A Multi-center Cohort Study for Conventional Ultrasound Image Set Collection to Create a Training Data Set for Research Purposes (Image Processing and Analysis, AI Model Training).
Conditions: Deep Vein Thrombosis
Sponsor: ThrombUS+

 
 
 
 
 
 
 
 
 
 
 
Clinical Study A Protocol 
 
 
28 May 2024 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Horizon Innovation Action | Agreement No. 101137227 
HORIZON-HLTH-2023-TOOL-05-05 
Co-funded by the European Union 
 
 

Study A Protocol 
 
 
 
 
v 1.0 | 28 May 2024 
 
ii 
ThrombUS+ Consortium 
ATHENA 
Research and Innovation Center in Information, 
Communication and Knowledge Technologies, Greece 
Eleni Kaldoudi 
kaldoudi@med.duth.gr 
KTU 
Kaunas University of Technology 
Lithuania 
Vaidotas Marozas 
vaidotas.marozas@ktu.lt 
VERMON 
Vermon SA  
France 
Mathieu Legros 
m.legros@vermon.com 
FRAUNHOFER 
Institute for Photonic Microsystems, Fraunhofer 
Germany 
Marco Kircher 
Marco.kircher@ipms.fraunhofer.de 
TELEMED 
Telemed Ultrasound Medical Systems 
Lithuania 
Dmitry Novikov 
d.novikov@pcultrasound.com 
EchoNous 
EchoNous Inc 
USA 
Pavlos Moustakidis 
pavlos.moustakidis@echonous.com 
MEDIS 
medis Medizinische Messtechnik GmbH 
Germany 
Susann Balling 
sb@medis.company 
ComfTech 
ComfTech SLR 
Italy 
Lara Alessia Moltani 
alessia.moltani@comftech.com 
TAU 
Faculty of Medicine and Health Technology 
Tampere University, Finland 
Antti Vehkaoja 
antti.vehkaoja@tuni.fi 
LMSU 
Lithuanian University of Health Science 
Lithuania 
Andrius Macas 
andrius.macas@lsmu.lt 
GNP 
Papageorgiou General Hospital 
Greece 
Maria Bigaki 
pmo@papageorgiou-hospital.gr 
CSS-IRCCS 
Home Relief of Suffering Hospital 
Italy 
Elvira Grandone 
e.grandone@operapadrepio.it 
HSV 
Simon Veil Hospital 
France 
Maxime Gautier 
maxime.gautier@ch-simoneveil.fr 
VDE 
Association for Electrical, Electronic & Information 
Technologies, Germany 
Thorsten Prinz 
thorsten.prinz@vde.com 
MEDEA 
MEDEA SRL 
Italy 
Pietro Dionisio 
p.dionisio@medeaproject.eu 
PHAZE 
Clinical Research and Pharma Consulting SA 
Greece 
Spiros Anagnostopoulos 
spiros.anagnostopoulos@phazeclinical.com 
PBY 
PredictBy Research and Consulting SL 
Spain 
Frans Folkvord 
ffolkvord@predictby.com 
SciGen 
SciGen Technologies SA  
Greece 
Katerina Pavlidi 
katerina.Pavlidi@scigentech.com 
 
Disclaimer 
This document contains description of the ThrombUS+ project work, findings, and products. The authors of this document have taken 
any available measure for its content to be accurate, consistent and lawful. However, neither the project consortium as a whole nor 
the individual partners that implicitly or explicitly participated in the creation and publication of this document hold any sort of 
responsibility that might occur as a result of using its content.  
Views and opinions expressed are those of the author(s) only and do not necessarily reflect those of the European Union or HADEA. 
Neither the European Union nor the granting authority HADEA can be held responsible for them.  
In case you believe that this document harms in any way intellectual property held by you as a person or as a representative of an 
entity, please do notify us immediately.  
ThrombUS+ is an Innovation Action Project co-funded by the European Union, under HORIZON-HLTH-2023-TOOL-
05-05 “Harnessing the potential of real-time data analysis and secure Point-of-Care computing for the benefit of 
person-centred health and care delivery”. 
 
 

Study A Protocol 
 
 
 
 
v 1.0 | 28 May 2024 
 
iii 
Contents 
About ThrombUS+ ...................................................................................................................................................... 1 
Terms and Definitions................................................................................................................................................. 2 
ThrombUS_A Clinical Trial Protocol ............................................................................................................................ 3 
1. 
Sponsors’ Protocol Signature Page ..................................................................................................................... 4 
2. 
Principal Investigator’s Protocol Signature Page ................................................................................................ 5 
3. 
Abstract ............................................................................................................................................................. 6 
4. 
Amendments and Updates ................................................................................................................................. 8 
5. 
Milestones ......................................................................................................................................................... 8 
6. 
Rationale and Background ................................................................................................................................. 8 
7. 
Objectives of the Study .....................................................................................................................................10 
8. 
Study Design .....................................................................................................................................................10 
8.1. 
Study Sites ....................................................................................................................................................... 10 
9. 
Study Population ...............................................................................................................................................11 
10. 
Study Visits ...................................................................................................................................................11 
11. 
Study Continuation .......................................................................................................................................12 
12. 
Study Size ......................................................................................................................................................12 
13. 
Data Management ........................................................................................................................................13 
14. 
Data Analysis ................................................................................................................................................14 
15. 
Limitations of the Research Methods ............................................................................................................14 
16. 
Source Documents ........................................................................................................................................14 
17. 
Monitoring ....................................................................................................................................................15 
18. 
Audit and Inspection .....................................................................................................................................15 
19. 
Storage Period of Records .............................................................................................................................15 
20. 
Data Protection .............................................................................................................................................15 
21. 
Completion of the Study ...............................................................................................................................16 
22. 
Protocol Deviations .......................................................................................................................................16 
23. 
Protection of Human Subjects .......................................................................................................................16 
24. 
Study Approval, Patient Information, and Informed Consent........................................................................16 

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iv 
25. 
Safety ............................................................................................................................................................17 
26. 
Publication Policy ..........................................................................................................................................17 
 

D7.1| Study A Submission Pack – Study Protocol 
 
 
 
 
 
v1.0 | 31 May 2024 
 
1 
About ThrombUS+  
Deep vein thrombosis (DVT) is the formation of a blood clot within the deep veins, most commonly those of 
the lower limbs, causing obstruction of blood flow. In 50% of people with DVT, the clot eventually breaks off 
and travels to the lung to cause pulmonary embolism. Clinical assessment of DVT is notoriously unreliable 
because up to 2/3 of DVT episodes are clinically silent and patients are symptom free even when pulmonary 
embolism has developed. Early diagnosis of DVT is crucial and despite the progress made in ultrasound 
imaging and plethysmography techniques, there is a need for new methods to enable continuous monitoring 
DVT diagnosis at the point of care. 
ThrombUS+ brings together an interdisciplinary team of industrial, technology, regulatory, social science and 
clinical trial experts to develop a novel wearable diagnostic device for point-of-care, operator free, 
continuous monitoring in patients with high DVT risk. The device will combine autonomous, AI driven DVT 
detection based on a novel wearable ultrasound hardware, impedance plethysmography and light reflection 
rheography for immediate detection of blood clot formation in the lower limb. Activity and other 
physiological measurements will be used to provide a continuous assessment of DVT risk and support DVT 
prevention via serious gaming. The aggregated data will drive an intelligence decision support unit that will 
provide accurate monitoring and alerts. Extended reality will be used to guide experts to design exercises 
and patients to use the device optimally.  
ThrombUS+ is intended for use by postoperative patients in the ward, during long surgical operations, cancer 
patients or otherwise bedridden patients at home or in care units, and women during pregnancy and 
postpartum. ThrombUS+ will use big data sets for AI model training collected in the project via 3 large scale 
clinical studies and will validate the outcome in the clinical setting via 1 early feasibility study and 1 multi-
centre clinical trial. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

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2 
Terms and Definitions 
Term 
Definition 
AIA 
Artificial Intelligence Act 
ALCOA+  
Attributable, Legible, Contemporaneous, Original, Accurate, and also Complete, 
Consistent, Enduring, and Available 
CDC 
Centres for Disease Control and Prevention 
CO 
Coordinator 
CRO 
Contract Research Organization 
DALY 
Disability-adjusted Life-years 
DICOM 
Digital Imaging and Communications in Medicine 
DVT 
Deep Vein Thrombosis 
eCRF 
Electronic Case Report Form 
EHDS 
European Health Data Space 
EOSC 
European Open Science Cloud 
GCP 
Good Clinical Practice 
GDPR 
General Data Protection Regulation 
GPP 
Good Pharmacoepidemiology Practice 
ICD-11 
International Classification of Diseases 
IEC 
Independent Ethics Committee 
IRB 
Institutional Review Board 
N/A 
Not Applicable 
PE 
Pulmonary Embolism 
SOPs 
Standard Operating Procedures 
ISF 
Investigator Site File 
VTE 
Venous Thromboembolism 
 
 
 

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3 
ThrombUS_A Clinical Trial Protocol 
Title: 
A multi-center cohort study for conventional ultrasound image set collection to create a 
training data set for research purposes (image processing and analysis, AI model training). 
Short title: 
ThrombUS_A 
Protocol version/ date: 
1.0_28may2024 
Sponsor: 
ThrombUS + EU Horizon Project Consortium represented by Consortium Coordinator 
“ATHENA”. 
Supported by: 
EU Horizon Innovation Action Grant Agreement.  
No: 101137227 
Study design: 
Non-Interventional diagnostic image data collection study. 
Countries: 
Finland, France, Greece, Italy, Lithuania. 
Sponsor’s/ consortium’s 
coordinator: 
ThromUS+ Project Coordinator  
Professor Eleni Kaldoudi. 
Professor of Medical Physics and Medical Informatics, School of Medicine, Democritus 
University of Thrace, Greece. 
Adjunct Researcher, ATHENA Research and Innovation Centre in Information, 
Communication and Knowledge Technologies, Greece. 
 
 
This document contains confidential information belonging to ThrombUS+ Consortium. This document may not – in 
full or in part – be passed on, reproduced, published or otherwise used without prior written permission. In the event 
of any actual or suspected breach of this obligation, ThrombUS+ Consortium must be promptly notified via the 
Coordinator, Prof. E. Kaldoudi, kaldoudi@athenarc.gr 
ThrombUS+ is an Innovation Action Project co-funded by the European Union, under HORIZON-HLTH-
2023-TOOL-05-05 “Harnessing the potential of real-time data analysis and secure Point-of-Care 
computing for the benefit of person-centred health and care delivery”. 
Views and opinions expressed are those of the author(s) only and do not necessarily reflect those of 
the European Union or HADEA. Neither the European Union nor the granting authority HADEA can be held responsible 
for them. 
 
 
 
 
 
 
 
 
 
 
 
 
 

Study A Protocol 
 
 
 
 
v 1.0 | 28 May 2024 
 
4 
 
1. 
Sponsors’ Protocol Signature Page 
 
 
Protocol Version 1.0 28 May 2024 
 
I herewith certify study’s design. 
 
Sponsor’s responsibility for the study name: Professor Eleni Kaldoudi, ThrombUS+ Project Coordinator 
 
 
Signature: 
 
 
 
 
Date: 28 May 2024 
 
 
 
 

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5 
2. 
Principal Investigator’s Protocol Signature Page  
 
 
 
Signature of the principal investigator at the study site 
 
Protocol Version 1.0, 28 May 2024 
 
The signatory agrees to the content of the final clinical study protocol as presented. 
 
Principal Investigator Name: 
 
Site number: 
 
Signature: 
 
 
Date: 
 
 
 
 

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6 
3. 
Abstract  
Title: 
A multi-center cohort study for conventional ultrasound image set collection to create a 
training data set for research purposes (image processing and analysis, AI model training). 
Short title: 
ThrombUS_A 
Protocol version/ date: 
1.0 31 May 2024  
Sponsor: 
ThrombUS+ EU Horizon Project Consortium represented by Consortium Coordinating 
Partner “ATHENA”. 
Grant Agreement Nr. 
(Funded by EU) 
EU Horizon Innovation Action Grant Agreement.  
No: 101137227 
Rationale and 
background: 
Deep vein thrombosis (DVT) and its fatal complication pulmonary embolism (PE) afflict 
millions of people worldwide and are responsible for a large percentage of acute 
hospitalizations. 
Early diagnosis of DVT is crucial and has been proven to prevent life-threatening 
complications (pulmonary embolism), minimize the risk of long-term disability (post-
thrombotic syndrome, recurrent DVT), improve treatment outcomes, and reduce 
healthcare costs. Despite the progress made in ultrasound imaging, impedance 
plethysmography and light reflection rheography techniques, there is a need for new 
methods to enable continuous monitoring DVT diagnosis in hospitalized and other high-risk 
patients at the point of care. 
ThrombUS+ EU Horizon project brings together an interdisciplinary team of industrial, 
technology, regulatory, social science and clinical trial experts to develop a novel wearable 
device for operator free, continuous monitoring in patients with high DVT risk. The device 
and software to be developed during this project are expected to achieve automated early 
DVT detection, provide a continuous assessment of DVT risk and support DVT prevention 
via extended reality and serious gaming. ThrombUS+ wearable is intended for use by 
postoperative patients in the ward, during long surgical operations, cancer patients or 
otherwise bedridden patients at home or in care units, and women during pregnancy and 
postpartum. ThrombUS+ will use big data sets for artificial intelligence (AI) model training 
collected in the project via 3 large scale clinical studies and will validate the outcome in the 
clinical setting via 1 early feasibility study and 1 multi-centre clinical trial. 
Aim: 
This study aims to collect and create a labelled ultrasound image data set containing 
ultrasound image series and video clips of patients that undergo routine ultrasound scans 
on lower limbs, because of suspected deep vein thrombosis.  
The data will be used to train an AI model within ThrombUS+ project to achieve automated 
detection of deep vein thrombosis on conventional ultrasound scans. 
Objectives: 
Primary objectives:  
1. Collect and curate imaging data from ultrasound scans of patients suspected for DVT. 
2. Collect accompanying metadata on patient demographics, referral note, existing known 
medical conditions at the time of scan, diagnosis based on the scan, operator 
anonymized ID, metadata on the ultrasound equipment used. 
3. Anonymize the data set according to established regulations to be used for research 
purposes and in specific for training an artificial intelligence model to achieve 
automated DVT detection. 
Secondary objectives: 
1. 
Describe the data set in the Argos/OpenAIRE tool and make it publicly available through 
the European Open Science Cloud (EOSC) portal via OpenAIRE, to be used by other 
researchers for image processing, analysis, and AI model training. 

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7 
Study design: 
This is a one visit, non-interventional, multicentre, diagnostic image data collection study. 
The study refers to data collection from conventional, routinely performed diagnostic 
ultrasound imaging. 
Population: 
Patients with suspected DVT referred for a DVT ultrasound scan will be consecutively 
selected to account for demographics, medical condition and ultrasound operator diversity 
in the sample; data selected will be anonymized and included in the data set. 
In and out-patients referred for an ultrasound scan for suspected DVT will be asked to 
participate (informed consent process). 
Inclusion/Exclusion 
Criteria: 
This study will include patients aged ≥18 years who are referred for a standard ultrasound 
scan to see if they have a DVT. 
For a patient to be considered eligible for participation in this study all the following 
inclusion criteria must be met: 
1. 
Age ≥18 years. 
2. 
The participant has the capacity to consent, and consent is obtained prior to any study-
specific procedures. 
3. 
The conventional diagnostic DVT algorithm indicates that an ultrasound is needed, or 
the patient has been referred for a scan on suspicion of DVT.  
A patient who meets any of the following exclusion criteria will not be permitted to 
participate in this study: 
1. 
With a known condition or reason that may potentially result in interrupting or stopping 
the ultrasound examination before its completion. 
2. 
Patients considered by their treating physician or the ultrasound operator as non-
suitable for a standard ultrasound scan. 
3. 
Patients who have not signed the informed consent. 
Study size: 
A minimum of 3000 patients. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

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8 
4. 
Amendments and Updates 
None 
5. 
Milestones  
Table 1. A summary of milestones regarding the ThrombUS+ Clinical Study A 
Milestone 
Planned date 
IRB/IEC approval 
June 2024 
Start of data collection 
July 2024 
Midterm recruitment report 
December 2024 
End of data collection 
June 2025 
Final report of study results 
July 2025 
 
6. 
Rationale and Background  
Deep vein thrombosis (DVT) and its fatal complication pulmonary embolism (PE) afflict millions of people 
worldwide and are responsible for a large percentage of acute hospitalizations. Clinical assessment of DVT is 
notoriously unreliable because up to 2/3 of DVT episodes are clinically silent and patients are symptom free 
even when PE has developed. Symptomatic DVT events, that are eventually referred for ultrasound imaging 
are only the tip of the DVT iceberg1. The subgroup of events that evolve to develop clinical indications cannot 
be accurately predicted and often lead to sudden death from PE regarded as “the leading cause of 
preventable death in hospitalized patients” and “the number one priority for improving patient safety in 
hospitals”2. 
Deep vein thrombosis (DVT) is the formation of a blood clot within the deep veins, most commonly those of 
the lower limbs, causing obstruction of blood flow. In 50% of people with DVT, the clot is at some point 
detached from the vein wall and travels to the lung to cause pulmonary embolism. About 25% of people 
experiencing pulmonary embolism (PE) will die from it, making it the 3rd leading cause of cardiovascular death 
worldwide after stroke and heart attack3. Even in patients who do not get PE, recurrent thrombosis and “post-
thrombotic syndrome” are major causes of mortality and reduced quality of life4. 
Recent European population studies report DVT incidence of 70-140 cases/100,000 person-year5, which 
translates to roughly 522,000 to 1.04 million cases per year in Europe. Respectively, Center for Disease 
 
1  Sharif-Kashani B, Behzadnia N, Shahabi P, Sadr M. Screening for deep vein thrombosis in asymptomatic high-risk 
patients: a comparison between digital photoplethysmography and venous ultrasonography. Angiology. 2009 Jun-
Jul;60(3):301-7. https://doi.org/10.1177/0003319708323494 
2  Agency for Healthcare Research and Quality. Preventing Hospital Associate Venous Thromboembolism. A Guide for 
Effective 
Quality 
Improvement, 
Last 
Review 
Feb 
2924. 
https://www.ahrq.gov/patient-
safety/settings/hospital/vtguide/index.html 
3  Waheed SM, Kudaravalli P, Hotwagner DT. Deep Vein Thrombosis. [Updated 2023 Jan 19]. In: StatPearls. Treasure 
Island (FL): StatPearls Publishing; 2024 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK507708/  
4  Nicholson M, Chan N, Bhagirath V, Ginsberg J. Prevention of Venous Thromboembolism in 2020 and Beyond. J Clin 
Med. 2020 Aug 1;9(8):2467. https://doi.org/10.3390/jcm9082467 
5  ISTH Steering Committee for World Thrombosis Day. Thrombosis: a major contributor to the global disease burden. 
J Thromb Haemost. 2014 Oct;12(10):1580-90. https://doi.org/10.1111/jth.12698 

Study A Protocol 
 
 
 
 
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9 
Control and Prevention (CDC) reports around 900,000 DVT incidents per year in USA6, with an estimate of 
60,000-100,000 related deaths per year. Venous thromboembolism (that collectively defines DVT and/or PE) 
during hospitalization is the leading cause of disability-adjusted life-years (DALYs) lost in low- and middle-
income countries, and the second most common cause in high-income countries, causing loss of more DALYs 
than nosocomial pneumonia, catheter-related bloodstream infections, and adverse drug events7. No 
identifiable provoking risk factor is reported in about 25%-40% of DVT and pulmonary embolism incidents8. 
Surgery is reported to account for 15% of the cases and especially orthopaedic surgery with postoperative 
rates of around 1% reported despite pharmacological thromboprophylaxis9; immobilization is reported to 
account for 15% and cancer for about 20% of cases.  
Early diagnosis of DVT is crucial and has been proven to prevent life-threatening complications (pulmonary 
embolism), minimize the risk of long-term disability (post-thrombotic syndrome, recurrent DVT), improve 
treatment outcomes, and reduce healthcare costs. Despite the progress made in ultrasound imaging and 
plethysmography techniques, there is a need for new methods to enable continuous monitoring DVT 
diagnosis in hospitalized and other high-risk patients at the point of care. 
ThrombUS+ EU Horizon project brings together an interdisciplinary team of industrial, technology, 
regulatory, social science and clinical trial experts to develop a novel wearable device for operator free, 
continuous monitoring in patients with high DVT risk. The devices and software to be developed during this 
project are expected to achieve automated early DVT detection, provide a continuous assessment of DVT risk 
and support DVT prevention via extended reality and serious gaming. ThrombUS+ is intended for use by 
postoperative patients in the ward, during long surgical operations, cancer patients or otherwise bedridden 
patients at home or in care units, and women during pregnancy and postpartum. ThrombUS+ will use big 
data sets for artificial intelligence (AI) training collected in the project via 3 large scale clinical studies and will 
validate the outcome in the clinical setting via 1 early feasibility study and 1 multi-centre clinical trial. 
This study aims to collect and create a labelled ultrasound image data set containing ultrasound images series 
of patients that undergo routine ultrasound scans on lower limbs, because of suspected deep vein 
thrombosis. 
The data will be used to train AI models within ThrombUS+ project to achieve automated detection of deep 
vein thrombosis on conventional ultrasound scans. 
The data set will include negative scans, positive for deep vein thrombosis scans, positive for other diagnosis 
scans and scans of insufficient quality to aid towards diagnosis, together with imaging metadata and a set of 
labels for each scan. In addition, the dataset will include pseudonymized patient demographics, referral note, 
existing known medical conditions at the time of scan, diagnosis based on the scan, operator anonymized ID, 
and metadata on the ultrasound equipment and scanning protocol parameters. 
The data set will be completely anonymized to be used for research purposes, in compliance with the General 
Data Protection Regulation (GDPR) and the European Health Data Space (EHDS) and the upcoming Artificial 
Intelligence Act (AIA). Furthermore, the anonymized data set will be described in the Argos/OpenAIRE tool 
and will be made available through the European Open Science Cloud (EOSC) portal via OpenAIRE, to be used 
by other researchers for image processing, analysis, and AI model training. This is a non-interventional 
diagnostic image data collection study. 
 
6  CDC, Data and Statistics on Venous Thromboembolism, 2023, https://www.cdc.gov/ncbddd/dvt/data.html 
7  ISTH Steering Committee for World Thrombosis Day. Thrombosis: a major contributor to the global disease burden. 
J Thromb Haemost. 2014 Oct;12(10):1580-90. https://doi.org/10.1111/jth.12698 
8  Heit JA, Spencer FA, White RH. The epidemiology of venous thromboembolism. J Thromb Thrombolysis. 2016 
Jan;41(1):3-14. https://doi.org/10.1007/s11239-015-1311-6 
9  Di Nisio M, van Es N, Büller HR. Deep vein thrombosis and pulmonary embolism. Lancet. 2016 Dec 
17;388(10063):3060-3073. https://doi.org/10.1016/S0140-6736(16)30514-1 

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10 
To our knowledge there is no such data sets (images and labels) publicly and readily available for research 
purposes, and especially for the purpose of training an AI model to achieve automated DVT detection. There 
are clinical studies that include ultrasound scanning of patients suspected for DVT, but none with primary or 
secondary objective to create the dataset planned in this study. 
7. 
Objectives of the Study 
Primary objectives:  
1. Collect and curate imaging data from ultrasound scans of patients suspected for DVT. 
2. Collect accompanying metadata on patient demographics, referral note, existing known medical 
conditions at the time of scan, diagnosis based on the scan, operator anonymized ID, metadata on the 
ultrasound equipment used. 
3. Anonymize the data set according to established regulations to be used for research purposes and 
specifically for training an artificial intelligence model to achieve automated DVT detection. 
Secondary objectives: 
1. Describe the data set in the Argos/OpenAIRE tool and make it publicly available through the European 
Open Science Cloud (EOSC) portal via OpenAIRE, to be used by other researchers for image processing, 
analysis, and AI model training. 
8. 
Study Design 
This is a one visit, non-interventional, multi-centre, diagnostic image data collection study. The study refers 
to data collection from conventional, routinely performed diagnostic ultrasound imaging. Patients with 
suspected DVT referred for a DVT ultrasound scan will be consecutively selected to account for 
demographics, medical condition and ultrasound operator diversity in the sample; data selected will be 
anonymized and included in the data set. 
In and out-patients referred for an ultrasound scan for suspected DVT will be asked to participate (informed 
consent process).  
8.1. Study Sites 
Participating sites are partners in the ThrombUS+ consortium, to be funded by the project to undertake the 
study (Table 2).  
Table 2. Hospitals participating in the ThrombUS+ Clinical Study A. 
Hospital 
TAU 
LSMU 
GNP 
CSS-IRCCS 
HSV 
Total 
Country 
Finland 
Lithuania 
Greece 
Italy 
France 
5 countries 
Number of beds 
1,000 
2,232 
745 
750 
976 
5,703 
Hospitalizations per year 
82,897 
83,192 
94,205 
45,000 
30,078 
335,372 
Out-patients per year 
1,162,227 
1,228,783 
244,227 
300,000 
236,164 
3,171,401 
Patients scanned for suspected 
DVT per year 
3,500 
4,000 
939 
2,400 
350 
11,189 
 

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11 
These partners were selected on the merits of research interest in the proposed smart wearable technology, 
their research background on respective fields and their strong collaboration links with technical partners of 
the consortium which ensures their willingness to participate and carry out the proposed clinical study.  
During the study, additional sites may be included to enhance recruitment progress without necessitating 
protocol amendments. The definitive list of participating sites will be documented in the Study Report. 
9. 
Study Population 
This study will include patients aged ≥18 years who are referred for a standard ultrasound scan to see if 
they have a DVT. 
For a patient to be considered eligible for participation in this study all the following inclusion criteria must 
be met: 
1. age ≥18 years, 
2. the participant has the capacity to consent and consent is obtained prior to any study-specific 
procedures, 
3. the conventional diagnostic DVT algorithm indicates that an ultrasound is needed, or the patient has 
been referred for a scan on suspicion of DVT.  
A patient who meets any of the following exclusion criteria will not be permitted to participate in this study: 
1. patients with a known condition or reason that may potentially result in interrupting or stopping the 
ultrasound examination before its completion, 
2. patients considered by their treating physician or the ultrasound operator as non-suitable for a standard 
ultrasound scan, 
3. patients who have not signed the informed consent. 
10. Study Visits 
The enrolment period for this study will commence once the approval of the appropriate Ethics Board has 
been granted and will continue till the required number of patients is reached, approximately for one year. 
After obtaining the written informed consent of participants, the investigators will collect data about: 
− 
demographics (age, sex, race), 
− 
body composition (height, weight, BMI, thigh/waist circumference, lower limb dimensions), 
− 
medical history that may be risk factors for DVT, 
− 
result of DVT ultrasound scan. 
During the study, each participant referred for a conventional DVT ultrasound scan will undergo the scan as 
planned and following the routine clinical procedure. Specific guidelines outlining the required images will 
be provided to the radiologist in the "ThrombUS+ Clinical Study A Manual for DVT ultrasound scan”. Both the 
radiologist and any other personnel conducting the ultrasound will be trained on the manual and the study's 
protocol prior to patient recruitment and scanning.  
The data and metadata will be collected for the study data set (Table 3). It is expected that the patient will be 
involved in the informed consent recruitment phase for about 30 minutes in addition to the allocated typical 
time for the conventional DVT scan as initially planned. 
 

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12 
Table 3. Flowchart 
Activity 
Visit  
Informed consent 
 
Inclusion/Exclusion criteria 
 
Patient demographics (age, sex, race) 
 
Body Composition (Height, Weight, BMI, 
Thigh/Waist circumference, lower limb dimensions). 
 
Medical history (related to DVT risk factors) 
 
DVT ultrasound scan 
 
Result of DVT ultrasound scan 
 
 
11. Study Continuation  
Sponsor reserves the right to discontinue the study overall or at a particular study site at any time for the 
following reasons: 
− 
Discontinuation or alteration of the ThrombUS+ project workplan that results in the corresponding 
discontinuation or alteration of this study.  
− 
Failure to meet expected enrolment goals overall or at a particular study site. 
− 
The emergence of any effectiveness/safety information that could significantly affect the continuation of 
the study. 
− 
Violation of the study protocol, disturbing the appropriate conduct of the study. 
The sponsor will be responsible for informing IRBs/IECs of the early termination of the study. 
12. Study Size 
A minimum of 3000 scanned patients. Interim data sets can be used progressively to start training of the AI 
model. Data will be de-identified and processed for experts’ annotation. Data will be partitioned based on 
stratified random sampling to ensure a similar distribution of measurements of vascular biomarkers in 
training and test sets. 
Sampe size calculation for artificial intelligence training is rather complex and not straightforward. A recent 
review on AI in medical imaging presents methods to assess the effect of sample size on model performance 
(e.g. NxSubsampling, NxRepeated Cross-Validation, No Repetition), and there are also model based and 
learning curve fitting approaches to determine the sample size for AI training regarding applications in 
medical imaging10. However, many researchers simply follow the Widrow-Hoff learning rule that suggests ten 
data (patients) for every imaging feature that will be used in the model11. For ultrasound-based diagnosis and 
DVT and vessel recognition and diagnosis, studies have used proprietary datasets ranging from a few hundred 
 
10  Balki I, Amirabadi A, Levman J, et al. Sample-Size Determination Methodologies for Machine Learning in Medical 
Imaging Research: A Systematic Review. Canadian Association of Radiologists Journal. 2019;70(4):344-353. 
https://doi.org/10.1016/j.carj.2019.06.002  
11  Castiglioni I, Rundo L, Codari M, Di Leo G, Salvatore C, Interlenghi M, Gallivanone F, Cozzi A, D'Amico NC, Sardanelli 
F. AI applications to medical images: From machine learning to deep learning. Phys Med. 2021 Mar;83:9-24. 
https://doi.org/10.1016/j.ejmp.2021.02.006  

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to several thousand images12,13,14,15. ThrombUS+ AI models will progressively be trained during the course of 
the study to determine the optimal size of the training dataset.  
All clinical partners are large hospitals routinely involved in various types of clinical trials. Evidence that 
supports their ability to recruit the required number of study participants is the current average number of 
screened patients for suspected DVT which is shown in the table above.  
Based on recent numerical data (Table 4), the participating sites have collectively the following: 
Table 4. Collective numerical data for the ThrombUS_A study.  
Total number of patients referred for an ultrasound scan for suspected DVT per year 
11,189  
Positive DVT scans per year 
2,547  
Positive DVT scans as a percentage of referred and scanned patients per year 
22%  
Duration of the study (in months) 
12 
Estimated patients available for recruitment 
11,000 
Expected patients to recruit 
3,000  
Expected patients to recruit as a percentage for available for recruitment 
27%  
Expected positive scans in the recruited population (20%) 
660  
 
13. Data Management 
Data management will be coordinated by the data managers of the Sponsor. 
Image data will be collected in the DICOM format and collected via DICOM protocol. Additional metadata will 
comply with appropriate medical standards, e.g., medical conditions and diagnosis will be based on ICD-
10/11 codes. Anonymization and security will be performed by technology and practices of the security 
expert ATHENA and follow all appropriate regulations: in particular, GDPR, and the ones currently under 
consultation: European Health Data Space and AIA. 
The Technical Coordinator of the study (ATHENA) will provide on-site software for image data anonymization 
and a secure, on-line platform for image and accompanying data collection. 
Patient-related data from medical records will be collected through the eCRF system. The use of eCRF system 
offers improved data quality, online discrepancy management, multicentre management, faster database 
lock etc. Main objectives behind eCRF development are preserving and maintaining quality and integrity of 
 
12  Nakayama Y, Sato M, Okamoto M, Kondo Y, Tamura M, Minagawa Y, Uchiyama M, Horii Y. Deep learning-based 
classification of adequate sonographic images for self-diagnosing deep vein thrombosis. PLoS One. 2023 Mar 
6;18(3):e0282747. https://doi.org/10.1371/journal.pone.0282747 
13  Kainz B, Heinrich MP, Makropoulos A, Oppenheimer J, Mandegaran R, Sankar S, Deane C, Mischkewitz S, Al-Noor F, 
Rawdin AC, Ruttloff A, Stevenson MD, Klein-Weigel P, Curry N. Non-invasive diagnosis of deep vein thrombosis from 
ultrasound imaging with machine learning. NPJ Digit Med. 2021 Sep 15;4(1):137. https://doi.org/10.1038/s41746-
021-00503-7 
14  Mittmann BJ, Braun M, Runck F, Schmitz B, Tran TN, Yamlahi A, Maier-Hein L, Franz AM. Deep learning-based 
classification of DSA image sequences of patients with acute ischemic stroke. Int J Comput Assist Radiol Surg. 2022 
Sep;17(9):1633-1641. https://doi.org/10.1007/s11548-022-02654-8  
15  Du T, Xie L, Zhang H, Liu X, Wang X, Chen D, Xu Y, Sun Z, Zhou W, Song L, Guan C, Lansky AJ, Xu B. Training and 
validation of a deep learning architecture for the automatic analysis of coronary angiography. EuroIntervention. 
2021 May 17;17(1):32-40. https://doi.org/10.4244/EIJ-D-20-00570  

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data. The collected data will be pseudonymized with the key file to be kept secured to each clinical site 
according to each site’s Standard Operational Procedures (SOPs).  
14. Data Analysis 
The study involves data collection from routine diagnostic imaging to be used for research purposes.  
The data will be de-identified and processed for experts’ annotation. Anonymized imaging data will be 
labelled via professional labelling tools.  
Data will be partitioned based on stratified random sampling to ensure a similar distribution of 
measurements of vascular biomarkers in training and test sets for the AI model training procedure.  
FAIR principles will be applied for the preparation of the dataset to be published in EOSC as open science 
data set. 
Statistical analysis is not applicable.  
15. Limitations of the Research Methods  
Limitations to the design of this study mainly involve patient selection bias, information bias, potentially 
missing data (including loss of data during transfer from paper to electronic sources), and selection bias 
related to including only patients referred to the hospital department participating in the study (and not 
other hospital departments potentially deploying DVT ultrasound scanning). To control and mitigate patient 
selection bias, the participating physicians will be requested to consecutively and non-selectively enrol all 
patients attending their clinic if they meet the eligibility criteria of the study. Moreover, the decision-making 
of each investigator to perform an ultrasound scan for suspected DVT will be based on the current medical 
practice and precede the consideration of the patient’s eligibility for study enrolment, while a screening log 
of all potentially eligible patients will be kept at each site. 
16. Source Documents 
Source documents provide evidence of patient participation and substantiate the integrity of the data 
collected. Source documents are filed at the investigator’s site. 
Data entered in the eCRFs that are transcribed from source documents must be consistent with the source 
documents or the discrepancies must be explained.  
Source data as well as reported data should follow the “ALCOA+ principles” to be Attributable, Legible, 
Contemporaneous, Original, Accurate, and be Complete, Consistent, Enduring, and Available. Changes to the 
data should be traceable (by audit trail).  
Data reported on the eCRF must be consistent with the source data or the discrepancies must be clearly 
documented and justifiable.  
Due to the characteristics of this study and the nature of the data collected it is possible for the eCRF to be 
considered source for certain types of data in some of the participating sites. In this case care must be taken 
by the Investigator to ensure that information important for patient management is transferred from the 
eCRF to the patient’s medical records/case notes. Such information is vital for patient management when 
patients are managed outside the trail team by different medical specialties.  
When the information is transferred from the eCRF to the medical records, Investigator also needs to clearly 
state which is the original source of the information (i.e) the CRF. 
 

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17. Monitoring  
Monitoring is defined as the act of overseeing the progress of a clinical study, and of ensuring that it is 
conducted, recorded, and reported in accordance with the protocol, applicable Standard Operating 
Procedures (SOPs), and the principles of GCP. 
The sponsor in collaboration with the CRO will develop a systematic, prioritized, risk-based approach to 
monitor this clinical study.  
Each site will appoint their own Principal Investigator to oversee and coordinate the local study and confer 
respectively with CRO and Sponsor CO. 
The risks to clinical study processes & clinical study data will be evaluated at both the system level (e.g., 
Standard Operating Procedures (SOPs), computerized systems, personnel) and clinical study level (e.g., study 
design, data collection, informed consent process) against existing risk controls by considering: 
1. The likelihood of errors occurring. 
2. The extent to which such errors would be detectable. 
3. The impact of such errors on human subject protection and reliability of study results. 
The sponsor will document the rationale for the resulting monitoring strategy in applicable manuals (e.g., in 
the Monitoring Manual)”. 
18. Audit and Inspection 
The investigator/institution will allow site study-related monitoring, audits, IRB/IEC review and regulatory 
inspections. Direct access must be provided to the eCRF and all source documents/data, including progress 
notes, copies of laboratory and medical test results, which must always be available for review by the 
monitor, auditor, and regulatory inspector. The accuracy of the data will be verified by direct comparison 
with the source documents described above. The sponsor/CRO will also monitor compliance with the 
protocol and GCP. The investigator should notify the sponsor/CRO immediately of any such inspection. Audits 
and inspections may occur at any time during or after completion of the study. 
19. Storage Period of Records 
The Investigator Site File (ISF), which includes records and documents, including signed ICFs, pertaining to 
the conduct of this study must be retained by the investigator for 25 years after study completion in 
accordance with Article 58 of the Clinical Trial Regulation, unless local regulations or institutional policies 
require a longer retention period. No records may be destroyed during the retention period without the 
written approval of the sponsor/CRO. No records may be transferred to another location or party without 
written notification to the sponsor/CRO. However, the participants’ medical records at the study site shall be 
archived in accordance with national law with which the sponsor is responsible to familiarize itself with.  
20. Data Protection 
Data protection and data security measures are implemented for the collection, storage, and processing of 
participant data in accordance with EU regulation 2016/679 GDPR.  
To ensure confidentiality of records and personal data, only pseudonymized data will be transferred to the 
sponsor, following state-of-the-art pseudonymization techniques to make them globally anonymous and 
using an artificial identifier (pseudonym) for each participant instead of the participant’s name (or any other 
personally identifiable information). The real identity of participant is only available at the site and must not 
be forwarded to the sponsor/CRO. Access to the participant files and clinical data is strictly limited; 
personalized treatment data may be given to the participant’s personal physician or to other appropriate 

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medical personnel responsible for the participant’s welfare irrespective of this study and according to 
conventional practise of each site. Data generated at the site because of the study need to be available for 
inspection on request by the participating physicians, the sponsor’s representatives, by the IRB/IEC, and the 
regulatory authorities.  
A potential data security breach will be assessed regarding the implications for the rights and privacy of the 
affected person(s). Immediate actions as well as corrective and preventive actions will be implemented. 
Respective regulatory authorities, IRBs/IECs, and participants will be notified as appropriate under Articles 
33 and 34 of the GDPR.  
Moreover, all information required to be given to data subjects in accordance with Article 13 of the GDPR 
shall be provided through the Informed Consent Form (ICF). 
21. Completion of the Study 
The Institutional Review Board (Scientific/Administrative Board, IRB) of each participating hospital centre 
needs to be notified about the end of the study (last patient out) or early termination of the study unless it 
is differently required by the national regulations that govern the conduct of such studies in case these 
regulations have been amended until the completion of the study. 
22. Protocol Deviations  
No waivers to inclusion/exclusion criteria will be granted; participants need to meet all criteria, exactly as 
specified, to be enrolled. Deviations that occur unintentionally or are the result of action by the participant 
must be documented and reported to the competent authorities, if applicable, according to regulations. 
Further details about the documentation, evaluation, and follow-up of protocol deviations are detailed in 
this study’s clinical monitoring plan. 
23. Protection of Human Subjects  
The study will be carried out in compliance with the protocol, the principles laid down in the Declaration of 
Helsinki, Guidelines for Good Pharmacoepidemiology Practice (GPP), and the relevant Standard Operating 
Procedures (SOPs). Standard medical care remains the responsibility of the treating physician of the patient. 
The investigator should inform the sponsor immediately of any urgent safety measures taken to protect the 
study subjects against any immediate hazard, and of any serious breaches of the protocol. 
24. Study Approval, Patient Information, and Informed Consent 
This study will be initiated only after all required legal documentation has been reviewed and approved by 
the respective IRB/IEC according to national and international regulations. The same applies to the 
implementation of changes introduced by amendments. 
Prior to patient participation in the study, written informed consent must be obtained from each patient per 
GPP and according to the regulatory and legal requirements of the participating country. Each signature must 
be personally dated by each signatory and the informed consent, and any additional patient information form 
retained by the Investigator as part of the study records. A signed copy of the informed consent and any 
additional patient information must be given to each patient. Re-consenting may become necessary when 
new relevant information becomes available that could influence the decision for participating in the study 
and should be conducted according to the sponsor’s/CRO’s instructions and timelines. The consent and re-
consenting process should be properly documented in the source documentation. 
 

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25. Safety  
Not applicable for this study.  
26. Publication Policy  
To maintain the scientific integrity of the study, data will not be released outside the consortium prior to the 
first publication of the analysis of the primary endpoints, without the permission of the ThrombUS+ 
Consortium. In addition, individual collaborators must not publish data concerning their participants which 
is directly relevant to the objectives of this study until the first publication of the analysis of the primary 
endpoints. 
 
 

ThrombUS + EU Horizon Project Consortium 
ThrombUS+ Study A 
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