Potential use of artificial intelligence for vaginal swab analysis in the assessment of common genital disorders: a pilot study.

Abstract

Genital disorders, such as vulvo-vaginal candidiasis (VVC), bacterial vaginosis (BV), and aerobic vaginitis (AV), are very common among fertile women and negatively impact their reproductive and relational life. Vaginal culture can help in the diagnostic workflow of these conditions. Recently, culture-based techniques have taken advantages of up-front specimen processing units, which also include a digital imaging system to record images of plates at programmable time points. In this proof-of-concept study, we assessed the characteristics of digital plate images of vaginal swabs plated by WASPLab system into different media, in order to detect microbial growth morphotypes specific for each genital disorder. A total of 104 vaginal specimens were included: 62 cases of normal lactobacilli-dominated flora, 12 of BV, 16 of VVC, and 14 of AV were analysed. Vaginal specimens were plated by WASPLab system into different chromogenic media and blood agar plates. Plate images were taken automatically by the digital imager at 38 h post-inoculation. We found that each genital condition was characterized by specific morphotypes in terms of microbial growth and colony colour, thus allowing the potential use of artificial intelligence not only to assess the presence of specific microbial genera/species but also to 'categorize' peculiar clinical conditions.