Image analysis and artificial intelligence in infectious disease diagnostics.

BACKGROUND Microbiologists are valued for their time-honed skills in image analysis including identification of pathogens and inflammatory context in Gram stains, ova and parasite preparations, blood smears, and histopathological slides. They also must classify colonial growth on a variety of agar plates for triage and workup. Recent advances in image analysis, in particular application of artificial intelligence (AI), have the potential to automate these processes and support more timely and accurate diagnoses. OBJECTIVES To review current artificial intelligence-based image analysis as applied to clinical microbiology and discuss future trends in the field. SOURCES Material sourced for this review included peer-reviewed literature annotated in the PubMed or Google Scholar databases and preprint articles from bioRxiv. Articles describing use of AI for analysis of images used in infectious disease diagnostics were reviewed. CONTENT We describe application of machine learning towards analysis of different types of microbiological image data. Specifically, we outline progress in smear and plate interpretation and potential for AI diagnostic applications in the clinical microbiology laboratory. IMPLICATIONS Combined with automation, we predict that AI algorithms will be used in the future to pre-screen and pre-classify image data, thereby increasing productivity and enabling more accurate diagnoses through collaboration between the AI and microbiologist. Once developed, image-based AI analysis is inexpensive and amenable to local and remote diagnostic use.