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Polizzi A, Quinzi V, Lo Giudice A, Marzo G, Leonardi R, Isola G. Accuracy of Artificial Intelligence Models in the Prediction of Periodontitis: A Systematic Review. JDR Clin Trans Res 2024; 9:312-324. [PMID: 38589339 DOI: 10.1177/23800844241232318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2024] Open
Abstract
INTRODUCTION Periodontitis is the main cause of tooth loss and is related to many systemic diseases. Artificial intelligence (AI) in periodontics has the potential to improve the accuracy of risk assessment and provide personalized treatment planning for patients with periodontitis. This systematic review aims to examine the actual evidence on the accuracy of various AI models in predicting periodontitis. METHODS Using a mix of MeSH keywords and free text words pooled by Boolean operators ('AND', 'OR'), a search strategy without a time frame setting was conducted on the following databases: Web of Science, ProQuest, PubMed, Scopus, and IEEE Explore. The QUADAS-2 risk of bias assessment was then performed. RESULTS From a total of 961 identified records screened, 8 articles were included for qualitative analysis: 4 studies showed an overall low risk of bias, 2 studies an unclear risk, and the remaining 2 studies a high risk. The most employed algorithms for periodontitis prediction were artificial neural networks, followed by support vector machines, decision trees, logistic regression, and random forest. The models showed good predictive performance for periodontitis according to different evaluation metrics, but the presented methods were heterogeneous. CONCLUSIONS AI algorithms may improve in the future the accuracy and reliability of periodontitis prediction. However, to date, most of the studies had a retrospective design and did not consider the most modern deep learning networks. Although the available evidence is limited by a lack of standardized data collection and protocols, the potential benefits of using AI in periodontics are significant and warrant further research and development in this area. KNOWLEDGE TRANSFER STATEMENT The use of AI in periodontics can lead to more accurate diagnosis and treatment planning, as well as improved patient education and engagement. Despite the current challenges and limitations of the available evidence, particularly the lack of standardized data collection and analysis protocols, the potential benefits of using AI in periodontics are significant and warrant further research and development in this area.
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Affiliation(s)
- A Polizzi
- Department of General Surgery and Surgical-Medical Specialties, School of Dentistry, University of Catania, Catania, Italy
| | - V Quinzi
- Department of Life, Health and Environmental Sciences, University of L'Aquila, L'Aquila, Abruzzo, Italy
| | - A Lo Giudice
- Department of General Surgery and Surgical-Medical Specialties, School of Dentistry, University of Catania, Catania, Italy
| | - G Marzo
- Department of Life, Health and Environmental Sciences, University of L'Aquila, L'Aquila, Abruzzo, Italy
| | - R Leonardi
- Department of General Surgery and Surgical-Medical Specialties, School of Dentistry, University of Catania, Catania, Italy
| | - G Isola
- Department of General Surgery and Surgical-Medical Specialties, School of Dentistry, University of Catania, Catania, Italy
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Boitor O, Stoica F, Mihăilă R, Stoica LF, Stef L. Automated Machine Learning to Develop Predictive Models of Metabolic Syndrome in Patients with Periodontal Disease. Diagnostics (Basel) 2023; 13:3631. [PMID: 38132215 PMCID: PMC10743072 DOI: 10.3390/diagnostics13243631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 12/04/2023] [Accepted: 12/06/2023] [Indexed: 12/23/2023] Open
Abstract
Metabolic syndrome is experiencing a concerning and escalating rise in prevalence today. The link between metabolic syndrome and periodontal disease is a highly relevant area of research. Some studies have suggested a bidirectional relationship between metabolic syndrome and periodontal disease, where one condition may exacerbate the other. Furthermore, the existence of periodontal disease among these individuals significantly impacts overall health management. This research focuses on the relationship between periodontal disease and metabolic syndrome, while also incorporating data on general health status and overall well-being. We aimed to develop advanced machine learning models that efficiently identify key predictors of metabolic syndrome, a significant emphasis being placed on thoroughly explaining the predictions generated by the models. We studied a group of 296 patients, hospitalized in SCJU Sibiu, aged between 45-79 years, of which 57% had metabolic syndrome. The patients underwent dental consultations and subsequently responded to a dedicated questionnaire, along with a standard EuroQol 5-Dimensions 5-Levels (EQ-5D-5L) questionnaire. The following data were recorded: DMFT (Decayed, Missing due to caries, and Filled Teeth), CPI (Community Periodontal Index), periodontal pockets depth, loss of epithelial insertion, bleeding after probing, frequency of tooth brushing, regular dental control, cardiovascular risk, carotid atherosclerosis, and EQ-5D-5L score. We used Automated Machine Learning (AutoML) frameworks to build predictive models in order to determine which of these risk factors exhibits the most robust association with metabolic syndrome. To gain confidence in the results provided by the machine learning models provided by the AutoML pipelines, we used SHapley Additive exPlanations (SHAP) values for the interpretability of these models, from a global and local perspective. The obtained results confirm that the severity of periodontal disease, high cardiovascular risk, and low EQ-5D-5L score have the greatest impact in the occurrence of metabolic syndrome.
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Affiliation(s)
- Ovidiu Boitor
- Dental Medicine Research Center, Faculty of Medicine, “Lucian Blaga” University, 550024 Sibiu, Romania;
| | - Florin Stoica
- Department of Mathematics and Informatics, Research Center in Informatics and Information Technology, Faculty of Sciences, “Lucian Blaga” University, 550024 Sibiu, Romania;
| | - Romeo Mihăilă
- Department of Internal Medicine, Faculty of Medicine, “Lucian Blaga” University, 550024 Sibiu, Romania;
| | - Laura Florentina Stoica
- Department of Mathematics and Informatics, Research Center in Informatics and Information Technology, Faculty of Sciences, “Lucian Blaga” University, 550024 Sibiu, Romania;
| | - Laura Stef
- Department of Oral Health, Dental Medicine Research Center, Faculty of Medicine, “Lucian Blaga” University, 550024 Sibiu, Romania;
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Enevold C, Nielsen CH, Christensen LB, Kongstad J, Fiehn NE, Hansen PR, Holmstrup P, Havemose-Poulsen A, Damgaard C. Suitability of machine learning models for prediction of clinically defined Stage III/IV periodontitis from questionnaires and demographic data in Danish cohorts. J Clin Periodontol 2023. [PMID: 37691160 DOI: 10.1111/jcpe.13874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 07/14/2023] [Accepted: 08/17/2023] [Indexed: 09/12/2023]
Abstract
AIM To evaluate if, and to what extent, machine learning models can capture clinically defined Stage III/IV periodontitis from self-report questionnaires and demographic data. MATERIALS AND METHODS Self-reported measures of periodontitis, demographic data and clinically established Stage III/IV periodontitis status were extracted from two Danish population-based cohorts (The Copenhagen Aging and Midlife Biobank [CAMB] and The Danish Health Examination Survey [DANHES]) and used to develop cross-validated machine learning models for the prediction of clinically established Stage III/IV periodontitis. Models were trained using 10-fold cross-validations repeated three times on the CAMB dataset (n = 1476), and the resulting models were validated in the DANHES dataset (n = 3585). RESULTS The prevalence of Stage III/IV periodontitis was 23.2% (n = 342) in the CAMB dataset and 9.3% (n = 335) in the DANHES dataset. For the prediction of clinically established Stage III/IV periodontitis in the CAMB cohort, models reached area under the receiver operating characteristics (AUROCs) of 0.67-0.69, sensitivities of 0.58-0.64 and specificities of 0.71-0.80. In the DANHES cohort, models derived from the CAMB cohort achieved AUROCs of 0.64-0.70, sensitivities of 0.44-0.63 and specificities of 0.75-0.84. CONCLUSIONS Applying cross-validated machine learning algorithms to demographic data and self-reported measures of periodontitis resulted in models with modest capabilities for the prediction of Stage III/IV periodontitis in two Danish cohorts.
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Affiliation(s)
- C Enevold
- Institute for Inflammation Research, Center for Rheumatology and Spine Diseases, Copenhagen University Hospital, Copenhagen, Denmark
| | - C H Nielsen
- Institute for Inflammation Research, Center for Rheumatology and Spine Diseases, Copenhagen University Hospital, Copenhagen, Denmark
- Research Area Periodontology, Section for Oral Biology and Immunopathology, Department of Odontology, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - L B Christensen
- Research Area Periodontology, Section for Oral Biology and Immunopathology, Department of Odontology, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - J Kongstad
- Research Area Periodontology, Section for Oral Biology and Immunopathology, Department of Odontology, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - N E Fiehn
- Costerton Biofilm Centre, Department of Immunology and Microbiology, University of Copenhagen, Copenhagen, Denmark
| | - P R Hansen
- Department of Cardiology, Herlev-Gentofte Hospital, Hellerup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - P Holmstrup
- Research Area Periodontology, Section for Oral Biology and Immunopathology, Department of Odontology, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - A Havemose-Poulsen
- Research Area Periodontology, Section for Oral Biology and Immunopathology, Department of Odontology, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - C Damgaard
- Research Area Periodontology, Section for Oral Biology and Immunopathology, Department of Odontology, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
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Troiano G, Nibali L, Petsos H, Eickholz P, Saleh MHA, Santamaria P, Jian J, Shi S, Meng H, Zhurakivska K, Wang HL, Ravidà A. Development and international validation of logistic regression and machine-learning models for the prediction of 10-year molar loss. J Clin Periodontol 2023; 50:348-357. [PMID: 36305042 DOI: 10.1111/jcpe.13739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 10/21/2022] [Accepted: 10/24/2022] [Indexed: 12/13/2022]
Abstract
AIM To develop and validate models based on logistic regression and artificial intelligence for prognostic prediction of molar survival in periodontally affected patients. MATERIALS AND METHODS Clinical and radiographic data from four different centres across four continents (two in Europe, one in the United States, and one in China) including 515 patients and 3157 molars were collected and used to train and test different types of machine-learning algorithms for their prognostic ability of molar loss over 10 years. The following models were trained: logistic regression, support vector machine, K-nearest neighbours, decision tree, random forest, artificial neural network, gradient boosting, and naive Bayes. In addition, different models were aggregated by means of the ensembled stacking method. The primary outcome of the study was related to the prediction of overall molar loss (MLO) in patients after active periodontal treatment. RESULTS The general performance in the external validation settings (aggregating three cohorts) revealed that the ensembled model, which combined neural network and logistic regression, showed the best performance among the different models for the prediction of MLO with an area under the curve (AUC) = 0.726. The neural network model showed the best AUC of 0.724 for the prediction of periodontitis-related molar loss. In addition, the ensembled model showed the best calibration performance. CONCLUSIONS Through a multi-centre collaboration, both prognostic models for the prediction of molar loss were developed and externally validated. The ensembled model showed the best performance in terms of both discrimination and validation, and it is made freely available to clinicians for widespread use in clinical practice.
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Affiliation(s)
- Giuseppe Troiano
- Department of Clinical and Experimental Medicine, University of Foggia, Foggia, Italy
| | - Luigi Nibali
- Periodontology Unit, Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King's College London, London, UK
| | - Hari Petsos
- Department of Periodontology, Center for Dentistry and Oral Medicine (Carolinum), Johann Wolfgang Goethe-University Frankfurt/Main, Frankfurt am Main, Germany
| | - Peter Eickholz
- Department of Periodontology, Center for Dentistry and Oral Medicine (Carolinum), Johann Wolfgang Goethe-University Frankfurt/Main, Frankfurt am Main, Germany
| | - Muhammad H A Saleh
- Department of Periodontics and Oral Medicine, University of Michigan School of Dentistry, Ann Arbor, Michigan, USA
| | - Pasquale Santamaria
- Periodontology Unit, Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King's College London, London, UK
| | - Jao Jian
- Department of Periodontology, National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Peking University School and Hospital of Stomatology, Beijing, China
| | - Shuwen Shi
- Department of Periodontology, National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Peking University School and Hospital of Stomatology, Beijing, China
| | - Huanxin Meng
- Department of Periodontology, National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Peking University School and Hospital of Stomatology, Beijing, China
| | - Khrystyna Zhurakivska
- Department of Clinical and Experimental Medicine, University of Foggia, Foggia, Italy
| | - Hom-Lay Wang
- Department of Periodontics and Oral Medicine, University of Michigan School of Dentistry, Ann Arbor, Michigan, USA
| | - Andrea Ravidà
- Department of Periodontics and Oral Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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