Adjunctive dental therapies in caries-active children: Shifting the cariogenic salivary microbiome from dysbiosis towards non-cariogenic health

Lyashenko, Claudia; Herrman, Elisa; Irwin, Jessica H.; James, Allie; Strauss, Shay; Warner, John S.; Khor, Brandon; Snow, Michael; Ortiz, Stephanie; Waid, Erin; Nasry, Bishoy; Chai, Jennifer; Choong, Carissa; Palmer, Elizabeth; Kutsch, Kim; Forsyth, Anna; Choi, Dong‐Hee; Maier, Tom; Machida, Curtis A.

doi:10.1016/j.humic.2020.100077

Cited by 3 publications

(4 citation statements)

References 32 publications

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“…Utilizing AI algorithms with salivary biomarkers to generate models that can be validated and applied potentially in clinical practice is more common than its use for biomarker discovery (Arias-Bujanda et al, 2020;Banavar et al, 2021;Bostanci et al, 2018;Carnielli et al, 2018;da Costa et al, 2022;de Dumast et al, 2018;Eriksson et al, 2022;Gomez Hernandez et al, 2021;Grier et al, 2021;Kim et al, 2021;Kistenev et al, 2018;Koller et al, 2021;Koopaie et al, 2021;Kouznetsova et al, 2021;Lee et al, 2021;Liu, Tong, et al, 2021;Lyashenko et al, 2020;Monedeiro et al, 2021;Nakano et al, 2014Nakano et al, , 2018Pang et al, 2021;Schulte et al, 2020;Shoukri et al, 2019;Song et al, 2020;Sonis et al, 2013;Tamaki et al, 2009;Winck et al, 2015;Wu et al, 2021;Zhang et al, 2021;Zhou et al, 2021;Zlotogorski-Hurvitz et al, 2019) AI-based biomarker platforms constructed during biomarker discovery may be executed for biomarker validation on the premise that each additional saliva sample would be subjected to similar large-scale analysis and used as input for the same models (Adeoye, Wan, et al, 2022). While this may not be cost-effective, feasible, or encourage validation, promising biomarkers selected using conventional statistical approaches or AI-based exploratory analysis (in biomarker discovery) may be used to develop new models for biomarker validation and potential clinical application (Koopaie et al, 2021;Tamaki et al, 2009).…”

Section: Ai Models In Salivary Biomarker Validationmentioning

confidence: 99%

“…Also, their AUCs were from 0.71 to 0.93 (Table 2). However, similar to the use of intelligent models for oral cancer, many studies had insufficient sample sizes to meet a minimum event‐per‐variable cutoff of twenty considering the number of predictive salivary biomarkers available for model construction (Grier et al, 2021; Koller et al, 2021; Koopaie et al, 2021; Lyashenko et al, 2020; Wu et al, 2021). These models will also benefit from retraining using larger sample sizes and external validation in the future.…”

Section: Current Applications Of Artificial Intelligence For Salivary...mentioning

confidence: 99%

“…Utilizing AI algorithms with salivary biomarkers to generate models that can be validated and applied potentially in clinical practice is more common than its use for biomarker discovery (Arias‐Bujanda et al, 2020; Banavar et al, 2021; Bostanci et al, 2018; Carnielli et al, 2018; Chen, Chen, et al, 2021; Chen, Wu, et al, 2021; da Costa et al, 2022; de Dumast et al, 2018; Eriksson et al, 2022; Gomez Hernandez et al, 2021; Grier et al, 2021; Kim et al, 2021; Kistenev et al, 2018; Koller et al, 2021; Koopaie et al, 2021; Kouznetsova et al, 2021; Lee et al, 2021; Li et al, 2021; Liu, Tong, et al, 2021; Lyashenko et al, 2020; Monedeiro et al, 2021; Nakano et al, 2014, 2018; Pang et al, 2021; Schulte et al, 2020; Shoukri et al, 2019; Song et al, 2020; Sonis et al, 2013; Tamaki et al, 2009; Winck et al, 2015; Wu et al, 2021; Zhang et al, 2021; Zhou et al, 2021; Zlotogorski‐Hurvitz et al, 2019). Many of the AI‐assisted salivary biomarker platforms would constitute supervised learning models implemented with structured data since disease or event indicators are often less taxing to annotate (with fewer biomarkers) before model training.…”

Section: Implementing Ai‐assisted Saliva Liquid Biopsy For Oral and M...mentioning

confidence: 99%

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Artificial intelligence in salivary biomarker discovery and validation for oral diseases

Adeoye

2023

Oral Diseases

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Salivary biomarkers can improve the efficacy, efficiency, and timeliness of oral and maxillofacial disease diagnosis and monitoring. Oral and maxillofacial conditions in which salivary biomarkers have been utilized for disease‐related outcomes include periodontal diseases, dental caries, oral cancer, temporomandibular joint dysfunction, and salivary gland diseases. However, given the equivocal accuracy of salivary biomarkers during validation, incorporating contemporary analytical techniques for biomarker selection and operationalization from the abundant multi‐omics data available may help improve biomarker performance. Artificial intelligence represents one such advanced approach that may optimize the potential of salivary biomarkers to diagnose and manage oral and maxillofacial diseases. Therefore, this review summarized the role and current application of techniques based on artificial intelligence for salivary biomarker discovery and validation in oral and maxillofacial diseases.

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Section: Ai Models In Salivary Biomarker Validationmentioning

confidence: 99%

Section: Current Applications Of Artificial Intelligence For Salivary...mentioning

confidence: 99%

“…Utilizing AI algorithms with salivary biomarkers to generate models that can be validated and applied potentially in clinical practice is more common than its use for biomarker discovery (Arias‐Bujanda et al, 2020; Banavar et al, 2021; Bostanci et al, 2018; Carnielli et al, 2018; Chen, Chen, et al, 2021; Chen, Wu, et al, 2021; da Costa et al, 2022; de Dumast et al, 2018; Eriksson et al, 2022; Gomez Hernandez et al, 2021; Grier et al, 2021; Kim et al, 2021; Kistenev et al, 2018; Koller et al, 2021; Koopaie et al, 2021; Kouznetsova et al, 2021; Lee et al, 2021; Li et al, 2021; Liu, Tong, et al, 2021; Lyashenko et al, 2020; Monedeiro et al, 2021; Nakano et al, 2014, 2018; Pang et al, 2021; Schulte et al, 2020; Shoukri et al, 2019; Song et al, 2020; Sonis et al, 2013; Tamaki et al, 2009; Winck et al, 2015; Wu et al, 2021; Zhang et al, 2021; Zhou et al, 2021; Zlotogorski‐Hurvitz et al, 2019). Many of the AI‐assisted salivary biomarker platforms would constitute supervised learning models implemented with structured data since disease or event indicators are often less taxing to annotate (with fewer biomarkers) before model training.…”

Section: Implementing Ai‐assisted Saliva Liquid Biopsy For Oral and M...mentioning

confidence: 99%

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Artificial intelligence in salivary biomarker discovery and validation for oral diseases

Adeoye

2023

Oral Diseases

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“…class label). Among the conventional ML methods, logistic regression (LR), RF ( Man et al, 2019 ), and support vector machine (SVM) ( Lyashenko et al, 2020 ) are the most frequently used supervised learning algorithms. Although ML algorithms such as RFs can handle a large number of features, their accuracy can still be limited in complex datasets ( Statnikov et al, 2013 ).…”

Section: Overview Of Machine Learningmentioning

confidence: 99%

Machine learning: a powerful tool for identifying key microbial agents associated with specific cancer types

Feng,

Yang,

Liu

et al. 2023

PeerJ

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Machine learning (ML) includes a broad class of computer programs that improve with experience and shows unique strengths in performing tasks such as clustering, classification and regression. Over the past decade, microbial communities have been implicated in influencing the onset, progression, metastasis, and therapeutic response of multiple cancers. Host-microbe interaction may be a physiological pathway contributing to cancer development. With the accumulation of a large number of high-throughput data, ML has been successfully applied to the study of human cancer microbiomics in an attempt to reveal the complex mechanism behind cancer. In this review, we begin with a brief overview of the data sources included in cancer microbiomics studies. Then, the characteristics of the ML algorithm are briefly introduced. Secondly, the application progress of ML in cancer microbiomics is also reviewed. Finally, we highlight the challenges and future prospects facing ML in cancer microbiomics. On this basis, we conclude that the development of cancer microbiomics can not be achieved without ML, and that ML can be used to develop tumor-targeting microbial therapies, ultimately contributing to personalized and precision medicine.

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Microbiome Changes in Children Treated under General Anesthesia for Severe Early Childhood Caries: Pilot Study

et al. 2022

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A full-mouth radical dental treatment under general anesthesia is a common approach for treating severe early childhood caries (S-ECC). However, previous study showed recurrence of the disease in 80% of cases within 12 months. The aim of the present study was to examine the changes in microbial composition of the dental biofilm of these children following treatment. Dental biofilm samples from five children (mean age 45.4 ± 10.1 months) were taken before and three months after treatment and analyzed for microbial composition using Next Generation Sequencing of the microbial DNA extracted from these samples. Although some reductions in the abundance of caries-pathogenic bacteria (e.g., Streptococcus mutans, Streptococcus sobrinus, Rothia dentocariosa and Scardovia wiggisiae) were seen in the post-treatment follow up samples, these reductions were for the most part not statistically significant, and these bacteria remained well above detection levels. Taken together, the results of the present pilot study suggest that the dental treatment alone is not enough to reduce the caries risk status of these children and that a more comprehensive approach should be considered.

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Adjunctive dental therapies in caries-active children: Shifting the cariogenic salivary microbiome from dysbiosis towards non-cariogenic health

Cited by 3 publications

References 32 publications

Artificial intelligence in salivary biomarker discovery and validation for oral diseases

Artificial intelligence in salivary biomarker discovery and validation for oral diseases

Machine learning: a powerful tool for identifying key microbial agents associated with specific cancer types

Microbiome Changes in Children Treated under General Anesthesia for Severe Early Childhood Caries: Pilot Study

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