{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T16:37:08Z","timestamp":1792859828421,"version":"4.5.0"},"reference-count":17,"publisher":"S. Karger AG","license":[{"start":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T00:00:00Z","timestamp":1791590400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T00:00:00Z","timestamp":1791590400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Caries Res"],"abstract":"<jats:p>Introduction: Although preventable, dental caries remains a global public health challenge due to its multifactorial nature and the deep influence of socio-structural determinants. This study evaluates the performance of artificial intelligence (AI) models as decision-support frameworks for population-level risk stratification, screening, and public health planning. Methods: We analyzed a representative sample (N = 23,726) from the SB Brasil 2023 national survey, stratified into three age cohorts: children (12 years), adolescents (15\u201319 years), and adults (35\u201344 years). Eleven predictors, encompassing socioeconomic (wealth score via PCA, household density, race, water access), psychosocial (self-rated oral health status), behavioral, and clinical domains, were used to predict caries presence (\u22651 untreated decayed tooth) and severity (DMFT index regression). To account for overdispersion and the high frequency of zero counts, we utilized advanced count-based regressions, including Zero-Inflated Negative Binomial (ZINB) and Negative Binomial models. Performance was assessed using AUC, mean absolute error (MAE), and decision curve analysis (DCA) via 5-fold cross-validation. Results: Caries prevalence increased progressively with age (36.8\u201352.7%). The non-clinical triage models demonstrated significant net benefit in DCA across all groups, effectively identifying high-risk individuals for prioritization without requiring clinical examination. For severity prediction, the ZINB model identified structural signals of inequity, particularly in late adolescence (15\u201319 years), where socio-structural determinants explained a meaningful proportion of clinical variance (R2 = 0.1010; MAE = 2.79). Across all stages, SHAP analysis reinforced that wealth score and household density are the primary structural predictors of caries risk. Conclusion: AI provides a feasible framework for population-level risk stratification and public health planning. The findings suggest that AI-driven frameworks should be utilized for managerial triage and resource allocation, enabling proportional universalism by identifying high-risk clusters through structural determinants rather than individual-level clinical diagnostics.<\/jats:p>","DOI":"10.1159\/cre\/abuag004","type":"journal-article","created":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T11:11:14Z","timestamp":1788520274000},"page":"1-1","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning for Predicting Dental Caries Presence and Severity: An Age-Cohort Perspective from a Nationally Representative Survey in Brazil"],"prefix":"10.1159","author":[{"given":"Jessye Melgarejo do Amaral","family":"Giordani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Orlando","family":"Luiz do Amaral Junior","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria Laura","family":"Braccini Fagundes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juliana Balbinot","family":"Hilgert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2222-7719","authenticated-orcid":false,"given":"Fernando Neves","family":"Hugo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"127","published-online":{"date-parts":[[2026,10,10]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(19)31146-8"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1159\/000545156"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1177\/0022034511402079"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1034\/j.1600-0528.2000.028006399.x"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1111\/j.1600-0528.2012.00680.x"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1186\/s12903-024-04073-4"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1111\/ger.12432"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2023.41625"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdent.2025.106164"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1023\/a:1010933404324"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1177\/0272989X06295361"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1111\/j.1600-0528.2007.00354.x"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1037\/0033-2909.97.1.129"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1093\/ije\/14.1.32"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0252873"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1111\/idj.12294"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1111\/j.1600-0528.2007.00332.x"}],"container-title":["Caries Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/karger.com\/article-pdf\/doi\/10.1159\/cre\/abuag004","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T16:01:46Z","timestamp":1792857706000},"score":1,"resource":{"primary":{"URL":"https:\/\/karger.com\/article\/doi\/10.1159\/cre\/abuag004"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,10]]},"references-count":17,"URL":"https:\/\/doi.org\/10.1159\/cre\/abuag004","archive":["Portico"],"relation":{},"ISSN":["0008-6568","1421-976X"],"issn-type":[{"value":"0008-6568","type":"print"},{"value":"1421-976X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,10]]}}}