Benefit of Machine Learning to Diagnose Deep Vein Thrombosis Compared to Gold Standard Ultrasound
Conditions: Deep Venous Thrombosis of Leg, Deep Vein Thrombosis
Sponsor: Imperial College London

 
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Template Protocol for non-CTIMPs 
ML in medical US 
 
 
 
 
Benefit of Machine learning to diagnose Deep vein Thrombosis 
compared to gold standard Ultrasound 
 
 
 
 
Version 1, 12.01.2022 
 
 
 
 
MAIN SPONSOR: Imperial College London  
FUNDERS: No funding required 
STUDY COORDINATION CENTRE: Vascular Lab, D4 Hammersmith Hospital 
 
IRAS Project ID: 313321 
REC reference: 22HH7485 
 
 
 
Protocol authorised by: 
 
Name & Role 
Date 
Signature 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
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Study Management Group 
 
Chief Investigator: Dr. M. Aslam 
 
Statistician: Sara Fontanella (s.fontanella@imperial.ac.uk) 
 
Study Management: Miss Kerstin Saupe 
 
Study Coordination Centre  
 
For general queries, supply of study documentation, and collection of data, please contact: 
 
Study Coordinator:  
Miss Kerstin Saupe 
Vascular Lab D4 
72 Du Cane Rd. 
Hammersmith Hospital 
 
W12 0HS 
 
 
 
Tel: 02033131541  
 
Email: ks1221@ic.ac.uk 
 
Clinical Queries 
 
Clinical queries should be directed to the study coordinator, Kerstin Saupe who will direct the 
query to the appropriate person. 
 
Sponsor 
 
Imperial College London is the main research sponsor for this study. For further information 
regarding the sponsorship conditions, please contact the Head of Regulatory Compliance at: 
 
 
 
Research Governance and Integrity Team 
Imperial College London and Imperial College Healthcare NHS Trust 
Room 215, Level 2, Medical School Building 
Norfolk Place 
London, W2 1PG 
Tel: 0207 594 1862 
Imperial College - Research Governance and Integrity Team (RGIT) Website 
 

 
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Funder 
No funding is required for this study. 
 
This protocol describes the benefit of machine learning to diagnose Deep vein Thrombosis 
(DVT) compared to gold standard Ultrasound diagnostic and provides information about 
procedures for entering participants.  Every care was taken in its drafting, but corrections or 
amendments may be necessary. These will be circulated to investigators in the study.  
Problems relating to this study should be referred, in the first instance, to study coordinator, 
Miss Kerstin Saupe or to the chief investigator, Dr. M.Aslam.  
 
This study will adhere to the principles outlined in the UK Policy Framework for Health and 
Social Care Research. It will be conducted in compliance with the protocol, the Data Protection 
Act and other regulatory requirements as appropriate.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
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Table of Contents                                                                  Page No 
 
GLOSSARY OF ABBREVIATIONS  
 
 
 
 
 
5 
KEYWORDS 
 
 
 
 
 
 
 
 
 
6 
STUDY SUMMARY 
 
 
 
 
 
 
 
 
6 
DESIGN 
 
 
 
 
 
 
 
 
 
 
6 
AIM  
 
 
 
 
 
 
 
 
 
 
6 
OUTCOME MEASURES 
 
 
 
 
 
 
 
7 
REFERENCE DIAGRAM 
 
 
 
 
 
 
 
8 
INTRODUCTION 
 
 
 
 
 
 
 
 
9 
STUDY OBJECTIVES  
 
 
 
 
 
 
        10 
STUDY DESIGN 
 
 
 
 
 
 
 
        10 
PARTICIPANT ENTRY 
 
 
 
 
 
 
        11 
ADVERSE EVENTS   
 
 
 
 
 
 
        11 
ASSESSMENT AND FOLLOW-UP  
 
 
 
 
        13 
STATISTICS AND DATA ANALYSIS  
 
 
 
        13 
REGULATORY ISSUES  
 
 
 
 
 
 
        13 
STUDY MANAGEMENT  
 
 
 
 
 
 
        14 
PUBLICATION POLICY 
 
 
 
 
 
 
        14 
REFERENCES  
 
 
 
 
 
 
 
        14 
 
Appendix   
 
 
 
 
 
 
 
 
         
1. Summary of investigations, treatment and assessment      15 
2. CE certificate 
 
 
 
 
 
 
 
 
16 
3. Reference list  
 
 
 
 
 
 
 
17 
4. Participant information sheet 1  
 
 
 
 
 
5. Participant information sheet 2 
 
 
 
 
 
 
 
 
 
 
 

 
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GLOSSARY OF ABBREVIATIONS 
 
AutoDVT 
Automatic deep vein thrombosis software 
DVT 
Deep vein thrombosis 
GP  
General Practitioner 
IRAS 
Integrated research Application system 
NHS 
National Health System 
PI 
Principal Investigator 
US 
Ultrasound 
USS 
Ultrasound Scan 
VTE 
Venous Thromboembolism 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
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KEYWORDS 
 
                               AutoDVT software, non-specialist, DVT, USS, gold standard, benefit 
 
STUDY SUMMARY 
 
TITLE 
Benefit of Machine learning to diagnose Deep vein Thrombosis 
compared to gold standard Ultrasound 
 
DESIGN This study aims to compare gold standard DVT diagnostic performed by a specialist 
sonographer to a scan by a non-specialist with a newly developed AutoDVT detection software 
device. Currently the process from the DVT symptom begin, to diagnosis and then treatment is 
all but not straight forward. It implements a laborious journey for the patient from their GP to A 
& E, then to a specialist sonographer. However, handheld Ultrasound devices have recently 
become available. The start-up company ThinkSono developed a software, which is hoped to 
divide between thrombosis and no thrombosis. In this single blinded pilot study, patients which 
present at St. Mary’s DVT Clinic, The Bays will be scanned by the specialist with the gold 
standard Duplex Sonography and after this by a non-specialist with the machine learning 
supported device. The accuracy of the device will be compared to the gold standard. This would 
mean that in future DVT could be diagnosed at point of care by a non-specialist such as a 
community nurse or nursing home nurse, where patients are multi morbid and limited in their 
mobility. The technology could reduce emergency department presentations and free up 
specialist sonographer to focus on other clinical tasks. These improvements could reduce the 
financial burden of DVT diagnostic services for the NHS. 
 
AIMS “AutoDVT” is a software system designed to assist non-specialist operators, such as nurses, 
general practitioners (GP) and other allied health professionals in the diagnosis of DVT. The 
software utilises a “machine learning” algorithm to assist non-specialists in the diagnosis of DVT 
as described below. Venous thrombosis commonly occurs in the deep leg veins as well as the 
deep veins of the pelvis, and they are divided clinically into above knee (iliac, femoral, popliteal) 
and below knee (calf veins) DVT. DVT is well recognised to cause globally significant morbidity 
and mortality both at the time of diagnosis and post diagnosis. Between 30 – 50% of patients 
diagnosed with DVT will go on to develop a post-thrombotic syndrome, which has a significant 
impact on patients’ long-term quality of life. Patients with DVT are also at risk of a oft fatal 
pulmonary embolus (PE), with rates estimated to be 0.4% during the initial anticoagulation 
period and post-treatment.  
 
According to the Charity Thrombosis UK dies every 37 seconds a person of a venous 
thromboembolism in developed countries (Thrombosis UK, 2019). However, positive cases only 
represent 12-25% of the total number of patients who present with suspected DVT. In other 
words, between 75-88% of suspected DVT cases, when fully investigated, are negative.  
It is important to note that this does not consider time lost from work, hospitalisation, treatment 
costs and costs for repeat ultrasound scans. It is notoriously difficult to diagnose a DVT by 
clinical exam alone. The standard approach to make a diagnosis of proximal DVT currently 
involves an algorithm combining pre-test probability- Wells Score, D-dimer (blood) testing, and 
compression ultrasonography (typically a three-point compression examination). There are new 
handheld ultrasound (US) probes available, meaning only the US probe is required for 
diagnostic purposes in conjunction with a smartphone or tablet. At present, although the new 
handheld probes are smaller and are better suited for point of care diagnosis, they still require 
an experienced radiologist or sonographer to perform the three-point compression exam.  

 
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This means that these devices can only be used wherever radiographers/ radiologists are based 
most often in hospitals. However, due to recent advances in “machine learning”, software is now 
being developed for these ‘app-based’ probes that can assist non-specialist healthcare 
professionals to carry out the compression US exam with minimal training. The previous data-
collecting study with 53 participants at Oxford University Hospital (OUH) were used to develop 
a highly sensitive and specific software (AutoDVT) that can be used by non-specialist healthcare 
professionals. 
It is expected that this technology will have similar diagnostic test accuracy to standard 
compression US. The study outlined in this protocol will test this hypothesis. 
 
OUTCOME 
MEASURES 
1)To compare the accuracy of the AutoDVT software with the scan carried out by a qualified 
sonographer as part of standard care. 
2) To compare the accuracy of the AutoDVT software in perspective of different patient groups. 
 
POPULATION Patients attending the DVT clinic, The Bay, at St.Mary’s NHS Trust with a suspected DVT will 
be initially screened by the clinical team for potential inclusion into the trial. If the patient is 
eligible for inclusion in this study, they will be approached to consider taking part in the study. 
Patients will be given sufficient time to read the participant information sheet, and to consider 
taking part and ask any questions they may have. If they agree, they will be asked to sign an 
informed consent form. Once consent has been obtained, the study coordinator, Miss Kerstin 
Saupe will perform the three-point compression ultrasound scan (USS) with the AutoDVT 
Software on the participant. At the end of the scan, the AutoDVT software will indicate "exam 
complete" or "repeat exam". The AutoDVT software will store the results of the scan for analysis 
and review. Following the AutoDVT scan, patients will continue with the standard DVT 
diagnostic pathway, including a standard compression ultrasound scan. 
 
ELIGIBILITY Patients are eligible for this trial if: 
1. The participant has capacity to consent and consent is obtained 
2. The participant is an adult (18 or older in the UK) 
3. The participant has symptoms suggestive of a deep venous thrombosis 
4. The diagnostic DVT algorithm indicates that an ultrasound is needed 
 
A patient will not be eligible for this study if they fulfil one or more of the following criteria: 
1. Patient is under the age of 18. 
2. No data of D-dimer result 
3. The participant is found to have a distal DVT during the US scan (retrospective exclusion) 
4. The participant did not sign the consent form 
 
DURATION 5 months 
 
 
 
 
 
 
 
 
 
 
 

 
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                                                    REFERENCE DIAGRAM 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Lost to follow-up (give reasons) (n= ) 
 
 
Completion of two scans (USS and AutoDVT scan) (n 
= ) 
 Completed two scans (n=  ) 
 Did not complete two scans (give reasons) (n=  ) 
Analysed  (n=  ) 
 Excluded from analysis (give reasons) (n=  ) 
Scan 
Analysis 
Follow-Up 
Adult patient presents with clinical 
suspicion of a deep venous thrombosis 
of their leg and the diagnostic algorithm 
suggests US required† 
Excluded  (n=   ) 
   Not meeting inclusion criteria 
(n=  ) 
   Declined to participate (n=  ) 
   Other reasons (n=  ) 
Enrolled into study 
Enrolment 

 
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1. INTRODUCTION  
 
1.1. 
BACKGROUND 
 
Previous Study: 
The AutoDVT software has been piloted at OUH in the UK for image and data collecting to 
feed the software. Since then, its sensitivity and specificity for DVT detection has been 
optimised. 
(Ethics: 18/SC/0220, IRAS 234007). Hence, this study will compare three-point compression 
AutoDVT to estimate the sensitivity of the software to detect proximal DVT of the leg 
compared to the gold standard. Patient groups, which could profit from this new diagnostic 
tool will be evaluated.  
 
Current Procedure: 
Currently the process from the DVT symptom begin, to diagnosis and then treatment is all but 
not straight forward. It implements a laborious journey for the patient from their GP to A & E, 
then to a specialist sonographer. However handheld Ultrasound device how recently become 
available. The start-up company ThinkSono developed a software which is hoped to divide 
between thrombosis and no thrombosis. In this single blinded pilot study, patients which will 
present at St Mary’s DVT Clinic will be scanned by the specialist with gold standard and after 
this by a non-specialist with the machine learning supported device. The accuracy of the 
device will be compared to the gold standard. This would mean that DVT could be diagnosed 
at point of care by a non-specialist such as a community nurse or nursing home nurse, where 
patients are multi morbid and limited in their mobility. The technology could reduce emergency 
department presentations and free up specialist sonographer to focus on other clinical tasks. 
These improvements could reduce the financial burden of DVT diagnostic services for the 
NHS. 
 
Risks and benefits for participants of study 
Risks: 
The risks to participants are minimal with no proven risks from DVT ultrasound. Careful 
monitoring of adverse events will ensure that any clinical issues are identified promptly. 
Patients will receive an additional scan of their leg to that of routine clinical care, which may 
increase the length of time at the clinic. All patients will be fully informed of what the study 
involves prior to entry. 
 
Benefits: 
There is no direct benefit to the participants but there will be a future potential benefit as 
patients might have access to: 
▪ 
Rapid access to a diagnostic test 
▪ 
Local point of care testing, reducing the need for patients to be seen by specialists at 
hospitals 
▪ 
Reduced need for prophylactic anticoagulation whilst waiting for a scan, thereby 
reducing the potential risk to patients of bleeding whilst taking an anticoagulant 
medication 
▪ 
Reduced cost for diagnosis to NHS due to no longer requiring radiographer led 
services and high technology US devices and reduces the use of anticoagulation 
▪ 
Higher patient satisfaction with their diagnostic clinical pathway  

 
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1.2. 
RATIONALE FOR CURRENT STUDY 
 
This study will compare three-point AutoDVT to estimate the sensitivity of the software to 
detect proximal DVT compared to the gold standard. Patient groups, which could profit from 
this new diagnostic tool will be evaluated. The aim of the study is to estimate the sensitivity 
and specificity of the AutoDVT software and AutoDVT with an additional review by a suitably 
qualified clinician with more than one year of experience diagnosing DVTs (e.g., sonographer 
or radiologist) compared to two or three-point compression ultrasound exam. 
 
2. STUDY OBJECTIVES 
 
The primary objectives of this pilot study are: 
 
1) To compare the accuracy of the AutoDVT software with the scan carried out by a qualified 
sonographer as part of standard care. 
 
2) To compare the accuracy of the AutoDVT software in perspective of different patient groups. 
 
 
3. STUDY DESIGN 
 
This is a non-randomised, single-blinded, prospective cohort pilot study. 
Participants attending the DVT clinic at St. Mary’s NHS Trust with a suspected DVT will be 
initially screened by the clinical team for potential inclusion into the trial. If the patient is eligible 
for inclusion, they will be 
approached to consider taking part in the study. Patients will be given sufficient time to read 
the participant information sheet, and to consider taking part and ask any questions they may 
have. If they agree, they will be asked to sign an informed consent form. Once consent has 
been obtained, Miss Kerstin Saupe will perform the three-point compression ultrasound scan 
(USS) with the AutoDVT software on the participants. At the end of the scan, the AutoDVT 
software will indicate "exam complete" or "repeat exam". The AutoDVT software will store the 
results of the scan for analysis and review. Following the AutoDVT scan, patients will continue 
with the standard DVT diagnostic pathway, including a standard compression ultrasound scan. 
 
Duration: 5 months 
Number and type of subjects: estimation of recruitment of approx. 50 participants 
 
3.1. 
STUDY OUTCOME MEASURES 
 
Primary Outcome:  
Estimate the sensitivity of the AutoDVT software to detect proximal DVT compared to the gold 
standard.  
 
Secondary Outcome:  
The study will evaluate the accuracy and quality, practicality and user acceptance of the 
AutoDVT software.  (Specificity of AutoDVT, positive and negative predictive values of 
AutoDVT, imaging failure rates (with reasons), numbers of eligible patients not enrolled (with 
reasons), duration of AutoDVT scans, number of repeat scans). 
 

 
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4. PARTICIPANT ENTRY 
 
4.1. 
PRE-REGISTRATION EVALUATIONS  
GP/ nurse practitioner obtains certain data before refereeing patient for DVT Scan: age, 
gender, 
underlying malignancy, pregnancy, previous DVT (if yes: when, where). This information gets 
obtained when estimating the Wells Score as one of the main indicators for DVT. Furthermore, 
the referring GP would take a blood test to test for elevated D-Dimer, as second indicator for 
DVT. If the blood test was not obtained from the GP or the result is unknown, it will get retested 
at the DVT clinic.  
 
4.2. 
INCLUSION CRITERIA  
Patients are eligible for this trial if: 
1. The participant has capacity to consent, and consent is obtained 
2. The participant is an adult (18 or older in the UK) 
3. The participant has symptoms suggestive of a deep venous thrombosis 
4. The diagnostic DVT algorithm indicates that an ultrasound is needed 
 
4.3. 
EXCLUSION CRITERIA 
A patient will not be eligible for this study if they fulfil one or more of the following criteria: 
1. Patient is under the age of 18 years. 
2. No data of D-dimer result 
3. The participant is found to have a distal DVT during the US scan (retrospective exclusion) 
4. Participant did not sign consent form 
 
4.4. 
WITHDRAWAL CRITERIA  
All patients will be fully informed of the risks/ benefits of the study. They have the right to 
withdraw. 
 
5. ADVERSE EVENTS  
 
5.1. 
DEFINITIONS  
Adverse Event (AE): any untoward medical occurrence in a patient or clinical study subject.   
 
Serious Adverse Event (SAE): any untoward medical occurrence or effect that: 
• 
Results in death 
• 
Is life-threatening – refers to an event in which the subject was at risk of death at the time of the 
event; it does not refer to an event which hypothetically might have caused death if it were more 
severe 
• 
Requires hospitalisation, or prolongation of existing inpatients’ hospitalisation 
• 
Results in persistent or significant disability or incapacity 
• 
Is a congenital anomaly or birth defect 
 
Medical judgement should be exercised in deciding whether an AE is serious in other 
situations.  Important AEs that are not immediately life-threatening or do not result in 
death or hospitalisation but may jeopardise the subject or may require intervention to 
prevent one of the other outcomes listed in the definition above, should also be 
considered serious. 
 
 

 
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The potential risks of the intervention are minimal. The participants will require an extra scan 
of their leg which will increase the diagnostic process time. The participants will be fully 
informed prior to entry about risks and expected study scan time. Staff doing the extra scan 
will be thoroughly trained to help minimise participants delay. 
 
5.2. 
REPORTING PROCEDURES  
All adverse events should be reported.  Depending on the nature of the event the 
reporting procedures below should be followed.  Any questions concerning adverse 
event reporting should be directed to the chief investigator or the study coordinator in 
the first instance.   
 
5.3.1 Non serious AEs 
All such events, whether expected or not, should be recorded. It should be specified if 
only some non-serious AEs will be recorded, any reporting should be consistent with 
the purpose of the trial end points.  
 
5.3.2 Serious AEs 
An SAE form should be completed and emailed to the study coordinator or chief 
investigator within 24 hours.  However,  hospitalisations for elective treatment of a pre-
existing condition do not need reporting as SAEs. 
 
All SAEs should be reported to the Imperial College Research Ethics Committee 
(ICREC) where in the opinion of the Chief Investigator, the event was: 
• 
‘related’, i.e., resulted from the administration of any of the research procedures; 
and 
• 
‘unexpected’, i.e., an event that is not listed in the protocol as an expected 
occurrence 
 
Reports of related and unexpected SAEs should be submitted within 15 days of the 
Chief Investigator becoming aware of the event, using the NRES SAE form for non-
IMP studies.  The Chief Investigator must also notify the Sponsor of all related and 
unexpected SAEs. 
 
Local investigators should report any SAEs as required by their Local Research Ethics 
Committee, Sponsor and/or Research & Development Office. 
 
Contact details for reporting SAEs 
RGIT@imperial.ac.uk 
 
Chief investigator: Dr.M. Aslam (m.aslam@imperial.ac.uk, Vascular Lab/ D4, 
Hammersmith Hospital, 72 Du Cane Rd., W12 0HS 
Study Coordinator: Miss Kerstin Saupe (ks1221@ioc.ac.uk, Vascular Lab./D4, 
Hammersmith Hospital, 72 Du Cane Rd., W12 0HS) 
 
 
 
 
 

 
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6. ASSESSMENT AND FOLLOW-UP  
 
There won’t be a follow up. This study does not require any continued involvement of the 
participants after their scan. No information of the AutoDVT scan will be given to 2nd parties 
such as the GP or the clinical care team.   
 
This study will only focus on acquired images of AutoDVT software. No incidental findings will 
be reviewed and reported. Participants will receive a DVT diagnostic scan by specialist, who 
can review and report potential findings.  
 
The AutoDVT scan will not be available after the research has finished. 
The collection of data will be finished by the 04.08.2022. The submission of the dissertation 
will be on the 26.08.2022.  
 
7. STATISTICS AND DATA ANALYSIS 
 
Personal data will be kept on the hospital computer systems. Anonymised data will be entered 
on a secure database by study Coordinator. The database itself will be a secure, validated 
system with restricted access, stored on a secure NHS server.  
 
Data and all appropriate documentation will be stored for a minimum of 10 years after the 
completion of the study, including the follow-up period.   
 
 
8. REGULATORY ISSUES  
 
8.1. 
ETHICS APPROVAL  
The Study Coordination Centre has obtained approval from the Research Ethics Committee 
(REC) of Imperial College and Health Research Authority (HRA). The study must also receive 
confirmation of capacity and capability from each participating NHS Trust before accepting 
participants into the study or any research activity is carried out. The study will be conducted 
in accordance with the recommendations for physicians involved in research on human 
subjects adopted by the 18th World Medical Assembly, Helsinki 1964 and later revisions. 
 
8.2. 
CONSENT   
(If study does not involve consent, this section is not relevant) 
Consent to enter the study must be sought from each participant only after a full explanation 
has been given, an information leaflet will be offered, and time allowed for consideration.  
Signed participant consent should be obtained.  The right of the participant to refuse to 
participate without giving reasons must be respected. All participants are free to withdraw at 
any time from the protocol treatment without giving reasons and without prejudicing further 
treatment. 
 
8.3. 
CONFIDENTIALITY  
Pseudonymised data is data that can be linked back to a person (e.g., coded data).  It is 
considered both personal and identifiable data. Anonymised data is data that has no code and 
cannot be linked back to a person (e.g., aggregated data for publication, data without a code 
that cannot be linked back to a person). 
 

 
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The chief investigator will preserve the confidentiality of participants taking part in the study 
and is registered under the Data Protection Act. 
 
Data will be pseudonymised. 
 
No identifiable data will be transferred. Anonymised images will be shared with ThinkSono to 
further optimise AutoDVT software. 
 
 
8.4. 
INDEMNITY 
Imperial College London holds negligent harm and non-negligent harm insurance policies 
which apply to this study 
 
8.5. 
SPONSOR  
Imperial College London will act as the main Sponsor for this study.  Delegated responsibilities 
will be assigned to the NHS trusts taking part in this study.   
 
8.6. 
FUNDING There are no funding this study.  
No payments are involved in this study, neither to participants, nor to principal 
investigator. 
 
8.7. 
AUDITS   
The study may be subject to audit by Imperial College London and their remit as sponsor and 
other regulatory bodies to ensure adherence to GCP and the UK Policy Framework for Health 
and Social Care Research.  
 
 
9. STUDY MANAGEMENT  
 
The day-to-day management of the study will be co-ordinated through the study coordinator, 
Miss Kerstin Saupe.   
 
 
10.  PUBLICATION POLICY  
 
The research will be available to audit by regulatory authorities such as statistics at Imperial 
College, IRAS and supervisor at vascular Lab and the chief investigator Dr. M. Aslam at 
Hammersmith Hospital. Publication may take place in medical peer reviewed journals.   
 
APPENDICES 
 
• 
CE Certificate AutoDVT software 
• 
Patient information sheet 1 
• 
Patient information sheet 2 
• 
Consent form 
• 
Reference list 
• 
Summary of investigations, treatment and assesment 

 
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Appendix 1. 
Summary of investigations, treatment and 
assessments 
 
 
 
  
Wells Score including 
data such as age, 
weight, gender, 
underlying 
malignancy, 
pregnancy, DM, 
previous DVT (if yes: 
when, where) 
Gold 
standard 
Duplex 
Scan by 
specialist 
Informed 
consent 
for 
AutoDVT 
scan 
Scan with 
AutoDVT by 
non-specialist 
Patient 
referred with 
DVT 
symptomatic 
to DVT clinic 

 
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EU Declaration of Conformity 
 
 
 
ThinkSono Limited 
Flat 35 
Healthcroft  
LONDON 
W5 3BF 
United Kingdom 
 
declares that under our sole responsibility, the product: 
 
AutoDVT R1 
 
is in conformity with the Essential Requirements and provisions of the following EC Directives, 
including all amendments and national legislation implementing these Directives: 
 
Directive 93/42/EEC (Medical Devices) 
 
Classification: 
 
Class 1 - Active Medical Device – Ultrasound Imaging System Application Software (40873) 
 
The following standards and technical specifications have been applied: 
 
BS EN ISO 13485:2016 
BS EN ISO 14971:2012 
DCB0129:2018 
ISO 15233-1:2016 
 
This product carries the CE Mark, which was first affixed in June 2020. 
 
Place 
Signature 
Date 
London, England 
 
24th June 2020 
 
Beverley Scott 
Regulatory Affairs Officer 
(BCS Clinical Consulting Limited) 
On behalf of ThinkSono 
 
 

 
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Reference list 
“The Holy Bible, English Standard Version” Reference Bible Crossway 
https://books.apple.com/de/book/esv-classic-reference-bible/id368214461?l=en This 
material may be protected by copyright... (2011). Wheaton, Ill: Crossway Bibles. 
Amboss (2021). Deep vein thrombosis - Knowledge @ AMBOSS. [online] 
www.amboss.com. Available at: 
https://www.amboss.com/us/knowledge/Deep_vein_thrombosis/ [Accessed 3 Dec. 2021]. 
Black, C.M. (2014). Anatomy and Physiology of the Lower-Extremity Deep and Superficial 
Veins. Techniques in Vascular and Interventional Radiology, 17(2), pp.68–73. 
Edwin, Büller, H.R. and Matthijs Oudkerk (2009). Deep vein thrombosis and pulmonary 
embolism. Chichester, Uk ; Hoboken, Nj: J. Wiley-Blackwell. 
Fleck, D., Albadawi, H., Wallace, A., Knuttinen, G., Naidu, S. and Oklu, R. (2017). Below-
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