Salivary cystatin S levels in children with early childhood caries in comparison with caries-free children; statistical analysis and machine learning

Abstract

Background Early childhood caries is the most common infectious disease in childhood, with a high prevalence in developing countries. The assessment of the variables that influence early childhood caries as well as its pathophysiology leads to improved control of this disease. Cystatin S, as one of the salivary proteins, has an essential role in pellicle formation, tooth re-mineralization, and protection. The present study aims to assess salivary cystatin S levels and demographic data in early childhood caries in comparison with caries-free ones using statistical analysis and machine learning methods. Methods A cross-sectional, case–control study was undertaken on 20 cases of early childhood caries and 20 caries-free children as a control. Unstimulated whole saliva samples were collected by suction. Cystatin S concentrations in samples were determined using human cystatin S ELISA kit. The checklist was collected from participants about demographic characteristics, oral health status, and dietary habits by interviewing parents. Regression and receiver operating characteristic (ROC) curve analysis were done to evaluate the potential role of cystatin S salivary level and demographic using statistical analysis and machine learning. Results The mean value of salivary cystatin S concentration in the early childhood caries group was 191.55 ± 81.90 (ng/ml) and in the caries-free group was 370.06 ± 128.87 (ng/ml). T-test analysis showed a statistically significant difference between early childhood caries and caries-free groups in salivary cystatin S levels (p = 0.032). Investigation of the area under the curve (AUC) and accuracy of the ROC curve revealed that the logistic regression model based on salivary cystatin S levels and birth weight had the most and acceptable potential for discriminating of early childhood caries from caries-free controls. Furthermore, using salivary cystatin S levels enhanced the capability of machine learning methods to differentiate early childhood caries from caries-free controls. Conclusion Salivary cystatin S levels in caries-free children were higher than the children with early childhood caries. Results of the present study suggest that considering clinical examination, demographic and socioeconomic factors, along with the salivary cystatin S levels, could be usefull for early diagnosis ofearly childhood caries in high-risk children; furthermore, cystatin S is a protective factor against dental caries.

Artificial Intelligence Its Uses and Application in Pediatric Dentistry: A Review

“…AI models developed for application in pedodontics have mainly focused on: dental plaque on primary teeth ( n = 1) [ 21 ], ECC ( n = 6) [ 25 , 26 , 27 , 28 , 29 , 30 ], fissure sealant categorization ( n = 1) [ 31 ], mesiodens and supernumerary tooth identification ( n = 6) [ 3 , 10 , 22 , 23 , 24 , 41 ], chronological age assessment ( n = 4) [ 32 , 33 , 37 , 38 ], identification of deciduous and young permanent teeth ( n = 3) [ 34 , 35 , 39 ], children’s oral Health ( n = 2) [ 7 , 20 ], and ectopic eruption ( n = 2) [ 39 , 40 ] ( Figure 3 ).…”

“…Koopaie, M. et al [ 27 ] used statistical analysis and machine learning approaches to compare the levels of salivary cystatin S and demographic data between ECC patients with caries-free patients. Different types of supervised learning models, including feed-forward neural networks, XGBoost, Random Forest, and Support Vector Machines (SVM), were used in this study.…”

“…Articles without complete texts, narrative reviews, scoping reviews, letters to the editor, opinion letters, case reports, brief communications, conference proceedings, and non-English language articles (84 articles) were all omitted ( Figure 1 ). Finally, only 25 papers met the qualifying requirements [ 3 , 7 , 10 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ].…”

Artificial intelligence in salivary biomarker discovery and validation for oral diseases

“…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). In this proce...…”

“…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.…”