ThinkSono DVT detection software results are published in Nature digital medicine (npj)

ThinkSono Ltd

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LONDON and POTSDAM, Germany, Sept. 15, 2021 /PRNewswire=KYODO JBN/--

 

A team of researchers are developing the use of an artificial intelligence (AI)

algorithm with the aim of diagnosing 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.

 

ThinkSono AutoDVT App on a smartphone

DVT is 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. 30-50% of people who develop a DVT can go on to have long-term

symptoms and disability.

 

Researchers at Oxford University, Imperial College and the University of

Sheffield collaborated with the tech company ThinkSono (which is led by Fouad

Al-Noor and Sven Mischkewitz), to train a machine learning AI algorithm

(AutoDVT) to distinguish patients who had 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 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, a researcher at Oxford University's Radcliffe

Department of Medicine and clinician at Oxford University Hospitals NHS

Foundation Trust.

 

This is the first study to show that machine learning AI algorithms can

potentially diagnose DVT, and the researchers are due to start a test-accuracy

blinded clinical study, comparing the accuracy of AutoDVT with standard care to

determine the sensitivity  of the for picking up DVT cases. The hope will be

that AutoDVT will get the right diagnosis faster to the nearly 8 million people

worldwide who potentially have a venous blood clot each year.

 

"The AI algorithm can not only be trained to analyse ultrasound images to

discriminate the presence versus the 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-effect,"

said Dr Curry, who is also part of the Oxford Centre for Haematology.

 

Fouad Al Noor, the CEO of ThinkSono stated: "We are delighted by the results in

this study and are excited to work further with Oxford University Hospital and

other partner hospitals to trial the software and bring it to patients

worldwide."

 

The results from the study are published in the journal npj Digital Medicine.

 

ThinkSono's website is found at:

www.thinksono.com

 

Contact ThinkSono for further information: Fouad Al Noor, hello@thinksono.com

 

Note: The research paper is found here at:

https://www.nature.com/articles/s41746-021-00503-7 with DOI:

https://doi.org/10.1038/s41746-021-00503-7

 

Photo - https://mma.prnewswire.com/media/1626577/ThinkSono.jpg

 

Source: ThinkSono Ltd

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