A pilot study to evaluate AI-assisted ultrasound software for the diagnosis of venous thrombosis
| ISRCTN | ISRCTN24293748 |
|---|---|
| DOI | https://doi.org/10.1186/ISRCTN24293748 |
| ClinicalTrials.gov (NCT) | Nil known |
| Clinical Trials Information System (CTIS) | Nil known |
| Protocol serial number | v1.0.0 |
| Sponsor | ThinkSono GmbH |
| Funder | ThinkSono GmbH |
- Submission date
- 11/05/2023
- Registration date
- 13/05/2023
- Last edited
- 13/11/2025
- Recruitment status
- No longer recruiting
- Overall study status
- Completed
- Condition category
- Circulatory System
Plain English summary of protocol
Background and study aims
Deep vein thrombosis (DVT) is a term that describes blood clots (thrombi) that can form in the deep veins. The deep leg veins are commonly affected (such as the proximal veins: the femoral vein or the popliteal vein) or the deep veins of the pelvis. The standard approach to making a diagnosis involves an algorithm combining pre-test probability, a blood test called the D-dimer test, and the patient undergoing an ultrasound of the leg veins. Ultrasound is currently completed by a trained expert (e.g. sonographer or radiologist). However, handheld ultrasound probes have recently become available and they have enabled ‘app-based’ ultrasonography to be performed. ThinkSono has developed software (AutoDVT software) allowing non-specialists to perform DVT ultrasound, hoping it has the same accuracy for diagnosing DVT as the standard ultrasound. If this study has a positive outcome, it would mean that DVT could be diagnosed at the point of care by non-experts such as nurses, junior doctors, general practitioners and other healthcare staff. By diagnosing DVT early in the clinical pathway (for example, at GP practices), the technology could reduce emergency department admissions and free up specialists to focus on other clinical tasks. These improvements could also potentially reduce the financial burden of the DVT diagnostic service on healthcare systems.
Who can participate?
Patients aged 18 years and over, coming for a check to see if they have a DVT and have symptoms suggesting that they need an ultrasound scan
What does the study involve?
Participants undergo two compression ultrasound scans. One is carried out by a non-radiology staff member (e.g. a nurse) using AI software to guide them and another ultrasound scan will be carried out as already scheduled by a sonographer or radiologist.
What are the possible benefits and risks of participating?
This study will not benefit participants directly in the short term but it may benefit patients having an ultrasound for a DVT in the future. The results from this study will improve knowledge of how software may be able to help diagnose blood clots accurately and quickly.
Ultrasound is a very safe method of confirming a DVT or not and is used already as standard care in hospitals. There are no risks of taking part. The scan does involve some pressing on the leg but if it is painful or participants want to stop they can let the researchers know.
Where is the study run from?
University General Hospital "Attikon" (Greece)
When is the study starting and how long is it expected to run for?
February 2021 to May 2022
Who is funding the study?
ThinkSono GmbH (Germany)
Who is the main contact?
Sven Mischkewitz (Sponsor contact), hello@thinksono.com
Contact information
Scientific
Skoufa 10
Kolonaki
Athens
106 73
Greece
| Phone | +30 210 6862675 |
|---|---|
| info@vascularhealth.gr |
Public
August-Bebel-Straße 88
Potsdam
14482
Germany
| Phone | +491754724848 |
|---|---|
| hello@thinksono.com |
Study information
| Primary study design | Interventional |
|---|---|
| Study design | Non-randomized prospective double-blind study |
| Secondary study design | Non randomised study |
| Scientific title | A pilot study to evaluate AI-assisted ultrasound software for the diagnosis of venous thrombosis |
| Study objectives | This study will compare the standard protocol of lower extremity venous ultrasound to rule out venous thrombosis, as the recognised modality of choice, with ultrasound-assisted by artificial intelligence software (AutoDVT) combined with a remote assessment by a specialist radiologist. |
| Ethics approval(s) | Approved 07/04/2021, Attikon University Hospital - Ethics Committee (1 Rimini Str, 12462 Chaidari, Greece; +30 210 5831692; greps@attikonhospital.gr), ref: ANT1N/ANGH, ED. 164/18-3-2021 |
| Health condition(s) or problem(s) studied | Proximal deep vein thrombosis |
| Intervention | Patients consented when scheduled for a DVT ultrasound exam with the radiology department. An AI-assisted scan with the AutoDVT software is performed by a non-specialist (nurse). A follow-up gold-standard scan is performed by a local specialist (compression ultrasound), i.e. radiologist. That same-day follow-up scan represents the standard of care. The images collected by the non-specialist are presented to a remote, qualified clinician who will evaluate image quality according to the quality scale of the American College of Emergency Physicians (ACEP) and consequently, if the image quality is sufficient, assesses whether the veins of the patient are compressible, incompressible or indeterminate. This is a single-arm study. Every patient received the AI-guided scan and was followed up by a gold standard exam which represents the standard of care. Nursing staff carried out the AI-guided ultrasound scan. They had no prior ultrasound experience at all. The remote qualified clinician assessing the images that have been collected by the AI-guided scan are qualified to diagnose DVT, i.e., radiologists. The AI-guided scan was performed face-to-face. The images that have been presented to the remote qualified clinician are evaluated retrospectively via an internet platform. This remote qualified clinician did not see the patient. The AI-guided scan was carried out in the rooms of the radiology department. |
| Intervention type | Device |
| Phase | Not Applicable |
| Drug / device / biological / vaccine name(s) | AutoDVT |
| Primary outcome measure(s) |
Sensitivity and specificity measured using AI-guided ultrasound and a local imaging specialist performing the gold-standard ultrasound exam at the same timepoint |
| Key secondary outcome measure(s) |
Image quality of the AI-guided ultrasound measured by a remote qualified clinician according to the American College of Emergency Physicians (ACEP) scoring scale from 1 to 5 at one timepoint |
| Completion date | 01/05/2022 |
Eligibility
| Participant type(s) | Patient |
|---|---|
| Age group | Mixed |
| Lower age limit | 18 Years |
| Upper age limit | 99 Years |
| Sex | All |
| Target sample size at registration | 50 |
| Total final enrolment | 50 |
| Key inclusion criteria | 1. Aged 18 years old and over 2. Suspicion of the presence of a deep vein thrombosis, indicating a compression ultrasound exam according to standard clinical practice 3. Capacity to consent to the study through the patients or the Legal Representative |
| Key exclusion criteria | 1. Inability to consent to the study or rejection through patients or the legal representative. 2. Pregnant for more than 12 weeks 3. D-dimer testing cannot be performed/patient is on anticoagulation 4. History of DVT in the symptomatic leg |
| Date of first enrolment | 19/10/2021 |
| Date of final enrolment | 11/04/2022 |
Locations
Countries of recruitment
- Greece
Study participating centre
Haidari, Athens
12462
Greece
Results and Publications
| Individual participant data (IPD) Intention to share | No |
|---|---|
| IPD sharing plan summary | Not expected to be made available |
| IPD sharing plan |
Study outputs
| Output type | Details | Date created | Date added | Peer reviewed? | Patient-facing? |
|---|---|---|---|---|---|
| Results article | 10/10/2023 | 13/11/2025 | Yes | No |
Editorial Notes
13/11/2025: The previous publication reference was incorrect and was removed, and a new publication reference added.
27/05/2025: Publication reference added.
12/05/2023: Trial's existence confirmed by the Administration of The 2nd Geo-Economic Region of the District of Piraeus and Aegean University General Hospital (Greece).
