{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T15:49:51Z","timestamp":1790351391653,"version":"4.1.0"},"reference-count":49,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T00:00:00Z","timestamp":1736208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 DE031307"],"award-info":[{"award-number":["R01 DE031307"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To identify landmarks in ultrasound periodontal images and automate the image-based measurements of gingival recession (iGR), gingival height (iGH), and alveolar bone level (iABL) using machine learning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We imaged 184 teeth from 29 human subjects. The dataset included 1580 frames for training and validating the U-Net convolutional neural network machine learning model, and 250 frames from new teeth that were not used in training for testing the generalization performance. The predicted landmarks, including the tooth, gingiva, bone, gingival margin (GM), cementoenamel junction (CEJ), and alveolar bone crest (ABC), were compared to manual annotations. We further demonstrated automated measurements of the clinical metrics iGR, iGH, and iABL.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Over 98% of predicted GM, CEJ, and ABC distances are within 200\u2009\u00b5m of the manual annotation. Bland-Altman analysis revealed biases (bias of machine learning vs ground truth) of \u22120.1\u2009\u00b5m, \u221237.6\u2009\u00b5m, and \u221240.9\u2009\u00b5m, with 95% limits of agreement of [\u2212281.3, 281.0] \u00b5m, [\u2212203.1, 127.9] \u00b5m, and [\u2212297.6, 215.8] \u00b5m for iGR, iGH, and iABL, respectively, when compared to manual annotations. On the test dataset, the biases were 167.5\u2009\u00b5m, 40.1\u2009\u00b5m, and 78.7\u2009\u00b5m with 95% CIs of [\u22121175 to 1510] \u00b5m, [\u2212910.3 to 990.4] \u00b5m, and [\u22121954 to 1796] \u00b5m for iGR, iGH, and iABL, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed machine learning model demonstrates robust prediction performance, with the potential to enhance the efficiency of clinical periodontal diagnosis by automating landmark identification and clinical metrics measurements.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/dmfr\/twaf001","type":"journal-article","created":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T13:44:45Z","timestamp":1736257485000},"page":"210-221","source":"Crossref","is-referenced-by-count":9,"title":["Machine learning for automated identification of anatomical landmarks in ultrasound periodontal imaging"],"prefix":"10.1093","volume":"54","author":[{"given":"Baiyan","family":"Qi","sequence":"first","affiliation":[{"name":"Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California San Diego , La Jolla, CA 92093,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lekshmi","family":"Sasi","sequence":"additional","affiliation":[{"name":"Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California San Diego , La Jolla, CA 92093,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suhel","family":"Khan","sequence":"additional","affiliation":[{"name":"Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California San Diego , La Jolla, CA 92093,","place":["United 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