The FEATHER project aims to reduce the adverse effects of urinary tract infections

The FEATHER project aims to reduce the adverse effects of urinary tract infections

A team of researchers from the University of Edinburgh and Heriot-Watt University are developing artificial intelligence (AI) and social work robots to detect urinary tract infections (UTIs) earlier.

The FEATHER project aims to reduce the number of serious adverse effects that can result from late or misdiagnosis and to reduce the amount of antibiotics prescribed while clinicians await laboratory results.

The research has received £1.1 million from the UK government through the Engineering and Physical Sciences Research Council, part of UK Research and Innovation, and the National Institute for Health and Care Research (NIHR).

UTIs affect 150 million people worldwide each year, making it one of the most common types of infection. When diagnosed early, it can be treated with antibiotics. If left untreated, UTIs can lead to sepsis, kidney damage, and even death.

Diagnosis, however, can be difficult with laboratory analysis, a process taking up to 48 hours, providing the only definitive result. Early signs of a UTI can also be difficult to recognize as symptoms vary depending on age and existing health conditions. There is no single sign of infection, but a collection of symptoms that can include pain, temperature, frequency of urination, changes in sleep patterns, and tremors.

UTIs are particularly difficult to diagnose in people receiving formal care, and there is significant antibiotic overtreatment in this group as clinicians wait for lab results to return.

To address these concerns, researchers from the University of Edinburgh and Heriot-Watt University are working with two industrial partners in the care sector. Scotland’s National Respite Centre, Leuchie House, and Blackwood Homes and Care are providing user insights to help researchers develop machine learning methods and interactions for social service robots to enable earlier detection of potential infection and trigger an alert for investigation by a clinician.

The project will collect continuous data on the daily activities of individuals in their homes via sensors that could help detect changes in behavior or activity levels and trigger interaction with a social worker robot. The FEATHER platform will combine and analyze these data points to flag potential signs of infection before an individual or caregiver realizes there is a problem. Behavioral changes could include the use of a kettle, a change in walking pace, cognitive function through interaction with a social service robot, or a change in sleep patterns.

The AI ​​and implementation aspects of the project will be led by Professor Kia Nazarpour, Dr Nigel Goddard and Dr Lynda Webb from the University of Edinburgh. Aspects of human-robot interaction will be led by Professor Lynne Baillie, assisted by Dr. Mauro Dragone, from Heriot-Watt University.

Professor Kia Nazarpour, Project Leader and Professor of Digital Health at the University of Edinburgh’s School of Informatics, said: “This unique data platform will help individuals, carers and clinicians recognize the signs potential UTIs much sooner, helping to trigger the necessary investigations and medical tests. Earlier detection enables rapid treatment, improves patient outcomes, reduces the number of people presenting to A&E and reduces costs for the NHS.

“We also believe it will help minimize the amount of antibiotics that are necessarily prescribed as cover while awaiting lab results. As the second most common reason for antibiotics being prescribed, infection contributes significantly to the problem. of growing concern of drug-resistant bacteria, and there is widespread benefit to society in implementing better diagnostics.

Professor Lynne Baillie, Head of the National Robotarium on Human-Robot Interaction, Assistive Living and Health, added: “We hope this work will create an additional structured support mechanism for people who live independently. Studies show that there is a significant association between delirium and UTIs in the elderly, and while it is possible that caregivers pick up on these signs, we should not rely on observations alone. We work with stakeholders to co-design robot interaction and data collection for machine learning methods to better support longer and healthier independent living.

“Working sensitively and supportively with this vulnerable social group is of utmost importance. By developing the technology in the new Assisted Living Lab at the National Robotarium, we are able to test it in a realistic social care setting.

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