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Systematic Machine Learning Algorithm for Rapid Thrombosis Detection
Sponsor: Ostfold Hospital Trust
Summary
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.
Official title: Evaluating a New Diagnostic Strategy for Suspected DVT Consisting of Point of Care D-dimer, AI-based Prediction Model and Compression Ultrasound
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
INTERVENTIONAL
Enrollment
1000
Start Date
2025-01-06
Completion Date
2029-01-05
Last Updated
2025-02-26
Healthy Volunteers
No
Conditions
Interventions
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.
Locations (1)
Østfold Hospital Trust
Sarpsborg, Norway