Diagnostic performance of an automated microscopy and pH test for diagnosis of vaginitis.

Vaginitis is a common gynecological problem, nevertheless, its clinical evaluation is often insufficient. This study evaluated the performance of an automated microscope for the diagnosis of vaginitis, by comparison of the investigated test results to a composite reference standard (CRS) of wet mount microscopy performed by a specialist in vulvovaginal disorders, and related laboratory tests. During this single-site cross-sectional prospective study, 226 women reporting vaginitis symptoms were recruited, of which 192 samples were found interpretable and were assessed by the automated microscopy system. Results showed sensitivity between 84.1% (95%CI: 73.67-90.86%) for Candida albicans and 90.9% (95%CI: 76.43-96.86%) for bacterial vaginosis and specificity between 65.9% (95%CI: 57.11-73.64%) for Candida albicans and 99.4% (95%CI: 96.89-99.90%) for cytolytic vaginosis. These findings demonstrate the marked potential of machine learning-based automated microscopy and an automated pH test of vaginal swabs as a basis for a computer-aided suggested diagnosis, for improving the first-line evaluation of five different types of infectious and non-infectious vaginal disorders (vaginal atrophy, bacterial vaginosis, Candida albicans vaginitis, cytolytic vaginosis, and aerobic vaginitis/desquamative inflammatory vaginitis). Using such a tool will hopefully lead to better treatment, decrease healthcare costs, and improve patients' quality of life.