{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T12:23:12Z","timestamp":1784550192602,"version":"3.55.0"},"reference-count":30,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T00:00:00Z","timestamp":1761868800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University of Oradea, Romania"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Dental caries remains one of the most prevalent chronic diseases worldwide, driven by complex interactions among dietary, hygienic, and biological factors. This study introduces a hybrid predictive framework that integrates mathematical modelling and artificial intelligence (AI) to estimate individual caries risk based on daily sugar intake, oral hygiene index, salivary pH, fluoride exposure, age, and sex. A first-order balance differential equation was applied to simulate demineralisation\u2013remineralisation dynamics, while a feed-forward artificial neural network (ANN) was trained on simulated and literature-derived datasets. The hybrid model demonstrated strong predictive performance, achieving 91.2% accuracy and an AUC of 0.98 in classifying individuals into low-, moderate-, and high-risk categories. Sensitivity analysis identified sugar intake and oral hygiene as dominant determinants, while fluoride and salivary pH showed protective effects. These findings highlight the feasibility of combining mechanistic and data-driven approaches to enhance early risk assessment and support the development of intelligent, personalised screening tools in preventive dentistry.<\/jats:p>","DOI":"10.3390\/bioengineering12111190","type":"journal-article","created":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T13:22:36Z","timestamp":1762176156000},"page":"1190","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Mathematical and AI-Based Predictive Modelling for Dental Caries Risk Using Clinical and Behavioural Parameters"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7701-2307","authenticated-orcid":false,"given":"Liliana","family":"Sachelarie","sequence":"first","affiliation":[{"name":"Department of Clinical Discipline, Apollonia University, 700511 Iasi, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2643-761X","authenticated-orcid":false,"given":"Ioana","family":"Scrobota","sequence":"additional","affiliation":[{"name":"Department of Dental Medicine, Faculty of Medicine and Pharmacy, University of Oradea, 10 1st Decembrie Street, 410073 Oradea, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roxana Alexandra","family":"Cristea","sequence":"additional","affiliation":[{"name":"Doctoral School of Biomedical Science, University of Oradea, No. 1 University Street, 410087 Oradea, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7507-5072","authenticated-orcid":false,"given":"Ramona","family":"Hodi\u0219an","sequence":"additional","affiliation":[{"name":"Department of Preclinical Disciplines, Faculty of Medicine and Pharmacy, University of Oradea, 410068 Oradea, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mihail","family":"Pantor","sequence":"additional","affiliation":[{"name":"Department of Dental Medicine, Faculty of Medicine and Pharmacy, University of Oradea, 10 1st Decembrie Street, 410073 Oradea, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1417-5963","authenticated-orcid":false,"given":"Gabriela","family":"Ciavoi","sequence":"additional","affiliation":[{"name":"Department of Dental Medicine, Faculty of Medicine and Pharmacy, University of Oradea, 10 1st Decembrie Street, 410073 Oradea, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,31]]},"reference":[{"key":"ref_1","first-page":"661","article-title":"The global burden of dental diseases and challenges for oral health","volume":"83","author":"Petersen","year":"2005","journal-title":"Bull. World Health Organ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1177\/0022034515573272","article-title":"Global burden of untreated caries: A systematic review and modeling analysis","volume":"94","author":"Kassebaum","year":"2015","journal-title":"J. Dent. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"55","DOI":"10.11149\/jkaoh.2020.44.1.55","article-title":"Prediction of dental caries in 12-year-old children using machine-learning algorithms","volume":"44","author":"Yang","year":"2020","journal-title":"J. Korean Acad. Oral Health"},{"key":"ref_4","first-page":"233","article-title":"Developing Children\u2019s Oral Health Assessment Toolkits Using Machine Learning Algorithm","volume":"5","author":"Wang","year":"2020","journal-title":"JDR Clin. Trans. Res."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kang, I.A., Njimbouom, S.N., and Kim, J.D. (2023). Optimal Feature Selection-Based Dental Caries Prediction Model Using Machine Learning for Decision Support System. Bioengineering, 10.","DOI":"10.20944\/preprints202301.0304.v1"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/bs.adcom.2019.09.007","article-title":"Energy-Efficient Edge-Based Real-Time Healthcare Support System","volume":"Volume 117","author":"Abirami","year":"2020","journal-title":"Advances in Computers"},{"key":"ref_7","first-page":"523","article-title":"Scope and challenges of machine learning-based diagnosis and prognosis in clinical dentistry: A literature review","volume":"7","author":"Reyes","year":"2021","journal-title":"J. Clin. Transl. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"25","DOI":"10.4103\/jmss.jmss_7_24","article-title":"Impact of Artificial Intelligence in Endodontics: Precision, Predictions, and Prospects","volume":"14","author":"Parinitha","year":"2024","journal-title":"J. Med. Signals Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ramos-Gomez, F., Marcus, M., Maida, C.A., Wang, Y., Kinsler, J.J., Xiong, D., Lee, S.Y., Hays, R.D., Shen, J., and Crall, J.J. (2021). Using a Machine Learning Algorithm to Predict the Likelihood of Presence of Dental Caries among Children Aged 2 to 7. Dent. J., 9.","DOI":"10.3390\/dj9120141"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"409","DOI":"10.15386\/mpr-2696","article-title":"Artificial Intelligence and Medical Specialties: Support or Substitution?","volume":"97","author":"Popa","year":"2024","journal-title":"Med. Pharm. Rep."},{"key":"ref_11","first-page":"191","article-title":"An Automated Machine Learning Classifier for Early Childhood Caries","volume":"43","author":"Karhade","year":"2021","journal-title":"Pediatr. Dent."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"22","DOI":"10.54276\/JUPD.2024.3105","article-title":"Deep learning concept for early dental caries detection","volume":"3","author":"Kotha","year":"2024","journal-title":"J. Updates Pediatr. Dent."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kong, H.J., and Kim, Y.L. (2024). Application of artificial intelligence in dental crown prosthesis: A scoping review. BMC Oral Health, 24.","DOI":"10.1186\/s12903-024-04657-0"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1111\/jopr.13805","article-title":"Artificial intelligence systems in dental shade-matching: A systematic review","volume":"33","author":"Shetty","year":"2024","journal-title":"J. Prosthodont."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lee, S., Oh, S.-I., Jo, J., Kang, S., Shin, Y., and Park, J.-W. (2021). Deep learning for early dental caries detection in bitewing radiographs. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-96368-7"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"You, W., Hao, A., Li, S., Wang, Y., and Xia, B. (2020). Deep learning-based dental plaque detection on primary teeth: A comparison with clinical assessments. BMC Oral Health, 20.","DOI":"10.1186\/s12903-020-01114-6"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"103425","DOI":"10.1016\/j.jdent.2020.103425","article-title":"Detecting caries lesions of different radiographic extension on bitewings using deep learning","volume":"100","author":"Cantu","year":"2020","journal-title":"J. Dent."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.prosdent.2021.05.008","article-title":"Artificial intelligence applications in implant dentistry: A systematic review","volume":"129","author":"Vyas","year":"2023","journal-title":"J. Prosthet. Dent."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Al Kuwaiti, A., Nazer, K., Al-Reedy, A., Al-Shehri, S., Al-Muhanna, A., Subbarayalu, A.V., Al Muhanna, D., and Al-Muhanna, F.A. (2023). A review of the role of artificial intelligence in healthcare. J. Pers. Med., 13.","DOI":"10.3390\/jpm13060951"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Alsyouf, A., Lutfi, A., Alsubahi, N., Alhazmi, F.N., Al-Mugheed, K., Anshasi, R.J., Alharbi, N.I., and Albugami, M. (2023). The use of a technology acceptance model (TAM) to predict patients\u2019 usage of a personal health record system: The role of security, privacy, and usability. Int. J. Environ. Res. Public Health, 20.","DOI":"10.3390\/ijerph20021347"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sukegawa, S., Yoshii, K., Hara, T., Yamashita, K., Nakano, K., Yamamoto, N., Nagatsuka, H., and Furuki, Y. (2020). Deep neural networks for dental implant system classification. Biomolecules, 10.","DOI":"10.3390\/biom10070984"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1007\/s11282-021-00572-0","article-title":"Diagnostic charting of panoramic radiography using deep-learning artificial intelligence system","volume":"38","author":"Basaran","year":"2022","journal-title":"Oral Radiol."},{"key":"ref_23","first-page":"182","article-title":"Multi oral disease classification from panoramic radiograph using transfer learning and XGBoost","volume":"13","author":"Jaiswal","year":"2022","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e946676","DOI":"10.12659\/MSM.946676","article-title":"Artificial Intelligence in Dentistry: A Narrative Review of Diagnostic and Therapeutic Applications","volume":"31","author":"Gao","year":"2025","journal-title":"Med. Sci. Monit."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"\u00c7ift\u00e7i, S., and A\u015fanto\u011frol, O. (2024). Utilization of machine learning models in predicting caries risk groups and oral health-related risk factors in adults. BMC Oral Health, 24.","DOI":"10.1186\/s12903-024-04210-z"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","article-title":"Identifying medical diagnoses and treatable diseases by image-based deep learning","volume":"172","author":"Kermany","year":"2018","journal-title":"Cell"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"100160","DOI":"10.1016\/j.dentre.2025.100160","article-title":"Generative Artificial Intelligence in Dentistry: A Narrative Review of Current Approaches and Future Challenges","volume":"5","author":"Villena","year":"2025","journal-title":"Dent. Rev."},{"key":"ref_28","first-page":"736","article-title":"Scoping Review of Artificial Intelligence and Immersive Digital Tools in Dental Education","volume":"86","author":"Saghiri","year":"2021","journal-title":"AADS Proc."},{"key":"ref_29","first-page":"1188","article-title":"The Use of Artificial Intelligence and Game-Based Learning in Removable Partial Denture Design: A Comparative Study","volume":"87","author":"Mahrous","year":"2023","journal-title":"AADS Proc."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1111\/eje.12973","article-title":"Use of Artificial Intelligence Software in Dental Education: A Study on Assisted Proximal Caries Assessment in Bitewing Radiographs","volume":"28","author":"Schropp","year":"2023","journal-title":"Eur. J. Dent. 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