Abstract
AbstractUrinary 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.
Funder
International Urogynecological Association
Philanthropic donor
Bundesministerium für Bildung und Forschung
Sächsisches Staatsministerium für Wissenschaft und Kunst
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Statistics, Probability and Uncertainty,Computer Science Applications,Education,Information Systems,Statistics and Probability
Reference45 articles.
1. Foxman, B. Epidemiology of urinary tract infections: incidence, morbidity, and economic costs. Am J Med 113(Suppl 1A), 5S–13S, https://doi.org/10.1016/s0002-9343(02)01054-9 (2002).
2. Hooton, T. M. Recurrent urinary tract infection in women. Int J Antimicrob Agents 17, 259–268, https://doi.org/10.1016/s0924-8579(00)00350-2 (2001).
3. NHS could slash emergency admission costs with better use of medical technology. The Medical Technology Group https://www.mtg.org.uk/wp-content/uploads/2016/07/Admissions-of-Failure-report-release-FINAL-131115-1.pdf (2015).
4. Lodise, T. P., Chopra, T., Nathanson, B. H. & Sulham, K. Hospital admission patterns of adult patients with complicated urinary tract infections who present to the hospital by disease acuity and comorbid conditions: How many admissions are potentially avoidable? Am J Infect Control 49, 1528–1534, https://doi.org/10.1016/j.ajic.2021.05.013 (2021).
5. Simmering, J. E., Tang, F., Cavanaugh, J. E., Polgreen, L. A. & Polgreen, P. M. The Increase in Hospitalizations for Urinary Tract Infections and the Associated Costs in the United States, 1998-2011. Open Forum Infect Dis 4, ofw281, https://doi.org/10.1093/ofid/ofw281 (2017).