Predicting oral malodour based on the microbiota in saliva samples using a deep learning approach

Nakano, Yoshio; Suzuki, Nao; Kuwata, Fumiyuki

doi:10.1186/s12903-018-0591-6

Cited by 35 publications

(28 citation statements)

References 18 publications

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“…In turn, the presence of bacteria, such as Firmicutes, Proteobacteria, Bacteroidetes, Actinobacteria, and Fusobacteria, was demonstrated in both the samples from examined and control groups. Firmicutes was the most abundant phylum in saliva samples from both groups [110,111].…”

Section: Chemical Compound Bacteriamentioning

confidence: 95%

The Role of Oral Microbiota in Intra-Oral Halitosis

Hampelska

Jaworska

Babalska

et al. 2020

JCM

102

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Halitosis is a common ailment concerning 15% to 60% of the human population. Halitosis can be divided into extra-oral halitosis (EOH) and intra-oral halitosis (IOH). The IOH is formed by volatile compounds, which are produced mainly by anaerobic bacteria. To these odorous substances belong volatile sulfur compounds (VSCs), aromatic compounds, amines, short-chain fatty or organic acids, alcohols, aliphatic compounds, aldehydes, and ketones. The most important VSCs are hydrogen sulfide, dimethyl sulfide, dimethyl disulfide, and methyl mercaptan. VSCs can be toxic for human cells even at low concentrations. The oral bacteria most related to halitosis are Actinomyces spp., Bacteroides spp., Dialister spp., Eubacterium spp., Fusobacterium spp., Leptotrichia spp., Peptostreptococcus spp., Porphyromonas spp., Prevotella spp., Selenomonas spp., Solobacterium spp., Tannerella forsythia, and Veillonella spp. Most bacteria that cause halitosis are responsible for periodontitis, but they can also affect the development of oral and digestive tract cancers. Malodorous agents responsible for carcinogenesis are hydrogen sulfide and acetaldehyde.

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Section: Chemical Compound Bacteriamentioning

confidence: 95%

The Role of Oral Microbiota in Intra-Oral Halitosis

Hampelska

Jaworska

Babalska

et al. 2020

JCM

102

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“…In the prediction of oral odor, the model was constructed and predicted by using 108 significantly different bacteria found in saliva samples, which can reach 78.9% accuracy. Among those bacteria, it was found that Bacteroides, Prevotella and Porphyromonas had the most discriminatory validity on oral odor [38]. In the prediction of oral and oropharyngeal carcinoma, the AUC values predicted by seven biomarkers such as Rothia, Haemophilus and Capnocytophaga of oral microbiota could even reach 90%-100% [39].…”

Section: Discussionmentioning

confidence: 97%

Pregnant women with gestational diabetes mellitus exhibit unique features in oral microbiome

Zheng

Ma³

et al. 2019

Preprint

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Background: Gestational diabetes mellitus (GDM) leads to a series of adverse pregnancy outcomes, seriously endangering the health of mothers and infants. Oral glucose tolerance test (OGTT) is the gold standard test for GDM diagnosis, but largely increases the discomfort of pregnant women, and is inconvenient to follow-up and detection. Considering that oral sampling is convenient, rapid, safe and non-invasive, the feasibility of distinguishing GDM via biomarkers from oral microbiota was evaluated in this study. Here, the 16S rRNA gene sequencing was used to compare the microbiome of saliva and dental plaque samples of 111 pregnant women, and to analyze the structure of oral microbiota in patients with GDM as well as to find effective biomarkers. Results: The results showed that the microbiota of both types of oral samples in patients with GDM changed, and it was quite different from that of neither periodontitis nor dental caries. By using bacterial biomarkers from oral microbiota, GDM classification models based on SVM and random forest algorithms were constructed. For the SVM algorithm, the AUC value of the classification model constructed by the combination of dental plaque Lautropia , Neisseria and saliva Veillonella achieved 0.83. For the random forest algorithm, the maximum AUC value of the model constructed by the dental plaque Streptococcus , Eikenella , Anoxybacillus and saliva Leptotrichia , Kingella was larger than 0.90. Conclusions: These findings revealed that certain bacteria of either saliva or dental plaque can accurately distinguish GDM from healthy pregnant women, which provides a potential non-invasive approach for GDM diagnosis with oral microbial markers.

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“…The remaining 16 papers were fully examined. The high-level data from Table 1 [45] Dataset study Tenfold Genus Obesity [46] New methods Tenfold, 10 times Species 5 body sites 24 environments Colorectal cancer [47] New methods Tenfold Genus Classification of body site Classification of subjects Classification of disease states [48] Dataset study Tenfold Species Colorectal Cancer (CRC) and Colorectal Adenoma (CRA) [49] Dataset study NA Genus Crohn's Disease (CD) [50] Dataset study Tenfold, 10 times Species Colorectal Polyps [51] Dataset study Leave-one-out Species Colorectal Adenoma Colorectal Cancer [52] Dataset study Fivefold, 5 times Genus Type 2 diabetes (T2D) Liver Cirrhosis Rheumatoid Arthritis (RA) [53] Dataset study Leave-one-out Genus Oral malodour…”

Section: Discussionmentioning

confidence: 99%

“…Among these, the work by Zhou and Gallins [17] is a review. Although it has been excluded from the study, it provided four new articles [48,52,53,85] to be added in full-text to the list of the eligible papers. Also, the paper [18] has been excluded as it is also a review.…”

Section: Selection Of Sources Of Evidencementioning

confidence: 99%

Gut microbiota and artificial intelligence approaches: A scoping review

et al. 2020

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This article aims to provide a thorough overview of the use of Artificial Intelligence (AI) techniques in studying the gut microbiota and its role in the diagnosis and treatment of some important diseases. The association between microbiota and diseases, together with its clinical relevance, is still difficult to interpret. The advances in AI techniques, such as Machine Learning (ML) and Deep Learning (DL), can help clinicians in processing and interpreting these massive data sets. Two research groups have been involved in this Scoping Review, working in two different areas of Europe: Florence and Sarajevo. The papers included in the review describe the use of ML or DL methods applied to the study of human gut microbiota. In total, 1109 papers were considered in this study. After elimination, a final set of 16 articles was considered in the scoping review. Different AI techniques were applied in the reviewed papers. Some papers applied ML, while others applied DL techniques. 11 papers evaluated just different ML algorithms (ranging from one to eight algorithms applied to one dataset). The remaining five papers examined both ML and DL algorithms. The most applied ML algorithm was Random Forest and it also exhibited the best performances.

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Predicting oral malodour based on the microbiota in saliva samples using a deep learning approach

Cited by 35 publications

References 18 publications

The Role of Oral Microbiota in Intra-Oral Halitosis

The Role of Oral Microbiota in Intra-Oral Halitosis

Pregnant women with gestational diabetes mellitus exhibit unique features in oral microbiome

Gut microbiota and artificial intelligence approaches: A scoping review

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