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Four deep ConvNet models were developed using ensemble learning and outperformed a single model. The performances of the different models were evaluated by comparing the accuracy and sensitivity for recognizing the existence of gingivitis from intraoral images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The\n                      <jats:italic>ResNet<\/jats:italic>\n                      model achieved an area under the curve (AUC) value of 97%, while the AUC values for the\n                      <jats:italic>GoogLeNet<\/jats:italic>\n                      ,\n                      <jats:italic>AlexNet<\/jats:italic>\n                      , and\n                      <jats:italic>VGG<\/jats:italic>\n                      models were 94%, 92%, and 89%, respectively. Although the\n                      <jats:italic>ResNet<\/jats:italic>\n                      and\n                      <jats:italic>GoogLeNet<\/jats:italic>\n                      models performed best in classifying gingivitis from images, the sensitivity outcomes were not significantly different among the\n                      <jats:italic>ResNet<\/jats:italic>\n                      ,\n                      <jats:italic>GoogLeNet<\/jats:italic>\n                      , and\n                      <jats:italic>Alexnet<\/jats:italic>\n                      models (\n                      <jats:italic>p<\/jats:italic>\n                      &gt;0.05). However, the sensitivity of the\n                      <jats:italic>VGGNet<\/jats:italic>\n                      model differed significantly from those of the other models (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      The\n                      <jats:italic>ResNet<\/jats:italic>\n                      and\n                      <jats:italic>GoogLeNet<\/jats:italic>\n                      models show promise for identifying chronic gingivitis from images. These models can help doctors diagnose periodontal diseases efficiently or based on self-examination of the oral cavity by patients.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12903-024-04460-x","type":"journal-article","created":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T12:02:09Z","timestamp":1721217729000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Evaluation of transfer ensemble learning-based convolutional neural network models for the identification of chronic gingivitis from oral photographs"],"prefix":"10.1186","volume":"24","author":[{"given":"Wen","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enting","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuyang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leiying","family":"Miao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weibin","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,17]]},"reference":[{"issue":"1","key":"4460_CR1","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1111\/idh.12615","volume":"21","author":"DG Kamath","year":"2023","unstructured":"Kamath DG, Nadimpalli H, Nayak SU, Rajendran V, Natarajan S. 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