Using artificial intelligence to reduce diagnostic workload without compromising detection of urinary tract infections.

ABSTRACT:

Background

A substantial proportion of microbiological screening in diagnostic laboratories is due to suspected urinary tract infections (UTIs), yet approximately two thirds of urine samples typically yield negative culture results. By reducing the number of query samples to be cultured and enabling diagnostic services to concentrate on those in which there are true microbial infections, a significant improvement in efficiency of the service is possible.

Methodology

Screening process for urine samples prior to culture was modelled in a single clinical microbiology laboratory covering three hospitals and community services across Bristol and Bath, UK. Retrospective analysis of all urine microscopy, culture, and sensitivity reports over one year was used to compare two methods

SUBMITTER: Burton RJ 

PROVIDER: S-EPMC6708133 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature