AI and robots used to enable faster detection of UTIs

Some 150 million people a year worldwide can suffer from UTIs, making them one of the most common types on infection.
AI and robots used to enable faster detection of UTIs
A new initiative using AI to help detect UTIs could reduce anti-biotic prescribing, those involved in the project have claimed (Chris Ison/PA)

Residents in care homes in Scotland could be some of the first to benefit from a project which aims to use artificial intelligence (AI) and robots to detect urinary tract infections (UTIs) earlier.

Researchers from both the University of Edinburgh and the city’s Heriot-Watt University are working together on the Feather initiative – which seeks to reduce some of the negative consequences of late diagnosis of UTIs.

The expert leading the scheme, Professor Kia Nazarpour from the University of Edinburgh, said it could eventually reduce the number of people attending accident and emergency with infections, as well as helping to tackle the over-prescription of antibiotics.

Some 150 million people a year worldwide can suffer from UTIs, making them one of the most common types on infection.

When detected early, UTIs can be treated with antibiotics, but if left undiagnosed they can lead to sepsis, kidney damage and even death.

Early signs of a UTI can be difficult to spot because symptoms vary according to age and existing health conditions.

There is no single sign of infection but a collection of symptoms which could include pain, a raised temperature, frequency of urination, changes in sleep patterns and tremors.

The new research involves sensors being installed to spot changes in behaviour which could indicate someone has such an infection – such as increased kettle use or a change in walking pace – before the individual or their carers are aware of the problem.

These would then trigger an interaction with “socially assistive robot”, with the technology for this being developed at the new Assisted Living Lab at the National Robotarium at Heriot-Watt University.

The work has already been awarded £1.1 million in UK Government funding from the Engineering and Physical Sciences Research Council – which is part of UK Research and Innovation – and the National Institute for Health and Care Research (NIHR).

Work is ongoing with two industry partners from the care sector – Scotland’s national respite centre, Leuchie House, and Blackwood Homes and Care – with researchers looking to develop the machine learning methods and interactions for socially assistive robots that could support earlier detection of a potential infection and raise an alert for investigation by a clinician.

Scotland Office minister Lord Offord said the research could ‘make a big difference to detecting UTIs as quickly as possible’ (Aaron Chown/PA )

Kitty Walker, who is a regular guest at Leuchie House, said UTIs could affect her speech, making it hard to communicate.

“The impact of having a UTI can be far more serious than a lot of people may realise,” she said.

“I’ve been hospitalised in the past after the late diagnosis of a UTI led to me having a seizure and I required mouth-to-mouth resuscitation.

“It can often take a long time to receive a full diagnosis and be given the right antibiotics to tackle the infection.

Being able to spot the early indicators that I have a UTI would save any anxiety I might feel when I know there is a problem and help reduce the number of different antibiotics I need to take.”

Prof Nazarpour, the project lead and professor of digital health at the School of Informatics, University of Edinburgh, said the work with the Feather project would “help individuals, carers and clinicians to recognise the signs of potential urinary tract infections far earlier, helping to prompt the investigations and medical tests needed”.

He added: “Earlier detection makes timely treatment possible, improving outcomes for patients, lowering the number of people presenting at A&E, and reducing costs to the NHS.

“We also believe it will help to minimise the amount of antibiotics that are necessarily prescribed as a cover while waiting for lab results.

“As the second most common reason for the prescription of antibiotics, the infection makes a significant contribution to the increasingly concerning problem of drug-resistant bacteria, and there is widespread advantage to society in implementing better diagnosis.”

Scotland Office minister Malcolm Offord said: “Data and AI have the potential to transform diagnosis and treatment of so many conditions and improve outcomes for patients.

“This research will make a big difference to detecting UTIs as quickly as possible, and I am glad residents in Scotland’s care sector will be some of the first to benefit.

“The UK Government is providing £1.1 million of research funding for this project, and through the City Deal we are investing £21 million in the new National Robotorium facilities at Heriot-Watt University.”

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