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

Abstract 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 0.84 (95%CI:0.75–0.93) and 0.90 (95%CI:0.81-1.00) and specificity between 0.66 (95%CI: 0.57–0.74) and 0.99 (95%CI: 0.98-1.00) for the various conditions. These findings demonstrate the marked potential of machine learning based automated microscopy and pH test of vaginal swabs for improving the first-line evaluation of five different types of infectious and non-infectious vaginal disorders, hopefully resulting in better treatment, decreasing healthcare costs, and an improvement in patients’ quality of life.