AI-based Echocardiography for Detection of Cardiac Amyloidosis in Patients Undergoing Transthoracic Echocardiography With Left Ventricular Hypertrophy
Phase: NA
Conditions: Cardiac Amyloidosis, Heart Failure With Preserved Ejection Fraction (HFPEF), Left Ventricular Hypertrophy
Sponsor: Germans Trias i Pujol Hospital

 
 
Research Project Protocol 
 
TITLE 
 
 
PROSPECTIVE AI-BASED ECHOCARDIOGRAPHY FOR DETECTION OF CARDIAC AMYLOIDOSIS 
IN PATIENTS UNDERGOING TRANSTHORACIC ECHOCARDIOGRAPHY WITH LEFT 
VENTRICULAR HYPERTROPHY 
 
 
 
STUDY CODE 
 
ORCHESTRA-PROS 
 
PROMOTOR  
 
Hospital University Germans Trias i Pujol 
Date 
17/12/2025 
 
   
 
 

 
 
 Study protocol version:   2.0  -  Date:   17/12/2025   
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1. RESUMEN DEL PROTOCOLO  
 
 
Title and subtitles, version and date 
of the protocol 
PROSPECTIVE AI-BASED ECHOCARDIOGRAPHY FOR 
DETECTION OF CARDIAC AMYLOIDOSIS IN PATIENTS 
UNDERGOING TRANSTHORACIC 
ECHOCARDIOGRAPHY WITH LEFT VENTRICULAR 
HYPERTROPHY 
ORCHESTRA 
Protocol version 2.0 
Date: 17/12/2025 
Justification and context 
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 (AI) assisted 
algorithm applied to echocardiographic data 
may allow identification of suspected cardiac 
amyloidosis more precisely as compared to 
cardiologists with expertise in cardiac imaging. 
Objetives 
 
The main objective of this prospective analysis is to 
estimate the true prevalence of ATTR-cardiac 

 
 
 Study protocol version:   2.0  -  Date:   17/12/2025   
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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.  
We will also evaluate: 
• 
The diagnostic accuracy of the AI-based 
algorithm to identify ATTR-cardiac amyloidosis. 
• 
The characteristics of patients in whom the 
clinician would have not diagnosed the disease 
with the echocardiography but the AI-algorithm 
identifies them as suggestive of having the 
disease. 
• 
The impact on the workflow of the patients with 
suspected cardiac amyloidosis by the 
implementation of AI-tools at the 
echocardiography laboratory. 
 
Design 
 
Prospective 
Study population 
 
Patients 18 years old or older 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 
echocardiography considers there is not 
cardiac amyloidosis but the AI-based tool 

 
 
 Study protocol version:   2.0  -  Date:   17/12/2025   
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disagrees will be asked for consent to 
participate in the study with further tests 
including 99mTc-pyrophosphate (PYP) SPECT 
and hematological tests. 
 
Sample size 
 
200 
Variables  
 
The variables that will be analysed include 
demographics, clinical (symptoms and signs), 
laboratory (hemogram and regular biochemistry 
data such as renal function, glycemia, lipid profile..) 
and specific tests to confirm or rule out the diagnosis 
of cardiac amyloidosis. 
Source of data 
 
PACS and SAP 
Medication or medical device 
subject to evaluation (when 
applicable) 
NA 
Data analysis 
 
Categorical variables are presented as frequencies 
and percentages, and continuous variables are 
reported as mean ± standard deviation or median 
with interquartile range (IQR) according to the 
distribution of the variable. The Student T-test will 
be used for comparison of continuous variables, and 
the Chi-square test will be used to compare 
categorical variables. Sensitivity, specificity, 
negative and positive predictive value will be 
analysed using a 2x2 contingency table. A p-value of 
<0.05 will be considered statistically significant for 
all analyses. All statistical tests will be two-sided. 
 
Stages and schedule 
 
Please see below the scheme 
 
  

 
 
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1. BACKGROUND 
 
Cardiac amyloidosis is characterized by deposition of misfolded protein in the 
myocardium causing mainly heart failure symptoms with preserved left ventricular 
ejection fraction.1  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…).2 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.2 
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.2 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.3 Several studies have demonstrated the accuracy of AI-based 
algorithms applied to echocardiography for the diagnosis of cardiac amyloidosis.4-6 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 (Figure 1), 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.8  
 
Figure 1: Diagnostic algorithm of cardiac amyloidosis.7 

 
 
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2. REFERENCES  
 
1.- Writing Committee; Kittleson   MM, Ruberg   FL, Ambardekar   AV, Brannagan   TH, 
Cheng   RK, et al.  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;81:1076–
126. 10.1016/j.jacc.2022.11.022 
2.- Lane T, Fontana M, Martinez-Naharro A, et al. Natural History, Quality of Life, and 
Outcome in Cardiac Transthyretin Amyloidosis. Circulation 2019; 140:16–26. 
doi:10.1161/CIRCULATIONAHA.118.038169 
3.- Ioannou   A, Patel   RK, Razvi   Y, Porcari   A, Sinagra   G, Venneri   L, et al.  Impact of 
earlier diagnosis in cardiac ATTR amyloidosis over the course of 20 years. Circulation  
2022;146:1657–70. 10.1161/CIRCULATIONAHA.122.060852 
4.-Chang RS, Chiu I, Tacon P, Abiragi M, Cao L, Hong G, et al. Detection of cardiac 
amyloidosis using machine learning on routine echocardiographic measurements. Open 
Heart. 2024;11:e002884. https://doi.org/10.1136/openhrt-2024-002884 
5.-Goto S, Mahara K, Beussink-Nelson L, et al. Artificial intelligence-enabled fully 
automated detection of cardiac amyloidosis using electrocardiograms and 
echocardiograms. Nat Commun 2021; 12:2726. 
doi:10.1038/s41467-021-22877-8 
6.-Oikonomou   EK, Vaid   A, Holste   G, Coppi   A, McNamara   RL, Baloescu   C, et al.  
Artificial intelligence-guided detection of under-recognised cardiomyopathies on point-
of-care cardiac ultrasonography: a multicentre study. Lancet Digit Health  2025;7:e113–
23. 10.1016/S2589-7500(24)00249-8 
7.- Garcia-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 

 
 
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Pericardial Diseases. Eur J Heart Fail. 2021 Apr;23(4):512-526. doi: 10.1002/ejhf.2140. 
Epub 2021 Apr 7. 
8.-Venneri, L., Aimo, A., Porcari, A., Sezer, I., Ioannou, A., Sheikh, A., Mansell, J., Razvi, 
Y., Iyer, S.B., Martinez-Naharro, A., Bandera, F., Lim, S.C., Frost, M., Ezekowitz, J., Lam, 
C.S.P., Moody, W., Whelan, C., Lachmann, H., Wechelakar, A., Emdin, M., Hawkins, P.N., 
Solomon, S.D., Gillmore, J.D. and Fontana, M. (2025), Artificial intelligence-based 
echocardiographic assessment for monitoring disease progression in transthyretin 
cardiac amyloidosis. Eur J Heart Fail. https://doi.org/10.1002/ejhf.70073 
 
 
3. HYPOTHESIS 
 
The systematic use of AI assisted algorithm applied to echocardiographic data will 
provide an accurate estimate of the true prevalence of ATTR cardiac amyloidosis 
among patients referred to echocardiography who present red flags of this disease and 
inform about policies that can help to improve the workflow of these patients and the 
access to effective therapies.  
 
4. OBJETIVES  
 
4.1. Primary objective 
 
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.  
 
 
4.2.  Secondary objective  
We will also evaluate: 
• 
The diagnostic accuracy of the AI-based algorithm to identify ATTR-cardiac amyloidosis. 
• 
The characteristics of patients in whom the clinician would have not diagnosed the disease 
with the echocardiography but the AI-algorithm identifies them as suggestive of having the 
disease. 
• 
The impact on the workflow of the patients with suspected cardiac amyloidosis by the 
implementation of AI-tools at the echocardiography laboratory. 
 
 
5. METHODS 
 
5.1 Type of study 
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-

 
 
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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 distribution in addition to 
country specific regulatory clearances. In April 2024, Us2.ai received FDA Clearance for Us2.v2 
an extended version of its software, 22 additional measurements and new features such as 
Strain, Stress Echo and integrating Cardiac Amyloid detection (measurements based indications 
on International Guidelines). CE clearance granted in April 2025 - also includes disease detection 
for HCM and Mitral Regurgitation. In December 2024, the validation study of Us2.ca was 
presented as Late Breaking Trial at EuroEcho Imaging Congress in Berlin for indications for 
Cardiac Amyloid using Pattern recognition and Echo measurements Guidelines Directed 
indications. This model has received CE Mark clearance in April 2025 and is pending FDA 
clearance. As included in the supplemental material, the device is a software platform that 
automatically processes, analyses and makes measurements on acquired transthoracic cardiac 
ultrasound images, producing a full report with measurements of several key cardiac structural 
and functional parameters. The data produced by this software is intended to be used to support 
qualified cardiologists, sonographers, or other licensed professional healthcare practitioners for 
clinical decision-making. The device is indicated for use in adult patients. The device has not 
been validated for the assessment of congenital heart disease, valve disease, pericardial disease, 
and/or intra-cardiac lesions (e.g. tumours, thrombi). The fact that the device is CE Mark 
approved for making measurements of structural and functional parameters and to support 
clinical-decision making in adult patients, this qualifies the device to provide data that points 
out towards a diagnosis of cardiac amyloidosis.  
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 echocardiography considers there is not 
cardiac amyloidosis but the AI-based tool disagrees will be asked for consent to 
participate in the study with further tests including 99mTc-pyrophosphate (PYP) SPECT 
and hematological tests. 
All patients will be  asked to provide informed consent to participate in this prospective 
observational study for analysis of the clinical data and in those in whom as per current 
guidelines the additional tests are not mandatory to undergo those tests. 

 
 
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Figure 2: Workflow of patients referred to transthoracic echocardiography.  
 
Clinical data will be pseudoanonymized. Important aspects to bear in mind include: 
• 
The pseudonymization of the collected data in the database is performed by a third 
person with technical and functional independence from the research team and access 
to SAP. The data do not leave the hospital at all and it is secured in an institutional 
(departmental) unit (J:). The echocardiographic data do not leave the hospital and they 
will be analyzed in hospital. The analysis the software performs does not go back to PACs 
and it is stored in a an institutional (departmental) unit (J:). 
• 
The software Us2.ai can not perform training during the study. If agreed in future 
projects, the patient data could be used for training and retraining the software, for 
which we would reapply the ethical approval and this would require a different 
version of the software (development mode). 
• 
The hospital has experience with the use of this software. The software is already 
implemented in the center for another project that received the approval (PI-22-
272) and the DOSI was always involved in its implementation. For this new project 
we will also request the help of the DOSI because in this specific project the software 
will be physically installed. 
 
5.2  Design 
This is an observational, prospective analysis. 
 
5.3 Study population 
− 
Inclusion criteria, Patients 18 years old or older with left ventricular hypertrophy defined by 
a wall thickness of at least 12 mm and other echocardiographic red flags of suspected cardiac 
amyloidosis 
 
− 
Exclusion criteria, Patients with poor echocardiographic acoustic window to allow proper 
analysis of the data will be excluded as well as patients with already known cardiac 
amyloidosis. 
 
 

 
 
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5.4 Study variables 
 
5.4.1 Outcome, exposure, or effect variables 
The variable of interest will be the prevalence of ATTR-cardiac amyloidosis among patients 
referred to transthoracic echocardiography and with echocardiographic red flags of this disease. 
 
5.4.1 
Other variables  
Other variables of interest will include: 
• 
Demographics: age and sex. 
• 
Clinical variables: symptoms of heart failure, blood pressure, heart rate, body mass 
index, heart rhythm, medications, cardiovascular risk factors and comorbidities (i.e. 
chronic kidney disease, chronic obstructive pulmonary disease, cancer, stroke…). 
• 
Laboratory variables: hemogram, biochemistry, NT.proBNP, troponin. 
• 
Echocardiographic variables: chamber quantification, presence of significant valvular 
heart disease, longitudinal strain. 
• 
Hematological tests to rule out/confirm AL amyloidosis 
• 
 99mTc-pyrophosphate (PYP) SPECT Perugini score and quantitative assessment 
• 
Clinical outcomes: heart failure hospitalization, cardiovascular death, all-cause death 
• 
Time to occurrence of the clinical outcome: from the time of the echocardiogram to the 
occurrence of the clinical outcome. 
 
5.5 Study assessments: 
This is a prospective study of echocardiographic data clinically acquired. The results by the 
expert in echocardiography clinician and the AI-based algorithm will be compared and 
confirmed using the ground truth that include 99mTc-pyrophosphate (PYP) SPECT and 
hematological tests. This will allow us to estimate the prevalence of the disease and dimension 
the need for effective therapies. 
 
5.6 Sample size 
The prevalence of cardiac amyloidosis remains unclear and the available data are biased. 
Therefore, this prospective analysis will help us to dimension the needs for imaging and 
additional tests as well as the workflows of patients with cardiac amyloidosis that may benefit 
from effective therapies. 
 
5.7 Statistical analysis 
 
Categorical variables are presented as frequencies and percentages, and continuous variables 
are reported as mean ± standard deviation or median with interquartile range (IQR) according 
to the distribution of the variable. The Student T-test will be used for comparison of 
continuous variables, and the Chi-square test will be used to compare categorical variables. 
Sensitivity, specificity, negative and positive predictive value will be analysed using a 2x2 
contingency table. A p-value of <0.05 will be considered statistically significant for all analyses. 
All statistical tests will be two-sided. 
 
5.8 Limitations 
 

 
 
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There will be a selection bias in those patients in whom the further workflow to diagnose the 
disease will not be completed. 
 
 
 
6. SOURCES OF DATA ACQUISITION AND MANAGEMENT 
 
6.1 Data sources 
 
The source of data is secondary (from the clinically acquired echocardiographic data and stored 
in PACS). 
 
6.2 Data management and quality control 
 
I do not expect to have missing data. 
 
 
7. ETHICAL AND LEGAL ASPECTS 
 
We commit to submit the study for evaluation by the accredited ethical committee of the 
University Hospital Germans Trias i Pujol. 
 
The present study is compliant with the fundamental ethical principles contained in the 
Declaration of Helsinki (Helsinki, October 2024) and the specific regulations according to the 
type of study: 
 
• 
Research involving the analysis and storage of biological material: Law 14/2007 on 
Biomedical Research and Royal Decree 1716/2011 regulating Biobanks. 
• 
Observational studies involving medicinal products: Royal Decree 957/2020, of 
November 3, regulating observational studies involving medicinal products for human 
use. 
• 
Research involving healthcare devices: Law EU 2017/745 i Royal Decree 192/2023 
 
7.1 Benefit-risk assessment for research subjects 
The present analysis will inform us about the accuracy of the physicians and the AI-based 
algorithm for the diagnosis of cardiac amyloidosis and the prevalence of the disease (cardiac 
amyloidosis) helping us to elaborate workflows that will be more efficient and optimize the 
resources. 
 
7.2 Information to subjects and informed consent 
Patients will be asked to sign the informed consent to participate in the study (please see 
appendix). 
 
7.3 Confidentiality and data protection 

 
 
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All patient data will be pseudoanonymised and handled in compliance with national and EU 
data protection laws, including the General Data Protection Regulation (GDPR) and Spanish 
Organic Law 3/2018. Data will be securely stored, with access restricted to authorized 
researchers only. 
 
7.4 Interference with prescribing and dispensing habits 
 
Not applicable. 
 
 
8. MANAGEMENT AND COMMUNICATION OF ADVERSE REACTIONS 
 
This prospective study evaluates a diagnostic workflow with minimal adverse reactions 
(claustrophobia in case of cardiac magnetic resonance or limited nephrotoxicity in case of 
patients with renal disease that will undergo cardiac magnetic resonance – improbable since in 
those patients we will not use gadolinium contrast agent – or derived from the extraction of 
blood samples for the haematological tests). All these tests will be clinically indicated. 
 
 
9. OBTAINING AND MANAGEMENT OF BIOLOGICAL SAMPLES 
 
Not applicable. 
 
 
10. PLANS FOR DISSEMINATION AND COMMUNICATION OF RESULTS 
 
Not applicable. 
 
 
11. SOURCE OF FUNDING 
The study will receive an unrestricted grant from Astra Zeneca, which has no access to the data 
and does not have influence on the results of the study.  
 
12. WORK PLAN FOR THE EXECUTION OF THE STUDY 
 
Please see below: