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Deep Venous Thrombosis of Leg Deep Vein Thrombosis
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The study coordinator aims to compare gold standard deep vein thrombosis (DVT) diagnostic performed by a specialist sonographer to a scan by a non-specialist with a newly developed an automated DVT (AutoDVT) detection software device.
The title of the project is: Benefit of Machine learning to diagnose Deep Vein thrombosis compared to gold standard Ultrasound.
Currently the process from the DVT symptom begin, to diagnosis and then treatment is all but not straightforward. It implements a laborious journey for the patient from their general practitioner (GP) to accident and emergency (A\&E), then to a specialist sonographer.
However, handheld Ultrasound devices have recently become available and they have been implemented with a machine learning software. The startup company ThinkSono developed a software which is hoped to divide between thrombosis and no thrombosis. In this single-blinded pilot study, patients which present at St Mary's DVT Clinic will be scanned by the specialist and then by a non-specialist with the machine learning supported device. The accuracy and sensitivity of this device will be compared to the gold standard.
This would mean that DVT could be diagnosed at point of care by a non-specialist such as a community nurse or nursing home nurse, for example beneficial for multimorbid confused nursing home patients. This technology could reduce A\&E crowding and free up specialist sonographer to focus on other clinical tasks. These improvements could significantly reduce the financial burden for the National Health System (NHS).
The AutoDVT has a CE (as the logo CЄ, which means that the manufacturer or importer affirms the good's conformity with European health, safety, and environmental protection standards) Certificate under the directive 93/42/ European Economic Community (EEC) for medical devices. It is classified in Class 1 - Active Medical Device - Ultrasound Imaging System Application Software (40873).
Furthermore, following standards and technical specifications have been applied: British Standard (BS) European Norm (EN) International Organisation for Standardisation (ISO) 13485:2016, BS EN ISO 14971:2012, Data Coordination Board (DCB)0129:2018, ISO 15233-1:2016.
Start dates may differ between countries and research sites. The research team are responsible for keeping the information up-to-date.
The recruitment start and end dates are as follows:
"Kainz B, Heinrich MP, Makropoulos A, Oppenheimer J, Mandegaran R, Sankar S, Deane C, Mischkewitz S, Al-Noor F, Rawdin AC, Ruttloff A, Stevenson MD, Klein-Weigel P, Curry N. Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning. NPJ Digit Med. 2021 Sep 15;4(1):137. doi: 10.1038/s41746-021-00503-7."; "34526639"
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This is in the inclusion criteria above
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The study is sponsored by Imperial College London
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