{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T12:37:52Z","timestamp":1793277472930,"version":"4.5.0"},"reference-count":40,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T00:00:00Z","timestamp":1775520000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Research Project Funding of Zhejiang Chinese Medical University","award":["2024RCZXZK16"],"award-info":[{"award-number":["2024RCZXZK16"]}],"id":[{"id":"https:\/\/ror.org\/04epb4p87","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Research Project of Education Department of Zhejiang Province","award":["Y202455076"],"award-info":[{"award-number":["Y202455076"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82501131"],"award-info":[{"award-number":["82501131"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Dent Res"],"abstract":"<jats:p>\n                    The high prevalence of periodontitis has imposed a significant global disease burden. Epidemiologic surveys rely on full-mouth periodontal examination (FMPE) or partial-mouth periodontal examination (PMPE). While FMPE is resource-intensive, the efficiency of PMPE remains questionable. Moreover, the value of subpopulation-tailored PMPE protocols is still unassessed. To address these gaps, we developed an interpretable, data-driven framework that ranks the importance of teeth using SHapley Additive exPlanations (SHAP) values from machine learning models. Using data from the National Health and Nutrition Examination Survey 2009\u20132014, XGBoost (XGB) and LightGBM (LGB) were trained on maximum interproximal probing depth and clinical attachment loss across 28 teeth from adults aged 35 y and older. Absolute SHAP values were aggregated separately for each model to calculate global tooth importance. The top 10 teeth were then evaluated and benchmarked against the Community Periodontal Index (CPI) and modified Ramfjord protocol. Primary outcomes included quadratic weighted kappa (QWK) for diagnostic agreement with FMPE and the inflation factor (IF) for prevalence estimation bias. The XGB-derived protocol (FDI 47, 27, 17, 26, 37, 16, 42, 46, 36, 41) achieved QWK\u2009=\u20090.85 and IF\u2009=\u2009127.29% in the external validation set, with significantly lower IF than CPI (\n                    <jats:italic toggle=\"yes\">P<\/jats:italic>\n                    \u2009=\u20090.003), indicating its potential to replace CPI. Further optimization for age- and gender-specific protocols occasionally yielded localized improvements, such as males aged 55 to 64\u2009y (teeth 17, 16, 27, 47, 26, 33, 45, 37, 43, 25), while the unified XGB-derived protocol remained robust across all strata. These findings support the general use of a unified, data-driven protocol for large-scale epidemiological surveys. SHAP-guided tooth selection offers an interpretable and efficient alternative to traditional PMPE, bridging the gap between accuracy and feasibility in large-scale periodontal surveys.\n                  <\/jats:p>","DOI":"10.1177\/00220345261432570","type":"journal-article","created":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T06:15:04Z","timestamp":1775628904000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Data-Driven Tooth Selection Enhances Partial-Mouth Periodontal Screening"],"prefix":"10.1177","author":[{"given":"N.","family":"Lin","sequence":"first","affiliation":[{"name":"School of Stomatology, Zhejiang Chinese Medical University, The Stomatology Hospital of Zhejiang Chinese Medical University, Hangzhou, People\u2019s Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C.","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Stomatology, Zhejiang Chinese Medical University, The Stomatology Hospital of Zhejiang Chinese Medical University, Hangzhou, People\u2019s Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Y.","family":"Shan","sequence":"additional","affiliation":[{"name":"School of Stomatology, Zhejiang Chinese Medical University, The Stomatology Hospital of Zhejiang Chinese Medical University, Hangzhou, People\u2019s Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6951-1218","authenticated-orcid":false,"given":"Z.","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Stomatology, Zhejiang Chinese Medical University, The Stomatology Hospital of Zhejiang Chinese Medical University, Hangzhou, People\u2019s Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,4,7]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"crossref","unstructured":"Alawaji YN Mostafa N Carvalho RM Alshammari A Aleksejuniene J. 2022. Accuracy and precision of using partial-mouth recordings to study the prevalence extent and risk associations of untreated periodontitis. Saudi Dent J. 34(2):142\u2013149. https:\/\/doi.org\/10.1016\/j.sdentj.2021.12.005","DOI":"10.1016\/j.sdentj.2021.12.005"},{"key":"e_1_3_2_3_1","doi-asserted-by":"crossref","unstructured":"Albandar JM. 2002. Global risk factors and risk indicators for periodontal diseases. Periodontol 2000. 29(1):177\u2013206. https:\/\/doi.org\/10.1034\/j.1600-0757.2002.290109.x","DOI":"10.1034\/j.1600-0757.2002.290109.x"},{"key":"e_1_3_2_4_1","doi-asserted-by":"crossref","unstructured":"Albandar JM. 2011. Underestimation of periodontitis in NHANES surveys. J Periodontol. 82(3):337\u2013341. https:\/\/doi.org\/10.1902\/jop.2011.100638","DOI":"10.1902\/jop.2011.100638"},{"key":"e_1_3_2_5_1","doi-asserted-by":"crossref","unstructured":"Alsulaiman AA et al. 2018. Incisor malalignment and the risk of periodontal disease progression. Am J Orthod Dentofacial Orthop. 153(4):512\u2013522. https:\/\/doi.org\/10.1016\/j.ajodo.2017.08.015","DOI":"10.1016\/j.ajodo.2017.08.015"},{"key":"e_1_3_2_6_1","unstructured":"Centers for Disease Control and Prevention. 2025. Age adjustment - health United States. National Center for Health Statistics; [updated 2025 Jun 9; accessed 2025 Oct 24]. https:\/\/www.cdc.gov\/nchs\/hus\/sources-definitions\/age-adjustment.htm"},{"key":"e_1_3_2_7_1","doi-asserted-by":"crossref","unstructured":"Chilukoti SV Shan L Tida VS Maida AS Hei X. 2024. A reliable diabetic retinopathy grading via transfer learning and ensemble learning with quadratic weighted kappa metric. BMC Med Inf Decis Making. 24(1):37. https:\/\/doi.org\/10.1186\/s12911-024-02446-x","DOI":"10.1186\/s12911-024-02446-x"},{"key":"e_1_3_2_8_1","doi-asserted-by":"crossref","unstructured":"D\u00edaz-Vallejo M et al. 2024. Analyses of the variable selection using correlation methods: an approach to the importance of statistical inferences in the modelling process. Ecol Modell. 498:110893. https:\/\/doi.org\/10.1016\/j.ecolmodel.2024.110893","DOI":"10.1016\/j.ecolmodel.2024.110893"},{"key":"e_1_3_2_9_1","doi-asserted-by":"crossref","unstructured":"Efthimiou O et al. 2024. Developing clinical prediction models: a step-by-step guide. BMJ. 386:e078276. https:\/\/doi.org\/10.1136\/bmj-2023-078276","DOI":"10.1136\/bmj-2023-078276"},{"key":"e_1_3_2_10_1","unstructured":"Eickholz P et al. 2025. Prevalence of the periodontal status in Germany: results of the 6th German Oral Health Study (DMS \u2022 6). Quintessence Int. 56(11):S40\u2013S47. https:\/\/doi.org\/10.3290\/j.qi.b5981979"},{"key":"e_1_3_2_11_1","doi-asserted-by":"crossref","unstructured":"Eke PI Borgnakke WS Genco RJ. 2020. Recent epidemiologic trends in periodontitis in the USA. Periodontol 2000. 82(1):257\u2013267. https:\/\/doi.org\/10.1111\/prd.12323","DOI":"10.1111\/prd.12323"},{"key":"e_1_3_2_12_1","doi-asserted-by":"crossref","unstructured":"Eke PI et al. 2015. Update on prevalence of periodontitis in adults in the United States: NHANES 2009 \u2013 2012. J Periodontol. 86(5):611\u2013622. https:\/\/doi.org\/10.1902\/jop.2015.140520","DOI":"10.1902\/jop.2015.140520"},{"key":"e_1_3_2_13_1","doi-asserted-by":"crossref","unstructured":"Eke PI Page RC Wei L Thornton-Evans G Genco RJ. 2012. Update of the case definitions for population-based surveillance of periodontitis. J Periodontol. 83(12):1449\u20131454. https:\/\/doi.org\/10.1902\/jop.2012.110664","DOI":"10.1902\/jop.2012.110664"},{"key":"e_1_3_2_14_1","doi-asserted-by":"crossref","unstructured":"Fahmy AS et al. 2022. An explainable machine learning approach reveals prognostic significance of right ventricular dysfunction in nonischemic cardiomyopathy. JACC Cardiovasc Imaging. 15(5):766\u2013779. https:\/\/doi.org\/10.1016\/j.jcmg.2021.11.029","DOI":"10.1016\/j.jcmg.2021.11.029"},{"key":"e_1_3_2_15_1","doi-asserted-by":"crossref","unstructured":"Fu H Li X Zhang R Zhu J Wang X. 2025. Global burden of periodontal diseases among the working-age population from 1990\u20132021: results from the Global Burden of Disease Study 2021. BMC Public Health. 25(1):1316. https:\/\/doi.org\/10.1186\/s12889-025-22566-x","DOI":"10.1186\/s12889-025-22566-x"},{"key":"e_1_3_2_16_1","doi-asserted-by":"crossref","unstructured":"Gul SS. 2024. Prevalence and severity of circumferential alveolar bone loss using CBCT images: a retrospective study of 20 620 surfaces of 5155 teeth. Diagnostics (Basel). 14(5):507. https:\/\/doi.org\/10.3390\/diagnostics14050507","DOI":"10.3390\/diagnostics14050507"},{"key":"e_1_3_2_17_1","doi-asserted-by":"crossref","unstructured":"Jepsen S Deschner J Braun A Schwarz F Eberhard J. 2011. Calculus removal and the prevention of its formation. Periodontol 2000. 55(1):167\u2013188. https:\/\/doi.org\/10.1111\/j.1600-0757.2010.00382.x","DOI":"10.1111\/j.1600-0757.2010.00382.x"},{"key":"e_1_3_2_18_1","unstructured":"Jordan AR Micheelis W editors. 2016. F\u00fcnfte Deutsche Mundgesundheitsstudie (DMS V) [The Fifth German Oral Health Study; in German]. Deutscher Zahn\u00e4rzte Verlag D\u00c4V."},{"key":"e_1_3_2_19_1","doi-asserted-by":"crossref","unstructured":"Jordan RA et al. 2014. The Fifth German oral health study (F\u00fcnfte Deutsche Mundgesundheitsstudie DMS V) \u2013 rationale design and methods. BMC Oral Health. 14:161. https:\/\/doi.org\/10.1186\/1472-6831-14-161","DOI":"10.1186\/1472-6831-14-161"},{"key":"e_1_3_2_20_1","doi-asserted-by":"crossref","unstructured":"Kang B et al. 2024. Reliability of partial-mouth periodontal examinations for diagnosing periodontal disease. J Periodontal Implant Sci. 55(4):273\u2013284. https:\/\/doi.org\/10.5051\/jpis.2403400170","DOI":"10.5051\/jpis.2403400170"},{"key":"e_1_3_2_21_1","doi-asserted-by":"crossref","unstructured":"Kapila YL. 2021. Oral health\u2019s inextricable connection to systemic health: special populations bring to bear multimodal relationships and factors connecting periodontal disease to systemic diseases and conditions. Periodontol 2000. 87(1):11\u201316. https:\/\/doi.org\/10.1111\/prd.12398","DOI":"10.1111\/prd.12398"},{"key":"e_1_3_2_22_1","doi-asserted-by":"crossref","unstructured":"Kinane DF Stathopoulou PG Papapanou PN. 2017. Periodontal diseases. Nat Rev Dis Primers. 3(1):17038. https:\/\/doi.org\/10.1038\/nrdp.2017.38","DOI":"10.1038\/nrdp.2017.38"},{"key":"e_1_3_2_23_1","doi-asserted-by":"crossref","unstructured":"Marcilio WE Eler DM. 2020. From explanations to feature selection: assessing SHAP values as feature selection mechanism. In: 2020 33rd SIBGRAPI Conference on Graphics Patterns and Images (SIBGRAPI); 2020 Nov 7\u201310; Virtual Conference. IEEE. p 340\u2013347. https:\/\/doi.org\/10.1109\/SIBGRAPI51738.2020.00053","DOI":"10.1109\/SIBGRAPI51738.2020.00053"},{"key":"e_1_3_2_24_1","doi-asserted-by":"crossref","unstructured":"Matthews DC Tabesh M. 2004. Detection of localized tooth-related factors that predispose to periodontal infections. Periodontol 2000. 34(1):136\u2013150. https:\/\/doi.org\/10.1046\/j.0906-6713.2003.003429.x","DOI":"10.1046\/j.0906-6713.2003.003429.x"},{"key":"e_1_3_2_25_1","doi-asserted-by":"crossref","unstructured":"Mombelli A Meier C. 2001. On the symmetry of periodontal disease. J Clin Periodontol. 28(8):741\u2013745. https:\/\/doi.org\/10.1034\/j.1600-051x.2001.280804.x","DOI":"10.1034\/j.1600-051X.2001.280804.x"},{"key":"e_1_3_2_26_1","doi-asserted-by":"crossref","unstructured":"Ngai T et al. 2024. Assessing polyomic risk to predict Alzheimer\u2019s disease using a machine learning model. Alzheimers Dement. 20(12):8700\u20138714. https:\/\/doi.org\/10.1002\/alz.14319","DOI":"10.1002\/alz.14319"},{"key":"e_1_3_2_27_1","doi-asserted-by":"crossref","unstructured":"Nohara Y Matsumoto K Soejima H Nakashima N. 2022. Explanation of machine learning models using Shapley additive explanation and application for real data in hospital. Comput Methods Programs Biomed. 214:106584. https:\/\/doi.org\/10.1016\/j.cmpb.2021.106584","DOI":"10.1016\/j.cmpb.2021.106584"},{"key":"e_1_3_2_28_1","doi-asserted-by":"crossref","unstructured":"Oh M-Y et al. 2025. Machine learning\u2013based explainable automated nonlinear computation scoring system for health score and an application for prediction of perioperative stroke: retrospective study. J Med Internet Res. 27:e58021. https:\/\/doi.org\/10.2196\/58021","DOI":"10.2196\/58021"},{"key":"e_1_3_2_29_1","doi-asserted-by":"crossref","unstructured":"Owens JD Dowsett SA Eckert GJ Zero DT Kowolik MJ. 2003. Partial-mouth assessment of periodontal disease in an adult population of the United States. J Periodontol. 74(8):1206\u20131213. https:\/\/doi.org\/10.1902\/jop.2003.74.8.1206","DOI":"10.1902\/jop.2003.74.8.1206"},{"key":"e_1_3_2_30_1","doi-asserted-by":"crossref","unstructured":"Perez-Lebel A Varoquaux G Le\u2009Morvan M Josse J Poline J-B. 2022. Benchmarking missing-values approaches for predictive models on health databases. Gigascience. 11:giac013. https:\/\/doi.org\/10.1093\/gigascience\/giac013","DOI":"10.1093\/gigascience\/giac013"},{"key":"e_1_3_2_31_1","doi-asserted-by":"crossref","unstructured":"Ponce-Bobadilla AV Schmitt V Maier CS Mensing S Stodtmann S. 2024. Practical guide to SHAP analysis: explaining supervised machine learning model predictions in drug development. Clin Transl Sci. 17(11):e70056. https:\/\/doi.org\/10.1111\/cts.70056","DOI":"10.1111\/cts.70056"},{"key":"e_1_3_2_32_1","doi-asserted-by":"crossref","unstructured":"Sreenivasan PK Prasad KVV Javali SB. 2016. Oral health practices and prevalence of dental plaque and gingivitis among Indian adults. Clin Exp Dent Res. 2(1):6\u201317. https:\/\/doi.org\/10.1002\/cre2.15","DOI":"10.1002\/cre2.15"},{"key":"e_1_3_2_33_1","doi-asserted-by":"crossref","unstructured":"Su X et al. 2023. The association between sex hormones and periodontitis among American adults: a cross-sectional study. Front Endocrinol (Lausanne). 14:1125819. https:\/\/doi.org\/10.3389\/fendo.2023.1125819","DOI":"10.3389\/fendo.2023.1125819"},{"key":"e_1_3_2_34_1","doi-asserted-by":"crossref","unstructured":"Suh B Yu H Cha J-K Choi J Kim J-W. 2025. Explainable deep learning approaches for risk screening of periodontitis. J Dent Res. 104(1):45\u201353. https:\/\/doi.org\/10.1177\/00220345241286488","DOI":"10.1177\/00220345241286488"},{"key":"e_1_3_2_35_1","doi-asserted-by":"crossref","unstructured":"Susin C Vecchia CFD Oppermann RV Haugejorden O Albandar JM. 2004. Periodontal attachment loss in an urban population of Brazilian adults: effect of demographic behavioral and environmental risk indicators. J Periodontol. 75(7):1033\u20131041. https:\/\/doi.org\/10.1902\/jop.2004.75.7.1033","DOI":"10.1902\/jop.2004.75.7.1033"},{"key":"e_1_3_2_36_1","doi-asserted-by":"crossref","unstructured":"Tay JRH Holtfreter B Baumeister S Peres MA Nascimento GG. 2025. Application of the 2018 Periodontal Status Classification to Epidemiological Survey Data (ACES) framework to estimate the periodontitis prevalence in the United States. J Clin Periodontol. 52(7):1032\u20131043. https:\/\/doi.org\/10.1111\/jcpe.14132","DOI":"10.1111\/jcpe.14132"},{"key":"e_1_3_2_37_1","doi-asserted-by":"crossref","unstructured":"Tonetti MS Greenwell H Kornman KS. 2018. Staging and grading of periodontitis: framework and proposal of a new classification and case definition. J Periodontol. 89(suppl 1):S159\u2013S172. https:\/\/doi.org\/10.1002\/jper.18-0006","DOI":"10.1002\/JPER.18-0006"},{"key":"e_1_3_2_38_1","doi-asserted-by":"crossref","unstructured":"Tran DT et al. 2014. Assessment of partial-mouth periodontal examination protocols for periodontitis surveillance. J Clin Periodontol. 41(9):846\u2013852. https:\/\/doi.org\/10.1111\/jcpe.12285","DOI":"10.1111\/jcpe.12285"},{"key":"e_1_3_2_39_1","doi-asserted-by":"crossref","unstructured":"Vandenbroucke JP et al. 2007. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. PLoS Med. 4(10):e297. https:\/\/doi.org\/10.1371\/journal.pmed.0040297","DOI":"10.1371\/journal.pmed.0040297"},{"key":"e_1_3_2_40_1","doi-asserted-by":"crossref","unstructured":"Walter E et al. 2025. Predictive modeling for step II therapy response in periodontitis - model development and validation. NPJ Digit Med. 8(1):445. https:\/\/doi.org\/10.1038\/s41746-025-01828-3","DOI":"10.1038\/s41746-025-01828-3"},{"key":"e_1_3_2_41_1","doi-asserted-by":"crossref","unstructured":"Wu L Zhang S Zhao L Ren Z Hu C. 2022. Global regional and national burden of periodontitis from 1990 to 2019: results from the Global Burden of Disease study 2019. J Periodontol. 93(10):1445\u20131454. https:\/\/doi.org\/10.1002\/jper.21-0469","DOI":"10.1002\/JPER.21-0469"}],"container-title":["Journal of Dental Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/00220345261432570","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/00220345261432570","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/00220345261432570","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T12:23:53Z","timestamp":1793276633000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/00220345261432570"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,7]]},"references-count":40,"alternative-id":["10.1177\/00220345261432570"],"URL":"https:\/\/doi.org\/10.1177\/00220345261432570","relation":{},"ISSN":["0022-0345","1544-0591"],"issn-type":[{"value":"0022-0345","type":"print"},{"value":"1544-0591","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,7]]},"article-number":"00220345261432570"}}