Researchers are developing an artificial intelligence (AI) algorithm to diagnose deep vein thrombosis (DVT) more quickly and as effectively as traditional radiologist-interpreted diagnostic scans, potentially cutting down long patient waiting lists and avoiding patients unnecessarily receiving drugs to treat DVT when they don’t have it.
The study, published in the journal Digital Medicine, is the first to show that machine learning algorithms can potentially diagnose DVT, a type of blood clot most commonly formed in the leg, causing swelling, pain and discomfort.
If left untreated, it can lead to fatal blood clots in the lungs. Between 30 and 50 per cent of people who develop a DVT can go on to have long-term symptoms and disability.
Researchers at the University of Oxford, Imperial College and the University of Sheffield collaborated with the tech company ThinkSono to train a machine-learning algorithm, AutoDVT, to distinguish patients who had DVT from those who did not.
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 save health services $150 per examination.
“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 hand-held ultrasound machine shows promising results,” said study lead Dr Nicola Curry, Consultant Haematologist at Oxford University Hospitals NHS Foundation Trust and Head of OUH’s Oxford Haemophilia and Thrombosis Centre. .
The research team are due to start a test-accuracy blinded clinical study, comparing the accuracy of AutoDVT with standard care to determine the sensitivity of the algorithm for picking up DVT cases.
“The AI algorithm can not only be trained to analyse ultrasound images to discriminate the presence or absence of a blood clot – it can also direct the user using the ultrasound wand 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 research team hope that the combination of the AutoDVT tool, with the inclusion of the AI algorithm, will allow non-specialist healthcare professionals, like GPs and nurses, to quickly diagnose and treat DVT. It may additionally allow the collection of images by non-specialists which could be sent to an expert, facilitating diagnosis of those unable to get to a specialist.
“Currently, many patients do not have a definitive diagnosis within 24 hours of a suspected DVT, and so many patients end up receiving painful injections of what can often be an unnecessary anticoagulant, with potential side-effects,” said Dr Curry, who is Co-theme Lead for the NIHR Oxford Biomedical Research Centre’s Haematology Theme and part of the Oxford Centre for Haematology.