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. 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 artificial intelligence (AI) model training.
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.
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.