Systematic Machine Learning Algorithm for Rapid Thrombosis Detection
Evaluating a New Diagnostic Strategy for Suspected DVT Consisting of Point of Care D-dimer, AI-based Prediction Model and Compression Ultrasound
About This Trial
The goal of this clinical trial is to compare the use of a machine learning-based algorithm and point-of-care D-dimer to laboratory D-dimer and compression ultrasound to exclude deep vein thrombosis in the under extremities in patients referred to a medical department suspected of having deep vein thrombosis. The main aim is to answer are if a machine learning algorithm and point of care D-dimer can exclude deep vein thrombosis in more patients than clinical assessment and D-dimer alone.
Who May Be Eligible (Plain English)
Original Eligibility Criteria
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Treatments Being Tested
POC D-dimer
POC D-dimer will be compared to laboratory D-dimer in hospital setting and used in a machine learning model
POC ultrasound
Point of care (POC) ultrasound performed by ED physicians compared to ultrasound performed by radiologist. POC ultrasound 3 point examination performed by ED physician will be compared with POC ultrasound full leg examination performed by ED physician.
Machine learning model
The DSS will be compared to the usual strategy. It will also be estimated how many participants where DVT could have been excluded without ultrasound.