Cited 0 times in Scipus Cited Count

Orthognathic surgical planning using graph CNN with dual embedding module: External validations with multi-hospital datasets

DC Field Value Language
dc.contributor.authorKim, IH-
dc.contributor.authorKim, JS-
dc.contributor.authorJeong, J-
dc.contributor.authorPark, JW-
dc.contributor.authorPark, K-
dc.contributor.authorCho, JH-
dc.contributor.authorHong, M-
dc.contributor.authorKang, KH-
dc.contributor.authorKim, M-
dc.contributor.authorKim, SJ-
dc.contributor.authorKim, YJ-
dc.contributor.authorSung, SJ-
dc.contributor.authorKim, YH-
dc.contributor.authorLim, SH-
dc.contributor.authorBaek, SH-
dc.contributor.authorKim, N-
dc.date.accessioned2023-12-11T05:42:29Z-
dc.date.available2023-12-11T05:42:29Z-
dc.date.issued2023-
dc.identifier.issn0169-2607-
dc.identifier.urihttp://repository.ajou.ac.kr/handle/201003/31997-
dc.description.abstractBackground and objective: Despite recent development of AI, prediction of the surgical movement in the maxilla and mandible by OGS might be more difficult than that of tooth movement by orthodontic treatment. To evaluate the prediction accuracy of the surgical movement using pairs of pre-(T0) and post-surgical (T1) lateral cephalograms (lat-ceph) of orthognathic surgery (OGS) patients and dual embedding module-graph convolution neural network (DEM-GCNN) model. Methods: 599 pairs from 3 institutions were used as training, internal validation, and internal test sets and 201 pairs from other 6 institutions were used as external test set. DEM-GCNN model (IEM, learning the lat-ceph images; LTEM, learning the landmarks) was developed to predict the amount and direction of surgical movement of ANS and PNS in the maxilla and B-point and Md1crown in the mandible. The distance between T1 landmark coordinates actually moved by OGS (ground truth) and predicted by DEM-GCNN model and pre-existed CNN-based Model-C (learning the lat-ceph images) was compared. Results: In both internal and external tests, DEM-GCNN did not exhibit significant difference from ground truth in all landmarks (ANS, PNS, B-point, Md1crown, all P > 0.05). When the accumulated successful detection rate for each landmark was compared, DEM-GCNN showed higher values than Model-C in both the internal and external tests. In violin plots exhibiting the error distribution of the prediction results, both internal and external tests showed that DEM-GCNN had significant performance improvement in PNS, ANS, B-point, Md1crown than Model-C. DEM-GCNN showed significantly lower prediction error values than Model-C (one-jaw surgery, B-point, Md1crown, all P < 0.005; two-jaw surgery, PNS, ANS, all P < 0.05; B point, Md1crown, all P < 0.005). Conclusion: We developed a robust OGS planning model with maximized generalizability despite diverse qualities of lat-cephs from 9 institutions.-
dc.language.isoen-
dc.subject.MESHCephalometry-
dc.subject.MESHHumans-
dc.subject.MESHMandible-
dc.subject.MESHMaxilla-
dc.subject.MESHOrthognathic Surgical Procedures-
dc.titleOrthognathic surgical planning using graph CNN with dual embedding module: External validations with multi-hospital datasets-
dc.typeArticle-
dc.identifier.pmid37857025-
dc.subject.keywordCephalometry-
dc.subject.keywordDeep learning-
dc.subject.keywordDual embedding module-
dc.subject.keywordGraph convolution neural network-
dc.subject.keywordMaxillofacial surgery-
dc.subject.keywordMulticenter study-
dc.subject.keywordOrthognathic surgery-
dc.subject.keywordSurgical prediction-
dc.contributor.affiliatedAuthorKim, YH-
dc.type.localJournal Papers-
dc.identifier.doi10.1016/j.cmpb.2023.107853-
dc.citation.titleComputer methods and programs in biomedicine-
dc.citation.volume242-
dc.citation.date2023-
dc.citation.startPage107853-
dc.citation.endPage107853-
dc.identifier.bibliographicCitationComputer methods and programs in biomedicine, 242. : 107853-107853, 2023-
dc.embargo.liftdate9999-12-31-
dc.embargo.terms9999-12-31-
dc.identifier.eissn1872-7565-
dc.relation.journalidJ001692607-
Appears in Collections:
Journal Papers > School of Medicine / Graduate School of Medicine > Dentistry
Files in This Item:
There are no files associated with this item.

qrcode

해당 아이템을 이메일로 공유하기 원하시면 인증을 거치시기 바랍니다.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse