Full metadata record
DC Field | Value | Language |
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dc.contributor.author | 김민지 | * |
dc.date.accessioned | 2022-02-28T16:30:06Z | - |
dc.date.available | 2022-02-28T16:30:06Z | - |
dc.date.issued | 2022 | * |
dc.identifier.issn | 0889-5406 | * |
dc.identifier.issn | 1097-6752 | * |
dc.identifier.other | OAK-30887 | * |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/260754 | - |
dc.description.abstract | Introduction: The purpose of this study was to evaluate the accuracy of auto-identification of the posteroanterior (PA) cephalometric landmarks using the cascade convolution neural network (CNN) algorithm and PA cephalogram images of a different quality from nationwide multiple centers nationwide. Methods: Of the 2798 PA cephalograms from 9 university hospitals, 2418 images (2075 training set and 343 validation set) were used to train the CNN algorithm for auto-identification of 16 PA cephalometric landmarks. Subsequently, 99 pretreatment images from the remaining 380 test set images were used to evaluate the accuracy of auto-identification of the CNN algorithm by comparing with the identification by a human examiner (gold standard) using V-Ceph 8.0 (Ostem, Seoul, South Korea). Pretreatment images were used to eliminate the effects of orthodontic bracket, tube and wire, surgical plate, and surgical screws. Paired t test was performed to compare the x- and y-coordinates of each landmark. The point-to-point error and the successful detection rate (range, within 2.0 mm) were calculated. Results: The number of landmarks without a significant difference between the location identified by the human examiner and by auto-identification by the CNN algorithm were 8 on the x-coordinate and 5 on the y-coordinate, respectively. The mean point-to-point error was 1.52 mm. The low point-to-point error (<1.0 mm) was observed at the left and right antegonion (0.96 mm and 0.99 mm, respectively) and the high point-to-point error (>2.0 mm) was observed at the maxillary right first molar root apex (2.18 mm). The mean successful detection rate of auto-identification was 83.3%. Conclusions: Cascade CNN algorithm for auto-identification of PA cephalometric landmarks showed a possibility of an effective alternative to manual identification. | * |
dc.language | English | * |
dc.publisher | MOSBY-ELSEVIER | * |
dc.title | Accuracy of auto-identification of the posteroanterior cephalometric landmarks using cascade convolution neural network algorithm and cephalometric images of different quality from nationwide multiple centers | * |
dc.type | Article | * |
dc.relation.issue | 4 | * |
dc.relation.volume | 161 | * |
dc.relation.index | SCIE | * |
dc.relation.index | SCOPUS | * |
dc.relation.startpage | E361 | * |
dc.relation.lastpage | E371 | * |
dc.relation.journaltitle | AMERICAN JOURNAL OF ORTHODONTICS AND DENTOFACIAL ORTHOPEDICS | * |
dc.identifier.doi | 10.1016/j.ajodo.2021.11.011 | * |
dc.identifier.wosid | WOS:000821064000007 | * |
dc.identifier.scopusid | 2-s2.0-85123260088 | * |
dc.author.google | Gil, Soo-Min | * |
dc.author.google | Kim, Inhwan | * |
dc.author.google | Cho, Jin-Hyoung | * |
dc.author.google | Hong, Mihee | * |
dc.author.google | Kim, Minji | * |
dc.author.google | Kim, Su-Jung | * |
dc.author.google | Kim, Yoon-Ji | * |
dc.author.google | Kim, Young Ho | * |
dc.author.google | Lim, Sung-Hoon | * |
dc.author.google | Sung, Sang Jin | * |
dc.author.google | Baek, Seung-Hak | * |
dc.author.google | Kim, Namkug | * |
dc.author.google | Kang, Kyung-Hwa | * |
dc.contributor.scopusid | 김민지(57201330607) | * |
dc.date.modifydate | 20240426142152 | * |