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Bone losses in the maxilla and mandibula (total alveolar bone loss) (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20092251), interdental bone losses (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200925303), and furcation defects (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20092815) were labeled using the segmentation method. In addition, interdental bone losses were divided into horizontal (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200921839) and vertical (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20093464) bone losses according to the defect patterns. A Convolutional Neural Network (CNN)-based artificial intelligence (AI) system was developed using U-Net architecture. The performance of the deep learning algorithm was statistically evaluated by the confusion matrix and ROC curve analysis.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The system showed the highest diagnostic performance in the detection of total alveolar bone losses (AUC\u2009=\u20090.951) and the lowest in the detection of vertical bone losses (AUC\u2009=\u20090.733). The sensitivity, precision, F1 score, accuracy, and AUC values were found as 1, 0.995, 0.997, 0.994, 0.951 for total alveolar bone loss; found as 0.947, 0.939, 0.943, 0.892, 0.910 for horizontal bone losses; found as 0.558, 0.846, 0.673, 0.506, 0.733 for vertical bone losses and found as 0.892, 0.933, 0.912, 0.837, 0.868 for furcation defects (respectively).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>AI systems offer promising results in determining periodontal bone loss patterns and furcation defects from dental radiographs. This suggests that CNN algorithms can also be used to provide more detailed information such as automatic determination of periodontal disease severity and treatment planning in various dental radiographs.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12903-024-03896-5","type":"journal-article","created":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T09:03:16Z","timestamp":1706691796000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":76,"title":["Detection of periodontal bone loss patterns and furcation defects from panoramic radiographs using deep learning algorithm: a retrospective study"],"prefix":"10.1186","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3711-6520","authenticated-orcid":false,"given":"Sevda","family":"Kurt-Bayrakdar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u0130brahim \u015eevki","family":"Bayrakdar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammet Burak","family":"Yavuz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nichal","family":"Sali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u00d6zer","family":"\u00c7elik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"O\u011fuz","family":"K\u00f6se","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bilge Cansu","family":"Uzun Saylan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Batuhan","family":"Kuleli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rohan","family":"Jagtap","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaan","family":"Orhan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,31]]},"reference":[{"key":"3896_CR1","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1111\/j.1600-0757.2011.00397.x","volume":"61","author":"A Dentino","year":"2013","unstructured":"Dentino A, Lee S, Mailhot J, Hefti AF. 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All participants gave informed consent. We confirmed that the study was carried out according to the relevant guidelines and regulations of the Helsinki Declaration.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Additional informed consent was obtained from all individual participants included in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that there is no conflict of interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"155"}}