Development of a periodontitis risk assessment model for primary care providers in an interdisciplinary setting

Shimpi, Neel; McRoy, Susan; Zhao, Huimin; Wu, Min; Acharya, Amit

doi:10.3233/thc-191642

Cited by 20 publications

(18 citation statements)

References 22 publications

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“…Periodontitis is widely prevalent in many countries and causes a major global social and economic impact. It is urgent and crucial to prevent and control periodontitis [ 4 , 36 ]. Microbiome and environmental factors have been considered as potential risk factors for periodontitis [ 37 ].…”

Section: Discussionmentioning

confidence: 99%

RETRACTED ARTICLE: VEGF as a potential molecular target in periodontitis: a meta-analysis and microarray data validation

Ren

Feng

et al. 2021

J Inflamm

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Background Anti-vascular endothelial growth factor (VEGF) has been used as a therapeutic drug for the treatment of some human diseases. However, no systematic evidence is performed for assessing the role of VEGF in periodontitis. We carried out a comprehensive analysis to explore the role of VEGF in patients with periodontitis. Methods Multiple databases were searched for eligible studies. The pooled standardized mean difference (SMD) and odds ratio (OR) with the corresponding 95% confidence interval (CI) were applied to evaluate the effect sizes. Clinical data validation from microarray analysis was used. Pathway and process enrichment analysis were also investigated. Results Finally, 16 studies were included in this analysis. Overall, there was a significantly higher level of VEGF expression in periodontitis than in healthy control groups (OR = 16.64, 95% CI = 6.01–46.06, P < 0.001; SMD = 2.25, 95% CI = 1.25–3.24, P < 0.001). Subgroup analysis of ethnicity showed that VEGF expression was still correlated with periodontitis in the Asian and European populations. No correlation was observed between VEGF expression and age, gender, and pathological type. A large clinical sample data (427 periodontitis patients and 136 healthy controls) further validated that VEGF expression was higher in periodontitis than in healthy control groups (P = 0.023). VEGF was involved in many functions such as blood vessel development, response to growth factor, cell proliferation, and cell adhesion. Conclusions High levels of VEGF were credible implications for the development of periodontitis. Anti-VEGF therapy may be valuable for the treatment of periodontitis in clinical management.

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Section: Discussionmentioning

confidence: 99%

RETRACTED ARTICLE: VEGF as a potential molecular target in periodontitis: a meta-analysis and microarray data validation

Ren

Feng

et al. 2021

J Inflamm

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“…The fourth application was the prediction of treatment outcomes, for example, for implants [56][57][58] and using predictors like trabeculae microstructure parameters, 57 insertion torque curve, 56 patients' health condition, 58 or for periodontally affected teeth. 59 For the latter, a range of clinical parameters has been employed, 60,61 while biosample data like saliva has only been infrequently used. 62 Again, models which combine the wealth of social and routinely collected data (on probing pocket depths, attachment level, mobility, etc.…”

Section: Discussionmentioning

confidence: 99%

Deep learning in periodontology and oral implantology: A scoping review

Mohammad‐Rahimi

Motamedian

Pirayesh

et al. 2022

J of Periodontal Research

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Deep learning (DL) has been employed for a wide range of tasks in dentistry. We aimed to systematically review studies employing DL for periodontal and implantological purposes. A systematic electronic search was conducted on four databases (Medline via PubMed, Google Scholar, Scopus, and Embase) and a repository (ArXiv) for publications after 2010, without any limitation on language. In the present review, we included studies that reported deep learning models' performance on periodontal or oral implantological tasks. Given the heterogeneities in the included studies, no meta‐analysis was performed. The risk of bias was assessed using the QUADAS‐2 tool. We included 47 studies: focusing on imaging data (n = 20) and non‐imaging data in periodontology (n = 12), or dental implantology (n = 15). The detection of periodontitis and gingivitis or periodontal bone loss, the classification of dental implant systems, or the prediction of treatment outcomes in periodontology and implantology were major use cases. The performance of the models was generally high. However, it varied given the employed methods (which includes various types of convolutional neural networks (CNN) and multi‐layered perceptron (MLP)), the variety in specific modeling tasks, as well as the chosen and reported outcomes, outcome measures and outcome level. Only a few studies (n = 7) showed a low risk of bias across all assessed domains. A growing number of studies evaluated DL for periodontal or implantological objectives. Heterogeneity in study design, poor reporting and a high risk of bias severely limit the comparability of studies and the robustness of the overall evidence.

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“…The objective of our study was to develop a prediction model to noninvasively screen EHR data of patients with no existing diagnosis for dysglycemia seen in the dental setting to identify presence of relative risk of DM. Predictive modeling using ML algorithms has been previously applied in a number of studies for risk determination of various health conditions [ 42 , 43 ]. A systematic review suggested that the ML algorithms applied in our study aligned with those applied in similar risk prediction modeling studies undertaken by other researchers [ 38 ].…”

Section: Discussionmentioning

confidence: 99%

Development of non-invasive diabetes risk prediction models as decision support tools designed for application in the dental clinical environment

Shimpi

Panny

Glurich

et al. 2019

Informatics in Medicine Unlocked

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The objective was to develop a predictive model using medical-dental data from an integrated electronic health record (iEHR) to identify individuals with undiagnosed diabetes mellitus (DM) in dental settings. Retrospective data retrieved from Marshfield Clinic Health System’s data-warehouse was pre-processed prior to conducting analysis. A subset was extracted from the preprocessed dataset for external evaluation (N validation ) of derived predictive models. Further, subsets of 30%–70%, 40%–60% and 50%–50% case-to-control ratios were created for training/testing. Feature selection was performed on all datasets. Four machine learning (ML) classifiers were evaluated: logistic regression (LR), multilayer perceptron (MLP), support vector machines (SVM) and random forests (RF). Model performance was evaluated on N validation . We retrieved a total of 5319 cases and 36,224 controls. From the initial 116 medical and dental features, 107 were used after performing feature selection. RF applied to the 50%–50% case-control ratio outperformed other predictive models over N validation achieving a total accuracy (94.14%), sensitivity (0.941), specificity (0.943), F-measure (0.941), Mathews-correlation-coefficient (0.885) and area under the receiver operating curve (0.972). Future directions include incorporation of this predictive model into iEHR as a clinical decision support tool to screen and detect patients at risk for DM triggering follow-ups and referrals for integrated care delivery between dentists and physicians.

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Development of a periodontitis risk assessment model for primary care providers in an interdisciplinary setting

Cited by 20 publications

References 22 publications

RETRACTED ARTICLE: VEGF as a potential molecular target in periodontitis: a meta-analysis and microarray data validation

RETRACTED ARTICLE: VEGF as a potential molecular target in periodontitis: a meta-analysis and microarray data validation

Deep learning in periodontology and oral implantology: A scoping review

Development of non-invasive diabetes risk prediction models as decision support tools designed for application in the dental clinical environment

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