective Observational Study of Epidemiology and Outcomes in Transthyretin Amyloid Cardiomyopathy in Germany
ATTR-CM (Transthyretin Amyloid Cardiomyopathy)
Active, not recruitingPhase 3
EPIC-ATTR: A Study to Evaluate the Effect of Eplontersen on the Transthyretin Reduction and Long-term Safety in Chinese Subjects With Transthyretin Amyloid Cardiomyopathy
Transthyretin Amyloid Cardiomyopathy
RecruitingObservational study
Essen Amyloidosis Registry
Amyloidosis; Systemic, Amyloidosis Cardiac
Active, not recruitingPhase 3
A Phase 3 Study of NTLA-2001 in ATTRv-PN
Neuromuscular Disease, Neuromuscular Diseases (NMD)
RecruitingPhase 3
MAGNITUDE: A Phase 3 Study of NTLA-2001 in Participants With Transthyretin Amyloidosis With Cardiomyopathy (ATTR-CM)
Transthyretin Amyloidosis (ATTR) With Cardiomyopathy
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.
Cedars-Sinai Medical Centre (Los Angeles)
Los Angeles, United States
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 Cardiac Amyloidosis, run by Cedars-Sinai Medical Center at 1 site in United States.
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 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 22 Jul 2026. 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 United States. 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?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Number of New Diagnoses of Cardiac Amyloidosis Found (time frame: 6 months). 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 22 Jul 2026.