A Multi-center Cohort Study for Conventional Ultrasound Image Set Collection to Create a Data Set for Research Purposes.

NCT06989255 | Recruiting DMC

• 2 other identifiers: ThrombUS-Study-A101137227-A
• Study Type: observational
• Target: 3,000
• Locations: 4 countries
• Sites: 5

Brief Summary

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. Primary objectives:

1. 1.Collect and curate imaging data from ultrasound scans of patients suspected for DVT.
2. 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. 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.

Conditions

Deep Vein Thrombosis

Keywords

deep vein thrombosis; ultrasound scan; lower limb; compression ultrasound

Outcome Measures

Primary Outcomes (1)

• Ultrasound data

  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.

  Day 1

Study Arms (1)

Patients with suspected DVT referred for a DVT ultrasound scan.

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).

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study 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).

You may qualify if:

• Age ≥18 years.
• The participant has the capacity to consent, and consent is obtained prior to any study-specific procedures.
• The conventional diagnostic DVT algorithm indicates that an ultrasound is needed, or the patient has been referred for a scan on suspicion of DVT.

You may not qualify if:

• Patients with a known condition or reason that may potentially result in interrupting or stopping the ultrasound examination before its completion.
• Patients considered by their treating physician or the ultrasound operator as non-suitable for a standard ultrasound scan.
• Patients who have not signed the informed consent.

Sponsors & Collaborators

• ThrombUS+: lead

Study Sites (5)

Groupement Hospitalier Eaubonne Montmorency Simone Veil

Montmorency, 95160, France

RECRUITING

University General Hospital of Alexandroupoli

Alexandroupoli, GR 68100, Greece

RECRUITING

Papageorgiou General Hospital

Thessaloniki, 54603, Greece

RECRUITING

Home Relief of Suffering Hospital

San Giovanni Rotondo, FG 71013, Italy

RECRUITING

Lithuanian University of Health Science

Kaunas, 44307, Lithuania

RECRUITING

Related Publications (8)

• Kaldoudi, E. et al. (2024). Towards Wearable Continuous Point-of-Care Monitoring for Deep Vein Thrombosis of the Lower Limb. In: Jarm, T., Šmerc, R., Mahnič-Kalamiza, S. (eds) 9th European Medical and Biological Engineering Conference. EMBEC 2024. IFMBE Proceedings, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-031-61628-0_36

  BACKGROUND

• ThrombUS+: Wearable Continuous Point-of-Care Monitoring, Risk Estimation and Prevention for Deep Vein Thrombosis, European Union, Horizon Europe Programme, Grant Agreement No, 101137227, 2024-2027, https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/opportunities/projects-details/43108390/101137227

  BACKGROUND

• Maynard G, 2015, Preventing Hospital-Associated Venous Thromboembolism A Guide for Effective Quality Improvement, AHRQ Publication No. 16-0001-EF, https://www.ahrq.gov/sites/default/files/publications/files/vteguide.pdf

  BACKGROUND

• Di Nisio M, van Es N, Buller HR. Deep vein thrombosis and pulmonary embolism. Lancet. 2016 Dec 17;388(10063):3060-3073. doi: 10.1016/S0140-6736(16)30514-1. Epub 2016 Jun 30.

  PMID: 27375038 BACKGROUND

• Heit JA, Spencer FA, White RH. The epidemiology of venous thromboembolism. J Thromb Thrombolysis. 2016 Jan;41(1):3-14. doi: 10.1007/s11239-015-1311-6.

  PMID: 26780736 BACKGROUND

• 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. doi: 10.1111/jth.12698.

  PMID: 25302663 BACKGROUND

• 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. doi: 10.3390/jcm9082467.

  PMID: 32752154 BACKGROUND

• 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. doi: 10.1177/0003319708323494. Epub 2008 Oct 14.

  PMID: 18854340 BACKGROUND

Related Links

• Data management plan
• ThrombUS+ EU funded project website
• ThrombUS+ public repository at ZENODO

MeSH Terms

Conditions

Venous Thrombosis

Condition Hierarchy (Ancestors)

Thrombosis; Embolism and Thrombosis; Vascular Diseases; Cardiovascular Diseases

Study Officials

• Eleni Kaldoudi, Prof.

  ATHENA Research Center, Greece

  STUDY DIRECTOR

• Andrius Macas, Prof. Dr.

  Lithuanian University of Health Science [Lietuvos Sveikatos Mokslu Universitetas], Lithuania

  PRINCIPAL INVESTIGATOR

• Michail Potoupnis, Prof.

  School of Medicine, Aristotle University of Thessaloniki and Papageorgiou General Hospital, Greece

  PRINCIPAL INVESTIGATOR

• Elvira Grandone, Prof.

  Home Relief of Suffering Hospital [Fondazione Casa Sollievo Della Sofferenza], Italy

  PRINCIPAL INVESTIGATOR

• Maxime Gautier, Dr.

  Simon Veil Hospital, France

  PRINCIPAL INVESTIGATOR

• Savvas Defteraios, Prof.

  University General Hospital of Alexandroupoli, Greece

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Eleni Kaldoudi, Prof.

CONTACT

+306937124358; kaldoudi@athenarc.gr

Stelios Didaskalou, Dr.

CONTACT

+30 697 163 5361; stelios.didaskalou@athenarc.gr

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 8, 2025
• First Posted: May 25, 2025
• Study Start: November 6, 2024
• Primary Completion: December 31, 2025
• Study Completion: December 31, 2025
• Last Updated: May 25, 2025

Record last verified: 2025-05

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, ICF, CSR
• Time Frame: Anticipated start date: June 2026 End date: --- 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 researcher.
• Access Criteria: Any interested party will access the data described above that will be available publicly. 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, existing related 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.

Locations

LI (1); FR (1); GR (2); IT (1)