University of Surrey develops New AI to detect urinary tract infections
The University of Surrey has developed a New AI to identify and help reduce urinary tract infections (UTI).
Scientists from the University’s Centre for Vision, Speech and Signal Processing (CVSSP) detailed how they used a technique called Non-negative Matrix Factorisation to find hidden clues of possible UTI cases. They then used novel machine learning algorithms to identify early UTI symptoms.
UTI is described as an infection of any part of the urinary system. Symptoms include pain in the lower part of the stomach, blood in urine, frequent urination or urgency to urinate.
UTIs also change the mood and behavior of patients and have been identified as a major cause of hospitalisation for people living with dementia.
Clinicians were allowed to remotely monitor the health of people with dementia living at home through a network of internet enabled devices. These included environmental and activity monitoring sensors and vital body signal monitoring devices.
Data collected from these devices was analysed using machine learning solutions, and the identified health problems were flagged on a digital dashboard.
Findings of the experiment are detailed in a paper published in PLOS ONE, a nonprofit publisher, innovator and advocacy organization.
Views on the Findings
In a publication by sciencedaily.com, Professor of Machine Intelligence at CVSSP , Payam Barnaghi, expressed satisfaction at the success of the experiment and was confident the developed algorithm will be a valuable tool for healthcare professionals.
His colleague, Dr Shirin Enshaeifar, a Senior Research Fellow at CVSSP, said
“I am delighted to see that the algorithms we have designed have an impact on improving the healthcare of people with dementia and providing a tool for clinicians to offer better support to their patients.”
Similarly, Director of CVSSP, Professor Adrian Hilton applauded the incredible potential of Professor Barnaghi’s research. In his view, Machine learning could provide improved care for people living with dementia to remain at home and free up bed space at hospitals.
The experiment was part of the TIHM (Technology Integrated Health Management) for dementia project, led by Surrey and Borders Partnership NHS Foundation. The project was part of the NHS Test Beds Programme and funded by NHS England the Office for Life Sciences.
According to Professor Helen Rostill, Director of Innovation and Development at the Foundation, the aim has been to create an Internet of Things led system that uses machine learning to alert clinicians of potential health problems.
By Teiko Daitey