{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T16:25:52Z","timestamp":1782318352960,"version":"3.54.5"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T00:00:00Z","timestamp":1719964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"Scientific Research Project of Health Care for Cadres of Jiangsu Province","award":["BJ21034"],"award-info":[{"award-number":["BJ21034"]}]},{"name":"Innovation and Entrepreneurship Training Program for College Students in Jiangsu Province","award":["202110312098E"],"award-info":[{"award-number":["202110312098E"]}]},{"name":"Jiangsu Province Capability Improvement"},{"name":"Science, Technology and Education-Jiangsu Provincial Research Hospital Cultivation Unit","award":["YJXYYJSDW4"],"award-info":[{"award-number":["YJXYYJSDW4"]}]},{"name":"Jiangsu Provincial Medical Innovation Center","award":["CXZX202227"],"award-info":[{"award-number":["CXZX202227"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objectives<\/jats:title>\n                  <jats:p>In order to assist junior doctors in better diagnosing apical periodontitis (AP), an artificial intelligence AP grading system was developed based on deep learning (DL) and its reliability and accuracy were evaluated.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>One hundred and twenty cone-beam computed tomography (CBCT) images were selected to construct a classification dataset with four categories, which were divided by CBCT periapical index (CBCTPAI), including normal periapical tissue, CBCTPAI 1-2, CBCTPAI 3-5, and young permanent teeth. Three classic algorithms (ResNet50\/101\/152) as well as one self-invented algorithm (PAINet) were compared with each other. PAINet were also compared with two recent Transformer-based models and three attention models. Their performance was evaluated by accuracy, precision, recall, balanced F score (F1-score), and the area under the macro-average receiver operating curve (AUC). Reliability was evaluated by Cohen\u2019s kappa to compare the consistency of model predicted labels with expert opinions.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>PAINet performed best among the four algorithms. The accuracy, precision, recall, F1-score, and AUC on the test set were 0.9333, 0.9415, 0.9333, 0.9336, and 0.9972, respectively. Cohen\u2019s kappa was 0.911, which represented almost perfect consistency.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>PAINet can accurately distinguish between normal periapical tissues, CBCTPAI 1-2, CBCTPAI 3-5, and young permanent teeth. Its results were highly consistent with expert opinions. It can help junior doctors diagnose and score AP, reducing the burden. It can also be promoted in areas where experts are lacking to provide professional diagnostic opinions.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/dmfr\/twae029","type":"journal-article","created":{"date-parts":[[2024,7,4]],"date-time":"2024-07-04T01:52:31Z","timestamp":1720057951000},"page":"447-458","source":"Crossref","is-referenced-by-count":7,"title":["An artificial intelligence grading system of apical periodontitis in cone-beam computed tomography data"],"prefix":"10.1093","volume":"53","author":[{"given":"Tianyin","family":"Zhao","sequence":"first","affiliation":[{"name":"Department of Endodontics, The Affiliated Stomatological Hospital of Nanjing Medical University , Nanjing, 210029,","place":["China"]},{"name":"Department of Oral & Maxillofacial Imaging, The Affiliated Stomatological Hospital of Nanjing Medical University , Nanjing, 210029,","place":["China"]},{"name":"Jiangsu Province Key Laboratory of Oral Diseases , Nanjing, 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