Liou, Natasha;
De, Trina;
Urbanski, Adrian;
Chieng, Catherine;
Kong, Qingyang;
David, Anna L;
Khasriya, Rajvinder;
... Horsley, Harry; + view all
(2024)
A clinical microscopy dataset to develop a deep learning diagnostic test for urinary tract infection.
Scientific Data
, 11
, Article 155. 10.1038/s41597-024-02975-0.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | A clinical microscopy dataset to develop a deep learning diagnostic test for urinary tract infection |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/s41597-024-02975-0 |
| Publisher version: | http://dx.doi.org/10.1038/s41597-024-02975-0 |
| Language: | English |
| Additional information: | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| Keywords: | Science & Technology, Multidisciplinary Sciences, Science & Technology - Other Topics, DEFENSES, MODEL |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Renal Medicine UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health > Maternal and Fetal Medicine |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10188945 |
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