RESUMO
In this second episode of the Microbiologist in the Clinic series, clinicians and laboratory scientists share their perspectives about a 75-year-old woman who was diagnosed with asymptomatic bacteriuria based on positive urine cultures. The patient and her GP are concerned about this laboratory finding as the patient will become immunosuppressed with planned chemotherapy. The patient has had an overactive bladder (OAB) for approximately 20 years, with good control of her urinary urgency and frequency (no incontinence) with a stable dose of OAB medication. The challenges of this clinical presentation are discussed, with evidence for evaluation and treatment.
RESUMO
Urinary tract infection (UTI) is a common disorder. Its diagnosis can be made by microscopic examination of voided urine for markers of infection. This manual technique is technically difficult, time-consuming and prone to inter-observer errors. The application of computer vision to this domain has been slow due to the lack of a clinical image dataset from UTI patients. We present an open dataset containing 300 images and 3,562 manually annotated urinary cells labelled into seven classes of clinically significant cell types. It is an enriched dataset acquired from the unstained and untreated urine of patients with symptomatic UTI using a simple imaging system. We demonstrate that this dataset can be used to train a Patch U-Net, a novel deep learning architecture with a random patch generator to recognise urinary cells. Our hope is, with this dataset, UTI diagnosis will be made possible in nearly all clinical settings by using a simple imaging system which leverages advanced machine learning techniques.