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Systemic Periodontal Risk Score Using an Innovative Machine Learning Strategy: An Observational Study

Early diagnosis is crucial for individuals who are susceptible to tooth-supporting tissue diseases (e.g., periodontitis) that may lead to tooth loss, so as to prevent systemic implications and maintain quality of life. The aim of this study was to propose a personalized explainable machine learning...

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Autores principales: Monsarrat, Paul, Bernard, David, Marty, Mathieu, Cecchin-Albertoni, Chiara, Doumard, Emmanuel, Gez, Laure, Aligon, Julien, Vergnes, Jean-Noël, Casteilla, Louis, Kemoun, Philippe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8879877/
https://www.ncbi.nlm.nih.gov/pubmed/35207705
http://dx.doi.org/10.3390/jpm12020217
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author Monsarrat, Paul
Bernard, David
Marty, Mathieu
Cecchin-Albertoni, Chiara
Doumard, Emmanuel
Gez, Laure
Aligon, Julien
Vergnes, Jean-Noël
Casteilla, Louis
Kemoun, Philippe
author_facet Monsarrat, Paul
Bernard, David
Marty, Mathieu
Cecchin-Albertoni, Chiara
Doumard, Emmanuel
Gez, Laure
Aligon, Julien
Vergnes, Jean-Noël
Casteilla, Louis
Kemoun, Philippe
author_sort Monsarrat, Paul
collection PubMed
description Early diagnosis is crucial for individuals who are susceptible to tooth-supporting tissue diseases (e.g., periodontitis) that may lead to tooth loss, so as to prevent systemic implications and maintain quality of life. The aim of this study was to propose a personalized explainable machine learning algorithm, solely based on non-invasive predictors that can easily be collected in a clinic, to identify subjects at risk of developing periodontal diseases. To this end, the individual data and periodontal health of 532 subjects was assessed. A machine learning pipeline combining a feature selection step, multilayer perceptron, and SHapley Additive exPlanations (SHAP) explainability, was used to build the algorithm. The prediction scores for healthy periodontium and periodontitis gave final F1-scores of 0.74 and 0.68, respectively, while gingival inflammation was harder to predict (F1-score of 0.32). Age, body mass index, smoking habits, systemic pathologies, diet, alcohol, educational level, and hormonal status were found to be the most contributive variables for periodontal health prediction. The algorithm clearly shows different risk profiles before and after 35 years of age and suggests transition ages in the predisposition to developing gingival inflammation or periodontitis. This innovative approach to systemic periodontal disease risk profiles, combining both ML and up-to-date explainability algorithms, paves the way for new periodontal health prediction strategies.
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spelling pubmed-88798772022-02-26 Systemic Periodontal Risk Score Using an Innovative Machine Learning Strategy: An Observational Study Monsarrat, Paul Bernard, David Marty, Mathieu Cecchin-Albertoni, Chiara Doumard, Emmanuel Gez, Laure Aligon, Julien Vergnes, Jean-Noël Casteilla, Louis Kemoun, Philippe J Pers Med Article Early diagnosis is crucial for individuals who are susceptible to tooth-supporting tissue diseases (e.g., periodontitis) that may lead to tooth loss, so as to prevent systemic implications and maintain quality of life. The aim of this study was to propose a personalized explainable machine learning algorithm, solely based on non-invasive predictors that can easily be collected in a clinic, to identify subjects at risk of developing periodontal diseases. To this end, the individual data and periodontal health of 532 subjects was assessed. A machine learning pipeline combining a feature selection step, multilayer perceptron, and SHapley Additive exPlanations (SHAP) explainability, was used to build the algorithm. The prediction scores for healthy periodontium and periodontitis gave final F1-scores of 0.74 and 0.68, respectively, while gingival inflammation was harder to predict (F1-score of 0.32). Age, body mass index, smoking habits, systemic pathologies, diet, alcohol, educational level, and hormonal status were found to be the most contributive variables for periodontal health prediction. The algorithm clearly shows different risk profiles before and after 35 years of age and suggests transition ages in the predisposition to developing gingival inflammation or periodontitis. This innovative approach to systemic periodontal disease risk profiles, combining both ML and up-to-date explainability algorithms, paves the way for new periodontal health prediction strategies. MDPI 2022-02-04 /pmc/articles/PMC8879877/ /pubmed/35207705 http://dx.doi.org/10.3390/jpm12020217 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Monsarrat, Paul
Bernard, David
Marty, Mathieu
Cecchin-Albertoni, Chiara
Doumard, Emmanuel
Gez, Laure
Aligon, Julien
Vergnes, Jean-Noël
Casteilla, Louis
Kemoun, Philippe
Systemic Periodontal Risk Score Using an Innovative Machine Learning Strategy: An Observational Study
title Systemic Periodontal Risk Score Using an Innovative Machine Learning Strategy: An Observational Study
title_full Systemic Periodontal Risk Score Using an Innovative Machine Learning Strategy: An Observational Study
title_fullStr Systemic Periodontal Risk Score Using an Innovative Machine Learning Strategy: An Observational Study
title_full_unstemmed Systemic Periodontal Risk Score Using an Innovative Machine Learning Strategy: An Observational Study
title_short Systemic Periodontal Risk Score Using an Innovative Machine Learning Strategy: An Observational Study
title_sort systemic periodontal risk score using an innovative machine learning strategy: an observational study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8879877/
https://www.ncbi.nlm.nih.gov/pubmed/35207705
http://dx.doi.org/10.3390/jpm12020217
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