A clinical microscopy dataset to develop a deep learning diagnostic test for urinary tract infection.
Sci Data
; 11(1): 155, 2024 Feb 01.
Article
in En
| MEDLINE
| ID: mdl-38302487
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Urinary Tract Infections
/
Deep Learning
Type of study:
Diagnostic_studies
/
Guideline
Limits:
Humans
Language:
En
Journal:
Sci Data
Year:
2024
Document type:
Article
Affiliation country: