Cardiac AmyloidosisORCHESTRANCT07529860
AI-based Echocardiography for Detection of Cardiac Amyloidosis
A study testing AI-based echocardiogram in people with Cardiac Amyloidosis and Heart Failure With Preserved Ejection Fraction (HFPEF), run by Germans Trias i Pujol Hospital.
Trial summary
Cardiac amyloidosis is characterized by deposition of misfolded protein in the myocardium causing mainly heart failure symptoms with preserved left ventricular ejection fraction. Read more about this study
- Who is it for?
- Ages 18 to 95 years
- Conditions the study is for
- Cardiac Amyloidosis, Heart Failure With Preserved Ejection Fraction (HFPEF), Left Ventricular Hypertrophy
- What is being tested?
- AI-based echocardiogram
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 200 participants
- Where
- No sites listed yet
- Study dates
- 1 Jul 2026to Oct 2028
- Phase
- Interventional
- 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 95 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Patients 18 years old or older
- Left ventricular hypertrophy defined by a wall thickness of at least 12 mm
- Echocardiographic red flags of suspected cardiac amyloidosis
- Informed consent signed
Who cannot join
- Patients with poor echocardiographic acoustic window to allow proper analysis of the data
- Patients with known cardiac amyloidosis.
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: AI-based Echocardiography for Detection of Cardiac Amyloidosis in Patients Undergoing Transthoracic Echocardiography With Left Ventricular Hypertrophy
Cardiac amyloidosis is characterized by deposition of misfolded protein in the myocardium causing mainly heart failure symptoms with preserved left ventricular ejection fraction. There are also specific clinical (bilateral carpal tunnel syndrome, polyneuropathy, skin bruising, ruptured biceps tendon…), biomarkers (disproportionally elevated NT-proBNP to the degree of heart failure, persistent elevated troponin, proteinuria..), electrocardiographic (reduced voltage of QRS, atrial fibrillation..) and echocardiographic features (concentric left ventricular hypertrophy, dilated atria, reduced global longitudinal strain with typical pattern of apical sparing, diastolic dysfunction…). Early diagnosis of the disease is crucial to identify patients that may benefit from appropriate treatment. Suspected cardiac amyloidosis on echocardiography or on cardiac magnetic resonance needs to prompt the request of serum free-light chain quantification and serum and urine immunofixation as well as single photon emission computed tomography (SPECT) using bone radiotracers. Echocardiography is the imaging technique of first choice to evaluate patients with dyspnea complaints and suspected heart failure as well as other pathologies. Echocardiography is a technique of first choice to evaluate patients with cardiovascular risk factors such as arterial hypertension and diabetes and many of those patients may have echocardiographic features that can be observed in early phases of cardiac amyloidosis. Currently, identification of patients with cardiac amyloidosis with available echocardiographic tools remains challenging. However, novel artificial intelligence (AI)-based algorithms applied to echocardiographic images for analysis may help the cardiologists in the identification of early phase of cardiac amyloidosis. Early diagnosis of cardiac amyloidosis is key to implement effective therapies that have demonstrated to improve survival. Several studies have demonstrated the accuracy of AI-based algorithms applied to echocardiography for the diagnosis of cardiac amyloidosis. The hypothesis of the present prospective study is to evaluate the accuracy of the AI-based algorithm to identify patients with echocardiographic findings suggestive of cardiac ATTR amyloidosis using as ground truth the subsequent analysis with imaging techniques that permit its diagnosis such as 99mTc-pyrophosphate (PYP) SPECT and cardiac magnetic resonance as well as hematologic tests. If needed, histological confirmation on cardiac or extracardiac tissue could be performed, as recommended by recent consensus document from the Heart Failure Association of the European Society of Cardiology.
In addition, this study will help to answer the true prevalence of ATTR cardiac amyloidosis among patients referred to transthoracic echocardiography that present red flags for ATTR cardiac amyloidosis. The AI-based algorithm is the software Us2.ai which has been used in other populations for this purpose, as previously published.
Read the detailed description
Background: Cardiac amyloidosis is characterized by deposition of misfolded protein in the myocardium causing mainly heart failure symptoms with preserved left ventricular ejection fraction. There are also specific clinical (bilateral carpal tunnel syndrome, polyneuropathy, skin bruising, ruptured biceps tendon…), biomarkers (disproportionally elevated NT-proBNP to the degree of heart failure, persistent elevated troponin, proteinuria..), electrocardiographic (reduced voltage of QRS, atrial fibrillation..) and echocardiographic features (concentric left ventricular hypertrophy, dilated atria, reduced global longitudinal strain with typical pattern of apical sparing, diastolic dysfunction…). Early diagnosis of the disease is crucial to identify patients that may benefit from appropriate treatment. Suspected cardiac amyloidosis on echocardiography or on cardiac magnetic resonance needs to prompt the request of serum free-light chain quantification and serum and urine immunofixation as well as single photon emission computed tomography using bone radiotracers.
Hypothesis: The use of artificial intelligence assisted algorithm applied to echocardiographic data may allow identification of suspected cardiac amyloidosis more precisely as compared to cardiologists with expertise in cardiac imaging.
Methods: This project proposal will comprised 3 different phases:
Phase 1: retrospective evaluation of clinically acquired echocardiographic data with reports indicating left ventricular hypertrophy (LV wall thickness ≥12 mm) and/or cardiac amyloidosis. This evaluation will consist of retrieval and analysis of echocardiographic data (around 20K studies) from 2022 to date. The data will be reanalysed by an experienced observer and a currently available artificial intelligence (AI)-based algorithm to detect suspected cardiac amyloidosis. The agreement between the algorithm and the observer will be tested. In those patients in whom the initial observer who reported the echocardiogram considered that there was suspicion of cardiac amyloidosis, the reports of additional test clinically required to confirm or rule out the diagnosis will be retrieved. Accordingly, the accuracy of the observer and the AI-based algorithm will be compared.
Phase 2. Based on the results of Phase 1, which will informed about the prevalence of the condition, the duration of the prospective assessment of AI-based algorithm assisted echocardiographic image analysis will be estimated to see if this would augment the capacity of cardiologists to pick up early patients suspected to have cardiac amyloidosis.
Phase 3. Pragmatic prospective AI-analysis assessment. Prospective analysis applying the AI-based algorithm to all patients presenting to the echo laboratory and following the clinically indicated diagnostic pathway to confirm or rule out cardiac amyloidosis according to contemporary guidelines. Patients will request to agree with the protocol and will sign informed consent.
Result measures:
The prevalence of cardiac amyloidosis will be analysed in the present project (Phase 1) and the agreement between the cardiologists experts in cardiac imaging and the artificial intelligence based algorithm will be reported. If it is demonstrated that the artificial intelligence-based algorithm provides a prevalence of true cardiac amyloidosis higher than the cardiologists, the impact in clinical practice is massive as a higher number of patients could be diagnosed and benefit from effective specific therapies, improving their clinical outcomes.
Quantify diagnostic suspicion ratio of amyloidosis of AI vs physician (positive \& negative suspicion rates)
Quantify the Echo Red Flags: AI vs physician recognized amyloidosis red flags (Red Flags will be guideline directed: wall thickness concentric and also affecting the right ventricle, "sparkling" aspect of the interventricular septum, lipomatous hypertrophy of the interatrial septum, atrial dilatation, pericardial effusion, reduced MAPSE and TAPSE, apical cherry pattern on global longitudinal strain analysis of the left ventricle, thickening of the leaflets of the heart valves, including aortic stenosis)
Quantify the AI sensitivity \& specificity. Determine the most specific and most sensitive Red Flags for the AI vs physician (Automated Us2.v2 parameters only)
Quantify the sensitivity \& specificity of Us2.V2 vs Us2.ca algorithm
Diagnosis Time: Average time it takes for a specialist to diagnose by echo vs. the average time it takes for the AI algorithm to diagnose.
Analysis of ATTR cardiac amyloidosis patient characteristics (e.g. HFpEF, HFmrEF, HFrEF, Atrial Fibrillation, Aortic Stenosis, other co-morbidities)
Laboratory: The non-invasive cardiac imaging unit of the Heart Institute of the University Hospital Germans Trias i Pujol performs 17,000 echocardiograms yearly, being more than 11,000 transthoracic echocardiograms. The collaboration with the department of nuclear medicine and radiology is excellent having access to cardiac magnetic resonance (700 studies performed yearly, and the expectations for next year will be around 1,000) and single photon emission computed tomography.
Therefore, the feasibility of the project is high. The artificial intelligence-based algorithm for the analysis of the echocardiographic images is also in place and they can be analysed.
- Conditions
- Cardiac Amyloidosis, Heart Failure With Preserved Ejection Fraction (HFPEF), Left Ventricular Hypertrophy
- Being tested
- AI-based echocardiogram
- Keywords
- artifical intelligence, echocardiography, strain, cardiac amyloidosis, left ventricular hypertrophy
- Medical subject headings
- Amyloid Neuropathies, Familial, Hypertrophy, Left Ventricular, Sprains and Strains
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-based echocardiogramDiagnostic test
In this prospective study, patients referred to transthoracic echocardiography and in whom the clinician expert in echocardiography or the AI-tool suggest that there are echocardiographic features that suggest ATTR-cardiac amyloidosis will be referred to the clinically indicated pathway (99mTc-pyrophosphate (PYP) SPECT and hematological tests) as follows (Figure 2):
Patients in whom the cardiologist expert in echocardiography and the AI-based tool agree on the suspicion of cardiac amyloidosis will be referred to further analysis with 99mTc-pyrophosphate (PYP) SPECT and hematological tests as clinically indicated.
Patients in whom the cardiologist expert in echocardiography considers there is suspected cardiac amyloidosis but the AI-based tool disagrees will be referred to the referring physician for further control and eventually analysis with 99mTc-pyrophosphate (PYP) SPECT and hematological tests as clinically indicated.
Patients in whom the cardiologist expert in echocardio
| Study group | Role | What they receive |
|---|---|---|
| AI-based diagnostic | Active comparator | Patients in whom the clinician does no consider that the echocardiogram suggests cardiac amyloidosis but the AI-based algorithm considers that it does, the patient will be assigned to continue further downstream diagnostics to confirm or rule out the diagnosis Receives: Diagnostic Test: AI-based echocardiogram |
| Control | No intervention | Patients in whom the clinician does no consider that the echocardiogram suggests cardiac amyloidosis but the AI-based algorithm considers that it does, the patient in consultation with the treating physician will not continue further downstream diagnostics to confirm or rule out the diagnosis |
What is being measured
The primary outcome is the main question the study is designed to answer.
Number of patients with ATTR-cardiac amyloidosis as assessed with AI-based echocardiography
The main objective of this prospective analysis is to estimate the true prevalence of ATTR-cardiac amyloidosis among patients referred for echocardiography and who present red flags of cardiac infiltration by amyloid by referring the patients to 99mTc-pyrophosphate (PYP) SPECT and hematological tests.
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 posted14 Apr 2026
- Study start1 Jul 2026 (estimated)
- Primary completionApr 2028 (estimated)
- Study completionOct 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (8)Papers the sponsor has linked to this study.
- Other trials for Cardiac AmyloidosisStudies 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
- Non-randomised
- Intervention model
- Sequential
- Model description
- This is an observational, prospective study that uses an AI-based algorithm (Us2.ai) that analyzes echocardiographic data currently available at the hospital. Us2.ai is the most comprehensive AI-driven echocardiogram clinical interpretation support tool on the market, with the ability to produce a complete and fully automated patient report with clinical findings. Us2.ai is intended to assist clinicians with the interpretation of the Echo studies and is currently in routine use globally. Us2.ai application was validated at the Brigham and Women's Hospital (results published by Tromp J et al. Nature Communications volume 13, Article number: 6776 (2022)), with high levels of accuracy shown across clinical and real-world cohorts worldwide (results published by Tromp J et al. The Lancet Digital Health, Volume 4, Issue 1, e46 - e54 2021). Us2.V1 including 23 key measurements and HF and PH disease indications based on International Guidelines has received FDA clearance and CE Mark for dist
- Masking
- None (open label)
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Germans Trias i Pujol Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- AstraZeneca
- Responsible party
- Sponsor
- Investigators
- Victoria Delgado Garcia, MD, PhD, Principal investigator, Hospital University Germans Trias i Pujol
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07529860
- Sponsor's protocol ID
- PI-25-265
- Acronym
- ORCHESTRA
- Status last verified
- Mar 2026
- First submitted
- 26 Mar 2026
- Last updated
- 12 Jun 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: all collected IPD, all IPD that underlie results in a publication
Publications
Venneri L, Aimo A, Porcari A, Sezer I, Ioannou A, Sheikh A, Mansell J, Razvi Y, Iyer SB, Martinez-Naharro A, Bandera F, Lim SC, Frost M, Ezekowitz J, Lam CSP, Moody W, Whelan C, Lachmann H, Wechelakar A, Emdin M, Hawkins PN, Solomon SD, Gillmore JD, Fontana M. Artificial intelligence-based echocardiographic assessment for monitoring disease progression in transthyretin cardiac amyloidosis. Eur J Heart Fail. 2025 Dec;27(12):3392-3400. doi: 10.1002/ejhf.70073. Epub 2025 Oct 16.
PubMed 41098006Garcia-Pavia P, Rapezzi C, Adler Y, Arad M, Basso C, Brucato A, Burazor I, Caforio ALP, Damy T, Eriksson U, Fontana M, Gillmore JD, Gonzalez-Lopez E, Grogan M, Heymans S, Imazio M, Kindermann I, Kristen AV, Maurer MS, Merlini G, Pantazis A, Pankuweit S, Rigopoulos AG, Linhart A. Diagnosis and treatment of cardiac amyloidosis. A position statement of the European Society of Cardiology Working Group on Myocardial and Pericardial Diseases. Eur J Heart Fail. 2021 Apr;23(4):512-526. doi: 10.1002/ejhf.2140. Epub 2021 Apr 7.
PubMed 33826207Oikonomou EK, Vaid A, Holste G, Coppi A, McNamara RL, Baloescu C, Krumholz HM, Wang Z, Apakama DJ, Nadkarni GN, Khera R. Artificial intelligence-guided detection of under-recognised cardiomyopathies on point-of-care cardiac ultrasonography: a multicentre study. Lancet Digit Health. 2025 Feb;7(2):e113-e123. doi: 10.1016/S2589-7500(24)00249-8.
PubMed 39890242Goto S, Mahara K, Beussink-Nelson L, Ikura H, Katsumata Y, Endo J, Gaggin HK, Shah SJ, Itabashi Y, MacRae CA, Deo RC. Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms. Nat Commun. 2021 May 11;12(1):2726. doi: 10.1038/s41467-021-22877-8.
PubMed 33976142Chang RS, Chiu IM, Tacon P, Abiragi M, Cao L, Hong G, Le J, Zou J, Daluwatte C, Ricchiuto P, Ouyang D. Detection of cardiac amyloidosis using machine learning on routine echocardiographic measurements. Open Heart. 2024 Dec 18;11(2):e002884. doi: 10.1136/openhrt-2024-002884.
PubMed 39694574Ioannou A, Patel RK, Razvi Y, Porcari A, Sinagra G, Venneri L, Bandera F, Masi A, Williams GE, O'Beara S, Ganesananthan S, Massa P, Knight D, Martinez-Naharro A, Kotecha T, Chacko L, Brown J, Rauf MU, Manisty C, Moon J, Lachmann H, Wechelakar A, Petrie A, Whelan C, Hawkins PN, Gillmore JD, Fontana M. Impact of Earlier Diagnosis in Cardiac ATTR Amyloidosis Over the Course of 20 Years. Circulation. 2022 Nov 29;146(22):1657-1670. doi: 10.1161/CIRCULATIONAHA.122.060852. Epub 2022 Nov 3.
PubMed 36325894Lane T, Fontana M, Martinez-Naharro A, Quarta CC, Whelan CJ, Petrie A, Rowczenio DM, Gilbertson JA, Hutt DF, Rezk T, Strehina SG, Caringal-Galima J, Manwani R, Sharpley FA, Wechalekar AD, Lachmann HJ, Mahmood S, Sachchithanantham S, Drage EPS, Jenner HD, McDonald R, Bertolli O, Calleja A, Hawkins PN, Gillmore JD. Natural History, Quality of Life, and Outcome in Cardiac Transthyretin Amyloidosis. Circulation. 2019 Jul 2;140(1):16-26. doi: 10.1161/CIRCULATIONAHA.118.038169. Epub 2019 May 21.
PubMed 31109193Writing Committee; Kittleson MM, Ruberg FL, Ambardekar AV, Brannagan TH, Cheng RK, Clarke JO, Dember LM, Frantz JG, Hershberger RE, Maurer MS, Nativi-Nicolau J, Sanchorawala V, Sheikh FH. 2023 ACC Expert Consensus Decision Pathway on Comprehensive Multidisciplinary Care for the Patient With Cardiac Amyloidosis: A Report of the American College of Cardiology Solution Set Oversight Committee. J Am Coll Cardiol. 2023 Mar 21;81(11):1076-1126. doi: 10.1016/j.jacc.2022.11.022. Epub 2023 Jan 23. No abstract available.
PubMed 36697326
Study at a glance
From the registry record, last updated by the sponsor on 12 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI-based Echocardiography for Detection of Cardiac Amyloidosis in Patients Undergoing Transthoracic Echocardiography With Left Ventricular Hypertrophy
- ClinicalTrials.gov ID
- NCT07529860
- Acronym / sponsor ID
- ORCHESTRA / PI-25-265
- Conditions
- Cardiac Amyloidosis, Heart Failure With Preserved Ejection Fraction (HFPEF), Left Ventricular Hypertrophy
- Keywords
- artifical intelligence, echocardiography, strain, cardiac amyloidosis, left ventricular hypertrophy
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Germans Trias i Pujol HospitalUniversity, hospital or other
- Collaborators
- AstraZeneca
- Study start
- 1 Jul 2026 (estimated)
- Primary completion
- Apr 2028 (estimated)
- Study completion
- Oct 2028 (estimated)
- First posted
- 14 Apr 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.
Cardiac amyloidosis is characterized by deposition of misfolded protein in the myocardium causing mainly heart failure symptoms with preserved left ventricular ejection fraction. There are also specific clinical (bilateral carpal tunnel syndrome, polyneuropathy, skin bruising, ruptured biceps tendon…), biomarkers (disproportionally elevated NT-proBNP to the degree of heart failure, persistent elevated troponin, proteinuria..), electrocardiographic (reduced voltage of QRS, atrial fibrillation..) and echocardiographic features (concentric left ventricular hypertrophy, dilated atria, reduced global longitudinal strain with typical pattern of apical sparing, diastolic dysfunction…). Early diagnosis of the disease is crucial to identify patients that may benefit from appropriate treatment. Suspected cardiac amyloidosis on echocardiography or on cardiac magnetic resonance needs to prompt the request of serum free-light chain quantification and serum and urine immunofixation as well as single photon emission computed tomography (SPECT) using bone radiotracers. Echocardiography is the imaging technique of first choice to evaluate patients with dyspnea complaints and suspected heart failure as well as other pathologies. Echocardiography is a technique of first choice to evaluate patients with cardiovascular risk factors such as arterial hypertension and diabetes and many of those patients may have echocardiographic features that can be observed in early phases of cardiac amyloidosis. Currently, identification of patients with cardiac amyloidosis with available echocardiographic tools remains challenging. However, novel artificial intelligence (AI)-based algorithms applied to echocardiographic images for analysis may help the cardiologists in the identification of early phase of cardiac amyloidosis. Early diagnosis of cardiac amyloidosis is key to implement effective therapies that have demonstrated to improve survival. Several studies have demonstrated the accuracy of AI-based algorithms applied to echocardiography for the diagnosis of cardiac amyloidosis. The hypothesis of the present prospective study is to evaluate the accuracy of the AI-based algorithm to identify patients with echocardiographic findings suggestive of cardiac ATTR amyloidosis using as ground truth the subsequent analysis with imaging techniques that permit its diagnosis such as 99mTc-pyrophosphate (PYP) SPECT and cardiac magnetic resonance as well as hematologic tests. If needed, histological confirmation on cardiac or extracardiac tissue could be performed, as recommended by recent consensus document from the Heart Failure Association of the European Society of Cardiology.
In addition, this study will help to answer the true prevalence of ATTR cardiac amyloidosis among patients referred to transthoracic echocardiography that present red flags for ATTR cardiac amyloidosis. The AI-based algorithm is the software Us2.ai which has been used in other populations for this purpose, as previously published.
Detailed description
Background: Cardiac amyloidosis is characterized by deposition of misfolded protein in the myocardium causing mainly heart failure symptoms with preserved left ventricular ejection fraction. There are also specific clinical (bilateral carpal tunnel syndrome, polyneuropathy, skin bruising, ruptured biceps tendon…), biomarkers (disproportionally elevated NT-proBNP to the degree of heart failure, persistent elevated troponin, proteinuria..), electrocardiographic (reduced voltage of QRS, atrial fibrillation..) and echocardiographic features (concentric left ventricular hypertrophy, dilated atria, reduced global longitudinal strain with typical pattern of apical sparing, diastolic dysfunction…). Early diagnosis of the disease is crucial to identify patients that may benefit from appropriate treatment. Suspected cardiac amyloidosis on echocardiography or on cardiac magnetic resonance needs to prompt the request of serum free-light chain quantification and serum and urine immunofixation as well as single photon emission computed tomography using bone radiotracers.
Hypothesis: The use of artificial intelligence assisted algorithm applied to echocardiographic data may allow identification of suspected cardiac amyloidosis more precisely as compared to cardiologists with expertise in cardiac imaging.
Methods: This project proposal will comprised 3 different phases:
Phase 1: retrospective evaluation of clinically acquired echocardiographic data with reports indicating left ventricular hypertrophy (LV wall thickness ≥12 mm) and/or cardiac amyloidosis. This evaluation will consist of retrieval and analysis of echocardiographic data (around 20K studies) from 2022 to date. The data will be reanalysed by an experienced observer and a currently available artificial intelligence (AI)-based algorithm to detect suspected cardiac amyloidosis. The agreement between the algorithm and the observer will be tested. In those patients in whom the initial observer who reported the echocardiogram considered that there was suspicion of cardiac amyloidosis, the reports of additional test clinically required to confirm or rule out the diagnosis will be retrieved. Accordingly, the accuracy of the observer and the AI-based algorithm will be compared.
Phase 2. Based on the results of Phase 1, which will informed about the prevalence of the condition, the duration of the prospective assessment of AI-based algorithm assisted echocardiographic image analysis will be estimated to see if this would augment the capacity of cardiologists to pick up early patients suspected to have cardiac amyloidosis.
Phase 3. Pragmatic prospective AI-analysis assessment. Prospective analysis applying the AI-based algorithm to all patients presenting to the echo laboratory and following the clinically indicated diagnostic pathway to confirm or rule out cardiac amyloidosis according to contemporary guidelines. Patients will request to agree with the protocol and will sign informed consent.
Result measures:
The prevalence of cardiac amyloidosis will be analysed in the present project (Phase 1) and the agreement between the cardiologists experts in cardiac imaging and the artificial intelligence based algorithm will be reported. If it is demonstrated that the artificial intelligence-based algorithm provides a prevalence of true cardiac amyloidosis higher than the cardiologists, the impact in clinical practice is massive as a higher number of patients could be diagnosed and benefit from effective specific therapies, improving their clinical outcomes.
Quantify diagnostic suspicion ratio of amyloidosis of AI vs physician (positive \& negative suspicion rates)
Quantify the Echo Red Flags: AI vs physician recognized amyloidosis red flags (Red Flags will be guideline directed: wall thickness concentric and also affecting the right ventricle, "sparkling" aspect of the interventricular septum, lipomatous hypertrophy of the interatrial septum, atrial dilatation, pericardial effusion, reduced MAPSE and TAPSE, apical cherry pattern on global longitudinal strain analysis of the left ventricle, thickening of the leaflets of the heart valves, including aortic stenosis)
Quantify the AI sensitivity \& specificity. Determine the most specific and most sensitive Red Flags for the AI vs physician (Automated Us2.v2 parameters only)
Quantify the sensitivity \& specificity of Us2.V2 vs Us2.ca algorithm
Diagnosis Time: Average time it takes for a specialist to diagnose by echo vs. the average time it takes for the AI algorithm to diagnose.
Analysis of ATTR cardiac amyloidosis patient characteristics (e.g. HFpEF, HFmrEF, HFrEF, Atrial Fibrillation, Aortic Stenosis, other co-morbidities)
Laboratory: The non-invasive cardiac imaging unit of the Heart Institute of the University Hospital Germans Trias i Pujol performs 17,000 echocardiograms yearly, being more than 11,000 transthoracic echocardiograms. The collaboration with the department of nuclear medicine and radiology is excellent having access to cardiac magnetic resonance (700 studies performed yearly, and the expectations for next year will be around 1,000) and single photon emission computed tomography.
Therefore, the feasibility of the project is high. The artificial intelligence-based algorithm for the analysis of the echocardiographic images is also in place and they can be analysed.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Non-randomised
- Intervention model
- Sequential
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- This is an observational, prospective study that uses an AI-based algorithm (Us2.ai) that analyzes echocardiographic data currently available at the hospital. Us2.ai is the most comprehensive AI-driven echocardiogram clinical interpretation support tool on the market, with the ability to produce a complete and fully automated patient report with clinical findings. Us2.ai is intended to assist clinicians with the interpretation of the Echo studies and is currently in routine use globally. Us2.ai application was validated at the Brigham and Women's Hospital (results published by Tromp J et al. Nature Communications volume 13, Article number: 6776 (2022)), with high levels of accuracy shown across clinical and real-world cohorts worldwide (results published by Tromp J et al. The Lancet Digital Health, Volume 4, Issue 1, e46 - e54 2021). Us2.V1 including 23 key measurements and HF and PH disease indications based on International Guidelines has received FDA clearance and CE Mark for dist
| Arm | Type | Receives |
|---|---|---|
| AI-based diagnostic | Active comparator | Diagnostic Test: AI-based echocardiogramPatients in whom the clinician does no consider that the echocardiogram suggests cardiac amyloidosis but the AI-based algorithm considers that it does, the patient will be assigned to continue further downstream diagnostics to confirm or rule out the diagnosis |
| Control | No intervention | –Patients in whom the clinician does no consider that the echocardiogram suggests cardiac amyloidosis but the AI-based algorithm considers that it does, the patient in consultation with the treating physician will not continue further downstream diagnostics to confirm or rule out the diagnosis |
Who may qualify
- Ages
- 18 to 95 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- Patients 18 years old or older
- Left ventricular hypertrophy defined by a wall thickness of at least 12 mm
- Echocardiographic red flags of suspected cardiac amyloidosis
- Informed consent signed
Key exclusion criteria (2 listed)
- Patients with poor echocardiographic acoustic window to allow proper analysis of the data
- Patients with known cardiac amyloidosis.
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 |
|---|---|---|
| AI-based echocardiogramIn this prospective study, patients referred to transthoracic echocardiography and in whom the clinician expert in echocardiography or the AI-tool suggest that there are echocardiographic features that suggest ATTR-cardiac amyloidosis will be referred to the clinically indicated pathway (99mTc-pyrophosphate (PYP) SPECT and hematological tests) as follows (Figure 2): Patients in whom the cardiologist expert in echocardiography and the AI-based tool agree on the suspicion of cardiac amyloidosis will be referred to further analysis with 99mTc-pyrophosphate (PYP) SPECT and hematological tests as clinically indicated. Patients in whom the cardiologist expert in echocardiography considers there is suspected cardiac amyloidosis but the AI-based tool disagrees will be referred to the referring physician for further control and eventually analysis with 99mTc-pyrophosphate (PYP) SPECT and hematological tests as clinically indicated. Patients in whom the cardiologist expert in echocardio | Diagnostic test | AI-based diagnostic |
Primary outcome
Number of patients with ATTR-cardiac amyloidosis as assessed with AI-based echocardiography
Time frame: 1 year
The main objective of this prospective analysis is to estimate the true prevalence of ATTR-cardiac amyloidosis among patients referred for echocardiography and who present red flags of cardiac infiltration by amyloid by referring the patients to 99mTc-pyrophosphate (PYP) SPECT and hematological tests.
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 12 Jun 2026.
- Background: PubMed 41098006Venneri L, Aimo A, Porcari A, Sezer I, Ioannou A, Sheikh A, Mansell J, Razvi Y, Iyer SB, Martinez-Naharro A, Bandera F, Lim SC, Frost M, Ezekowitz J, Lam CSP, Moody W, Whelan C, Lachmann H, Wechelakar A, Emdin M, Hawkins PN, Solomon SD, Gillmore JD, Fontana M. Artificial intelligence-based echocardiographic assessment for monitoring disease progression in transthyretin cardiac amyloidosis. Eur J Heart Fail. 2025 Dec;27(12):3392-3400. doi: 10.1002/ejhf.70073. Epub 2025 Oct 16.
- Background: PubMed 33826207Garcia-Pavia P, Rapezzi C, Adler Y, Arad M, Basso C, Brucato A, Burazor I, Caforio ALP, Damy T, Eriksson U, Fontana M, Gillmore JD, Gonzalez-Lopez E, Grogan M, Heymans S, Imazio M, Kindermann I, Kristen AV, Maurer MS, Merlini G, Pantazis A, Pankuweit S, Rigopoulos AG, Linhart A. Diagnosis and treatment of cardiac amyloidosis. A position statement of the European Society of Cardiology Working Group on Myocardial and Pericardial Diseases. Eur J Heart Fail. 2021 Apr;23(4):512-526. doi: 10.1002/ejhf.2140. Epub 2021 Apr 7.
- Background: PubMed 39890242Oikonomou EK, Vaid A, Holste G, Coppi A, McNamara RL, Baloescu C, Krumholz HM, Wang Z, Apakama DJ, Nadkarni GN, Khera R. Artificial intelligence-guided detection of under-recognised cardiomyopathies on point-of-care cardiac ultrasonography: a multicentre study. Lancet Digit Health. 2025 Feb;7(2):e113-e123. doi: 10.1016/S2589-7500(24)00249-8.
- Background: PubMed 33976142Goto S, Mahara K, Beussink-Nelson L, Ikura H, Katsumata Y, Endo J, Gaggin HK, Shah SJ, Itabashi Y, MacRae CA, Deo RC. Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms. Nat Commun. 2021 May 11;12(1):2726. doi: 10.1038/s41467-021-22877-8.
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- Background: PubMed 36325894Ioannou A, Patel RK, Razvi Y, Porcari A, Sinagra G, Venneri L, Bandera F, Masi A, Williams GE, O'Beara S, Ganesananthan S, Massa P, Knight D, Martinez-Naharro A, Kotecha T, Chacko L, Brown J, Rauf MU, Manisty C, Moon J, Lachmann H, Wechelakar A, Petrie A, Whelan C, Hawkins PN, Gillmore JD, Fontana M. Impact of Earlier Diagnosis in Cardiac ATTR Amyloidosis Over the Course of 20 Years. Circulation. 2022 Nov 29;146(22):1657-1670. doi: 10.1161/CIRCULATIONAHA.122.060852. Epub 2022 Nov 3.
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- Background: PubMed 36697326Writing Committee; Kittleson MM, Ruberg FL, Ambardekar AV, Brannagan TH, Cheng RK, Clarke JO, Dember LM, Frantz JG, Hershberger RE, Maurer MS, Nativi-Nicolau J, Sanchorawala V, Sheikh FH. 2023 ACC Expert Consensus Decision Pathway on Comprehensive Multidisciplinary Care for the Patient With Cardiac Amyloidosis: A Report of the American College of Cardiology Solution Set Oversight Committee. J Am Coll Cardiol. 2023 Mar 21;81(11):1076-1126. doi: 10.1016/j.jacc.2022.11.022. Epub 2023 Jan 23. No abstract available.
- Individual participant dataSharing planned: Yes. all collected IPD, all IPD that underlie results in a publication
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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.
Victoria Delgado Garcia, MD, PhD principal investigator
Hospital University Germans Trias i Pujol
Questions about this trial
What is this clinical trial studying?
A study testing AI-based echocardiogram in people with Cardiac Amyloidosis and Heart Failure With Preserved Ejection Fraction (HFPEF), run by Germans Trias i Pujol Hospital.
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 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: opening soon, as of the sponsor's last update on 12 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
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 Victoria Delgado Garcia, MD, PhD (+3493 497 8436, vdelgadog.germanstrias@gencat.cat), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Number of patients with ATTR-cardiac amyloidosis as assessed with AI-based echocardiography (time frame: 1 year). 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 NCT07529860, which also lists 8 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 12 Jun 2026.