MedTrace Logo

Systematic Machine Learning Algorithm for Rapid Thrombosis Detection

NCT06842446 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 1000

Last updated 2025-02-26

No results posted yet for this study

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.

Conditions

  • Deep Vein Thrombosis

Interventions

DIAGNOSTIC_TEST

POC D-dimer

POC D-dimer will be compared to laboratory D-dimer in hospital setting and used in a machine learning model

DIAGNOSTIC_TEST

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.

DIAGNOSTIC_TEST

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.

Sponsors & Collaborators

  • Sahlgrenska University Hospital

    collaborator OTHER

  • Ostfold Hospital Trust

    lead OTHER

Study Design

Allocation
NA
Purpose
DIAGNOSTIC
Masking
NONE
Model
SINGLE_GROUP

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-01-06
Primary Completion
2027-01-05
Completion
2029-01-05

Countries

  • Norway

Study Locations

More Related Trials

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06842446 on ClinicalTrials.gov