Performance of deep learning models in predicting the nugent score to diagnose bacterial vaginosis

Abstract

Bacterial vaginosis is a global health issue affecting women, causing symptoms such as abnormal vaginal discharge and discomfort. The Nugent score is a standard method for diagnosing bacterial vaginosis and is based on the manual interpretation of Gram-stained vaginal smears. However, this method relies on the skill and experience of trained professionals, leading to variability in results and poses significant challenges for settings with limited access to experienced technicians. The results of this study indicate that deep learning models can predict the Nugent score with high accuracy, offering the potential to standardize the diagnosis of bacterial vaginosis. By reducing observer variability, these models can facilitate reliable diagnoses, even in settings where experienced personnel are scarce. Although validation is needed on a larger scale, our results suggest that deep learning models may represent a new approach for diagnosing bacterial vaginosis.