{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,7]],"date-time":"2026-11-07T16:56:45Z","timestamp":1794070605546,"version":"4.8.0"},"reference-count":34,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:00:00Z","timestamp":1760140800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:00:00Z","timestamp":1760140800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Esthet Restor Dent"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The integration of thermal imaging with artificial intelligence (AI) offers a novel, non\u2010invasive approach for assessing gingival inflammation. While thermal imaging has been widely applied in other medical fields, its use in evaluating gingival health remains largely unexplored. This study is the first to utilize AI\u2010supported analysis of thermal gingival images in patients with mouth breathing habits, aiming to detect and classify gingival inflammation severity. This research establishes specific thermal thresholds for gingival health and disease in this unique population.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Forty participants were included, stratified according to periodontal status and clinically confirmed breathing pattern (mouth or nasal breathing), under standardized imaging conditions. From these participants, 160 images were annotated, producing 1734 labeled data points categorized according to bleeding on probing (BoP) for diagnosis, with Gingival Index (GI) applied only for secondary stratification of inflammation severity. Preprocessing included image resizing, outlier removal, and calculation of mean RGB values. The XGBoost algorithm was used for classification, with hyperparameters optimized via grid search and 5\u2010fold cross\u2010validation to ensure robust model performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The\n                      <jats:styled-content style=\"fixed-case\">XGBoost<\/jats:styled-content>\n                      Achieved Outstanding Classification Results, With an Accuracy of 92.74%, Precision of 92.95%, Sensitivity of 92.74%, and an\n                      <jats:styled-content style=\"fixed-case\">F1<\/jats:styled-content>\n                      Score of 92.78%. Cross\u2010Validation Confirmed the Model's Reliability, With Mean\u2010Test and Validation\u2010Scores of 88.28% and 89.43%, Respectively.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study represents the first application of AI\u2010supported thermal imaging for evaluating gingival inflammation in mouth breathers, marking a significant step forward in periodontal diagnostics. By establishing specific thermal thresholds in this unique population, it highlights the potential of this innovative approach as a non\u2010invasive, real\u2010time, and scalable diagnostic tool. Future research should focus on refining AI algorithms and expanding datasets to enhance clinical applicability, paving the way for advanced diagnostics and personalized care in periodontology.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jerd.70045","type":"journal-article","created":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:20:38Z","timestamp":1760167238000},"page":"362-370","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Artificial Intelligence Assisted Thermal Imaging for Gingival Inflammation Assessment: A Novel Approach"],"prefix":"10.1111","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4093-9755","authenticated-orcid":false,"given":"Zeynep Turgut","family":"\u00c7ankaya","sequence":"first","affiliation":[{"name":"Gazi University, Faculty of Dentistry Department of Periodontology  T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmet","family":"Koyuncu","sequence":"additional","affiliation":[{"name":"MIA Technology  Ankara T\u00fcrkiye"},{"name":"Department of Statistics Eski\u015fehir Technical University  Eski\u015fehir T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2277-2020","authenticated-orcid":false,"given":"S\u00fchan","family":"G\u00fcrb\u00fcz","sequence":"additional","affiliation":[{"name":"Gazi University, Faculty of Dentistry Department of Periodontology  T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,10,11]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/JPER.18-0157"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/jcpe.12939"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/jcpe.12940"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1902\/jop.2015.150455"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1902\/jop.2015.157001"},{"issue":"84","key":"e_1_2_10_7_1","first-page":"9","article-title":"The Prevention of Periodontal Disease\u2010An Overview","volume":"2020","author":"Scannapieco F. 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