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While its treatment is largely standardized, individual outcomes vary, with some patients experiencing further disease progression despite adherence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed a machine learning (ML) approach to predict individual outcomes 1 year post\u2010treatment using retrospectively assessed baseline parameters. We trained a Random Forest model on 18 demographic, clinical, microbiological, and treatment\u2010related features of 414 patients from randomized clinical trials (RCTs) in South America. We subsequently performed internal testing, interpretability analysis, and external testing on a second dataset of 78 patients from previous RCTs in North America and Europe exhibiting less severe disease.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In internal testing, the ML model achieved an area under the receiver operator characteristics curve (AUROC) of 0.93, an area under the precision\u2010recall curve (AUPRC) of 0.90, an F\n                      <jats:sub>1<\/jats:sub>\n                      \u2010score of 0.82, and an out\u2010of\u2010bag score of 0.71. Relative importances were 0.42 for clinical, 0.33 for treatment\u2010related, 0.21 for microbiological, and 0.04 for demographic features. In external testing, the ML model achieved an AUROC of 0.76, an AUPRC of 0.69, and an F\n                      <jats:sub>1<\/jats:sub>\n                      \u2010score of 0.71.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our study indicates that an ML\u2010based approach can assist in predicting individual responses to periodontal treatment. Prospective validation is needed for clinical application.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Plain language summary<\/jats:title>\n                    <jats:p>Using comprehensive data from patients with periodontitis, an inflammatory condition of the tooth\u2010supporting tissues, a\u00a0machine learning model was trained to predict how well patients might respond to different treatments after 1 year. The model was externally tested in patient populations from 2 continents different from the training dataset. The results suggest that with further research and refinement, this tool could eventually become a valuable asset in personalizing treatment plans for improved patient outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jper.24-0737","type":"journal-article","created":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T08:39:14Z","timestamp":1748335154000},"page":"1199-1212","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Machine learning\u2010assisted prediction of clinical responses to periodontal treatment"],"prefix":"10.1002","volume":"96","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4386-6237","authenticated-orcid":false,"given":"Balazs","family":"Feher","sequence":"first","affiliation":[{"name":"Department of Oral Medicine, Infection, and Immunity Harvard School of Dental Medicine  Boston Massachusetts USA"},{"name":"Department of Oral Surgery University Clinic of Dentistry, Medical University of Vienna  Vienna Austria"},{"name":"Department of Oral Biology University Clinic of Dentistry, Medical University of Vienna  Vienna Austria"},{"name":"Topic Group Dental Diagnostics and Digital Dentistry International Telecommunication Union\u2013World Health Organization\u2013World Intellectual Property Organization Global Initiative on Artificial Intelligence for Health  Geneva Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5823-6591","authenticated-orcid":false,"given":"Eduardo H.","family":"de Souza Oliveira","sequence":"additional","affiliation":[{"name":"Department of Oral Medicine, Infection, and Immunity Harvard School of Dental Medicine  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8872-5943","authenticated-orcid":false,"given":"Poliana","family":"Mendes Duarte","sequence":"additional","affiliation":[{"name":"Department of 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