{"title":"一种基于卷积神经网络的人工智能算法的自动头侧标记检测性能。","authors":"Mehmet Uğurlu","doi":"10.5152/TurkJOrthod.2022.22026","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>The aim of this study is to develop an artificial intelligence model to detect cephalometric landmark automatically en- abling the automatic analysis of cephalometric radiographs which have a very important place in dental practice and is used routinely in the diagnosis and treatment of dental and skeletal disorders.</p><p><strong>Methods: </strong>In this study, 1620 lateral cephalograms were obtained and 21 landmarks were included. The coordinates of all landmarks in the 1620 films were obtained to establish a labeled data set: 1360 were used as a training set, 140 as a validation set, and 180 as a testing set. A convolutional neural network-based artificial intelligence algorithm for automatic cephalometric landmark detection was developed. Mean radial error and success detection rate within the range of 2 mm, 2.5 mm, 3 mm, and 4 mm were used to eval- uate the performance of the model.</p><p><strong>Results: </strong>Presented artificial intelligence system (CranioCatch, Eskişehir, Turkey) could detect 21 anatomic landmarks in a lateral ceph- alometric radiograph. The highest success detection rate scores of 2 mm, 2.5 mm, 3 mm, and 4 mm were obtained from the sella point as 98.3, 99.4, 99.4, and 99.4, respectively. The mean radial error ± standard deviation value of the sella point was found as 0.616 ± 0.43. The lowest success detection rate scores of 2 mm, 2.5 mm, 3 mm, and 4 mm were obtained from the Gonion point as 48.3, 62.8, 73.9, and 87.2, respectively. The mean radial error ± standard deviation value of Gonion point was found as 8.304 ± 2.98.</p><p><strong>Conclusion: </strong>Although the success of the automatic landmark detection using the developed artificial intelligence model was not in- sufficient for clinical use, artificial intelligence-based cephalometric analysis systems seem promising to cephalometric analysis which provides a basis for diagnosis, treatment planning, and following-up in clinical orthodontics practice.</p>","PeriodicalId":37013,"journal":{"name":"Turkish Journal of Orthodontics","volume":"35 2","pages":"94-100"},"PeriodicalIF":0.8000,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9450082/pdf/tjo-35-2-94.pdf","citationCount":"3","resultStr":"{\"title\":\"Performance of a Convolutional Neural Network- Based Artificial Intelligence Algorithm for Automatic Cephalometric Landmark Detection.\",\"authors\":\"Mehmet Uğurlu\",\"doi\":\"10.5152/TurkJOrthod.2022.22026\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>The aim of this study is to develop an artificial intelligence model to detect cephalometric landmark automatically en- abling the automatic analysis of cephalometric radiographs which have a very important place in dental practice and is used routinely in the diagnosis and treatment of dental and skeletal disorders.</p><p><strong>Methods: </strong>In this study, 1620 lateral cephalograms were obtained and 21 landmarks were included. The coordinates of all landmarks in the 1620 films were obtained to establish a labeled data set: 1360 were used as a training set, 140 as a validation set, and 180 as a testing set. A convolutional neural network-based artificial intelligence algorithm for automatic cephalometric landmark detection was developed. Mean radial error and success detection rate within the range of 2 mm, 2.5 mm, 3 mm, and 4 mm were used to eval- uate the performance of the model.</p><p><strong>Results: </strong>Presented artificial intelligence system (CranioCatch, Eskişehir, Turkey) could detect 21 anatomic landmarks in a lateral ceph- alometric radiograph. The highest success detection rate scores of 2 mm, 2.5 mm, 3 mm, and 4 mm were obtained from the sella point as 98.3, 99.4, 99.4, and 99.4, respectively. The mean radial error ± standard deviation value of the sella point was found as 0.616 ± 0.43. The lowest success detection rate scores of 2 mm, 2.5 mm, 3 mm, and 4 mm were obtained from the Gonion point as 48.3, 62.8, 73.9, and 87.2, respectively. The mean radial error ± standard deviation value of Gonion point was found as 8.304 ± 2.98.</p><p><strong>Conclusion: </strong>Although the success of the automatic landmark detection using the developed artificial intelligence model was not in- sufficient for clinical use, artificial intelligence-based cephalometric analysis systems seem promising to cephalometric analysis which provides a basis for diagnosis, treatment planning, and following-up in clinical orthodontics practice.</p>\",\"PeriodicalId\":37013,\"journal\":{\"name\":\"Turkish Journal of Orthodontics\",\"volume\":\"35 2\",\"pages\":\"94-100\"},\"PeriodicalIF\":0.8000,\"publicationDate\":\"2022-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9450082/pdf/tjo-35-2-94.pdf\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Turkish Journal of Orthodontics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5152/TurkJOrthod.2022.22026\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"DENTISTRY, ORAL SURGERY & MEDICINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Turkish Journal of Orthodontics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5152/TurkJOrthod.2022.22026","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"DENTISTRY, ORAL SURGERY & MEDICINE","Score":null,"Total":0}
Performance of a Convolutional Neural Network- Based Artificial Intelligence Algorithm for Automatic Cephalometric Landmark Detection.
Objective: The aim of this study is to develop an artificial intelligence model to detect cephalometric landmark automatically en- abling the automatic analysis of cephalometric radiographs which have a very important place in dental practice and is used routinely in the diagnosis and treatment of dental and skeletal disorders.
Methods: In this study, 1620 lateral cephalograms were obtained and 21 landmarks were included. The coordinates of all landmarks in the 1620 films were obtained to establish a labeled data set: 1360 were used as a training set, 140 as a validation set, and 180 as a testing set. A convolutional neural network-based artificial intelligence algorithm for automatic cephalometric landmark detection was developed. Mean radial error and success detection rate within the range of 2 mm, 2.5 mm, 3 mm, and 4 mm were used to eval- uate the performance of the model.
Results: Presented artificial intelligence system (CranioCatch, Eskişehir, Turkey) could detect 21 anatomic landmarks in a lateral ceph- alometric radiograph. The highest success detection rate scores of 2 mm, 2.5 mm, 3 mm, and 4 mm were obtained from the sella point as 98.3, 99.4, 99.4, and 99.4, respectively. The mean radial error ± standard deviation value of the sella point was found as 0.616 ± 0.43. The lowest success detection rate scores of 2 mm, 2.5 mm, 3 mm, and 4 mm were obtained from the Gonion point as 48.3, 62.8, 73.9, and 87.2, respectively. The mean radial error ± standard deviation value of Gonion point was found as 8.304 ± 2.98.
Conclusion: Although the success of the automatic landmark detection using the developed artificial intelligence model was not in- sufficient for clinical use, artificial intelligence-based cephalometric analysis systems seem promising to cephalometric analysis which provides a basis for diagnosis, treatment planning, and following-up in clinical orthodontics practice.
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