"AutoDVT" is a software system designed to assist non-specialist operators, such as nurses, general practitioners (GP) and other allied health professionals in the diagnosis of DVT. The software utilises a "machine learning" algorithm as described below.
This study aims to improve the current laborious, time consuming and expensive diagnostic DVT pathway.
Venous thrombosis (VT) commonly occurs in the deep leg veins as well as the deep veins of the pelvis. DVTs can be divided into above knee (iliac, femoral, popliteal) and below knee (calf veins).
DVT is well recognised to cause globally significant morbidity and mortality both at the time of diagnosis and post-diagnosis. Between 30 - 50 percent of patients diagnosed with DVT will go on to develop a post-thrombotic syndrome, which has a significant impact on patients' long-term quality of life. Patients with DVT are also at risk to develop a fatal pulmonary embolism (PE). According to Charity Thrombosis United Kingdom (UK) dies every 37 seconds a person of a VT in developed countries.
Between 75-88 percent of suspected DVT cases, when fully investigated, are negative. The cost for diagnosing DVT over a decade ago was between 42-202 British Pound (£), such that the cost to the NHS of investigating all patients who present with DVT symptoms was approximately £175 million annually as stated in the study 'Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning' by Prof. Kainz from Imperial College London.
It is important to note that this value does not take into account any additional indirect costs such as time lost from work, hospitalisation, treatment costs and costs for repeat ultrasound scans. It is difficult to diagnose a DVT by clinical exam alone. The current standard approach to diagnose a proximal DVT involves an algorithm combining pre-test probability (Wells Score), D-dimer (blood) testing, and compression ultrasonography (typically a three-point compression examination).
There are new handheld ultrasound (US) probes available, meaning only the US probe is required for diagnostic purposes in conjunction with a mobile phone or tablet. At present, although the new handheld probes are smaller and are better suited for point of care diagnosis, they still require an experienced radiologist or sonographer to perform the three-point compression exam.
This means, that these devices can only be used wherever specialists such as radiographers or radiologists are based. However, due to recent advances in "machine learning", a software has now been developed for these 'app-based' probes that can assist non-specialist healthcare professionals to carry out the compression US exam with minimal training and divide between DVT and not DVT.
The previous data-collecting study for this device at Oxford University Hospital (OUH) was primarily used to improve the AutoDVT software but it also highlighted in a small pilot study that this technology had a similar diagnostic test accuracy to standard compression US. The study outlined in this protocol will test this hypothesis.
