Spatio-temporal based deep learning for rapid detection and identification of bacterial colonies through lens-free microscopy time-lapses.

ABSTRACT: Detection and identification of pathogenic bacteria isolated from biological samples (blood, urine, sputum, etc.) are crucial steps in accelerated clinical diagnosis. However, accurate and rapid identification remain difficult to achieve due to the challenge of having to analyse complex and large samples. Current solutions (mass spectrometry, automated biochemical testing, etc.) propose a trade-off between time and accuracy, achieving satisfactory results at the expense of time-consuming processes, which can also be intrusive, destructive and costly. Moreover, those techniques tend to require an overnight subculture on solid agar medium delaying bacteria identification by 12-48 hours, thus preventing rapid prescription of appropriate treatment as it hinders antibiotic susceptibility testin

SUBMITTER: Paquin P 

PROVIDER: S-EPMC9931332 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature