Technology-Enabled Care

Infection of the urinary tract, which comprises bladder, urethra, and kidneys, affects the health and wellbeing of 150 million people each year globally. If bacteria from faeces enter the urinary tract, a urinary tract infection (UTI) is likely. Poor hygiene, kidney stones, weakened immune system, physical disabilities, and urinary catheterisation are among the factors that increase the risk of infection. When detected early and correctly, UTI can be treated with antibiotics. However, any delay in the diagnosis or a misdiagnosis can lead to severe health conditions, e.g., sepsis, kidney damage, and in the worst case, death. Annually, urosepsis leads to 1.6M deaths in USA and EU.

The timely diagnosis of a UTI is not trivial. People typically see a medical professional only after they notice clear symptoms of a UTI, e.g., cloudy or smelly urine, or experience pain and increased frequency of urination. By that time the infection may be well developed. The gold standard for the diagnosis of a UTI is based on the laboratory analysis of urine but that can take up to 48 hours. In consequence, the diagnosis and treatment decisions are often based on presenting symptoms. Hospital admissions for UTI’s have increased significantly in all age groups.  

Our aim is to work with stakeholders to co-design new tools and machine learning-based methods for early recognition of urinary tract infections to enable longer and healthier independent living. 

Our working hypotheses are

• There are measurable indicators of a UTI in the movement, behaviour, and interaction patterns.
• An AI-enabled agent with conversational skills will facilitate the recognition of a UTI, which otherwise cannot be extracted.  
• These indicators can be analysed and in the case of a UTI, the person and/or their carers can be alerted in a timely manner.

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