Researchers at Oxford University, Imperial College, and the University of Sheffield collaborated with English tech company ThinkSono to train a machine learning artificial intelligence (AI) algorithm (AutoDVT) to distinguish patients who had deep vein thrombosis (DVT) from those without DVT. The AI algorithm accurately diagnosed DVT when compared to the gold standard ultrasound scan, and the team worked out that using the algorithm could potentially reduce the cost of such examinations.
“Traditionally, DVT diagnoses need a specialist ultrasound scan performed by a trained radiographer, and we have found that the preliminary data using the AI algorithm coupled to a handheld ultrasound machine shows promising results,” said study lead Nicola Curry, MD FRCP FRCPath, a researcher at Oxford’s Radcliffe Department of Medicine and clinician at Oxford University Hospitals NHS Foundation Trust.
“The AI algorithm can not only be trained to analyze ultrasound images to discriminate the presence versus the absence of a blood clot, it can also direct the ultrasound wand user to the right locations along the femoral vein, so that even a non-specialist user can acquire the right images,” said study team member Christopher Deane from the Oxford Haemophilia and Thrombosis Centre.
The researchers are due to start a test-accuracy blinded clinical study, comparing the accuracy of AutoDVT with standard care to determine the sensitivity for picking up DVT cases.