Predicting oral malodour based on the microbiota in saliva samples using a deep learning approach - Yoshio Nakano; Nao Suzuki; Fumiyuki Kuwata (BMC, 2018)
Abstract Background Oral malodour is mainly caused by volatile sulphur compounds produced by bacteria and bacterial interactions. It is difficult to predict the presence or absence of oral malodour based on the abundances of specific species and their combinations. This paper presents an effective way of deep learning approach to predicting the oral malodour from salivary microbiota. Methods The 16S rRNA genes from saliva samples of 90 subjects (45 had no or weak oral malodour, and 45 had marked oral malodour) were amplified, and gene sequence analysis was carried out. Deep learning classified oral malodour and healthy breath based on the resultant abundances of operational taxonomic units (OTUs) Results A discrimination classifier model was constructed by profiling OTUs and calculating their relative abundance in saliva samples from 90 subjects. Our deep learning model achieved a predictive accuracy of 97%, compared to the 79% obtained with a support vector machine. Conclusion This approach is expected to be useful in screening the saliva for prediction of oral malodour before visits to specialist clinics.