{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T18:55:37Z","timestamp":1791658537765,"version":"4.3.4"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T00:00:00Z","timestamp":1709078400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T00:00:00Z","timestamp":1709078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Key Research and Development Program of Science and Technology Department of Sichuan Province","award":["2022YFS0038"],"award-info":[{"award-number":["2022YFS0038"]}]},{"name":"Research and Develop Program West China Hospital of Stomatology Sichuan University","award":["LCYJ2019-20"],"award-info":[{"award-number":["LCYJ2019-20"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Oral Health"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Dento-maxillofacial deformities are common problems. Orthodontic\u2013orthognathic surgery is the primary treatment but accurate diagnosis and careful surgical planning are essential for optimum outcomes. This study aimed to establish and verify a machine learning\u2013based decision support system for treatment of dento-maxillofacial malformations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Patients (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009574) with dento-maxillofacial deformities undergoing spiral CT during January 2015 to August 2020 were enrolled to train diagnostic models based on five different machine learning algorithms; the diagnostic performances were compared with expert diagnoses. Accuracy, sensitivity, specificity, and area under the curve (AUC) were calculated. The adaptive artificial bee colony algorithm was employed to formulate the orthognathic surgical plan, and subsequently evaluated by maxillofacial surgeons in a cohort of 50 patients. The objective evaluation included the difference in bone position between the artificial intelligence (AI) generated and actual surgical plans for the patient, along with discrepancies in postoperative cephalometric analysis outcomes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The binary relevance extreme gradient boosting model performed best, with diagnostic success rates\u2009&gt;\u200990% for six different kinds of dento-maxillofacial deformities; the exception was maxillary overdevelopment (89.27%). AUC was &gt;\u20090.88 for all diagnostic types. Median score for the surgical plans was 9, and was improved after human\u2013computer interaction. There was no statistically significant difference between the actual and AI- groups.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Machine learning algorithms are effective for diagnosis and surgical planning of dento-maxillofacial deformities and help improve diagnostic efficiency, especially in lower medical centers.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12903-024-04063-6","type":"journal-article","created":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T09:02:09Z","timestamp":1709110929000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Machine learning-based decision support system for orthognathic diagnosis and treatment planning"],"prefix":"10.1186","volume":"24","author":[{"given":"Wen","family":"Du","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjun","family":"Bi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaokun","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Tai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"En","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,28]]},"reference":[{"key":"4063_CR1","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/B978-1-4557-2698-1.00003-4","volume-title":"Orthognathic surgery","author":"JC Posnick","year":"2014","unstructured":"Posnick JC. 3 - definition and prevalence of Dentofacial deformities. In: Posnick JC, editor. Orthognathic surgery. St. Louis: W.B. Saunders; 2014. pp. 61\u20138."},{"key":"4063_CR2","volume-title":"Contemporary orthodontics","author":"WR Proffit","year":"2007","unstructured":"Proffit WR, Fields HW, Sarver DM. Contemporary orthodontics. 4th ed. St Louis: Mosby; 2007.","edition":"4"},{"key":"4063_CR3","doi-asserted-by":"publisher","first-page":"9104","DOI":"10.3390\/ijerph17239104","volume":"17","author":"V Lanteri","year":"2020","unstructured":"Lanteri V, Cavagnetto D, Abate A, Mainardi E, Gaffuri F, Ugolini A, et al. Buccal bone changes around First Permanent molars and Second Primary molars after Maxillary Expansion with a low compliance Ni\u2013Ti Leaf Spring Expander. Int J Environ Res Public Health. 2020;17:9104.","journal-title":"Int J Environ Res Public Health"},{"key":"4063_CR4","doi-asserted-by":"crossref","unstructured":"Abate A, Cavagnetto D, Fama A, Maspero C, Farronato G. Relationship between Breastfeeding and Malocclusion: a systematic review of the literature. Nutrients. 2020;12.","DOI":"10.3390\/nu12123688"},{"key":"4063_CR5","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.coms.2019.08.008","volume":"32","author":"K Klein","year":"2020","unstructured":"Klein K, Kaban L, Masoud M. Orthognathic surgery and orthodontics inadequate planning leading to complications or unfavorable results. Oral Maxillofac Surg Clin North Am. 2020;32:71\u201382.","journal-title":"Oral Maxillofac Surg Clin North Am"},{"key":"4063_CR6","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1186\/s12903-021-01513-3","volume":"21","author":"W Shin","year":"2021","unstructured":"Shin W, Yeom H-G, Lee GH, Yun JP, Jeong SH, Lee JH, et al. Deep learning based prediction of necessity for orthognathic surgery of skeletal malocclusion using cephalogram in Korean individuals. BMC Oral Health. 2021;21:130.","journal-title":"BMC Oral Health"},{"key":"4063_CR7","doi-asserted-by":"publisher","first-page":"2124","DOI":"10.3390\/app10062124","volume":"10","author":"K-S Lee","year":"2020","unstructured":"Lee K-S, Ryu J-J, Jang HS, Lee D-Y, Jung S-K. Deep Convolutional Neural Networks Based Analysis of Cephalometric Radiographs for Differential diagnosis of orthognathic surgery indications. Appl Sci. 2020;10:2124.","journal-title":"Appl Sci"},{"key":"4063_CR8","doi-asserted-by":"publisher","first-page":"2958","DOI":"10.1109\/JBHI.2021.3054494","volume":"25","author":"D Xiao","year":"2021","unstructured":"Xiao D, Lian C, Deng H, Kuang T, Liu Q, Ma L, et al. Estimating reference bony shape models for Orthognathic Surgical Planning using 3D point-cloud deep learning. IEEE J Biomed Health Inf. 2021;25:2958\u201366.","journal-title":"IEEE J Biomed Health Inf"},{"key":"4063_CR9","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/j.ijom.2010.10.013","volume":"40","author":"JM Plooij","year":"2011","unstructured":"Plooij JM, Maal TJJ, Haers P, Borstlap WA, Kuijpers-Jagtman AM, Berg\u00e9 SJ. Digital three-dimensional image fusion processes for planning and evaluating orthodontics and orthognathic surgery. A systematic review. Int J Oral Maxillofac Surg. 2011;40:341\u201352.","journal-title":"Int J Oral Maxillofac Surg"},{"key":"4063_CR10","doi-asserted-by":"publisher","first-page":"107917","DOI":"10.1016\/j.cmpb.2023.107917","volume":"243","author":"R Ryypp\u00f6","year":"2024","unstructured":"Ryypp\u00f6 R, H\u00e4yrynen S, Joutsijoki H, Juhola M, Sepp\u00e4nen MRJ. Comparison of machine learning methods in the early identification of vasculitides, myositides and glomerulonephritides. Comput Methods Programs Biomed. 2024;243:107917.","journal-title":"Comput Methods Programs Biomed"},{"key":"4063_CR11","first-page":"28","volume":"72","author":"S Haraguchi","year":"2002","unstructured":"Haraguchi S, Takada K, Yasuda Y. Facial asymmetry in subjects with skeletal class III deformity. Angle Orthod. 2002;72:28\u201335.","journal-title":"Angle Orthod"},{"key":"4063_CR12","doi-asserted-by":"crossref","unstructured":"Naini FB, Gill DS. Orthognathic Surgery: Principles, Planning and Practice. 1st edition. Newark: Newark: John Wiley & Sons, Incorporated; 2017.","DOI":"10.1002\/9781119004370"},{"key":"4063_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jcms.2019.01.039","volume":"48","author":"W Du","year":"2020","unstructured":"Du W, Yang M, Liu H, Ji H, Xu C, Luo E. Treatment of hemimandibular hyperplasia by computer-aided design and computer-aided manufacturing cutting and drilling guides accompanied with pre-bent titanium plates. J Craniomaxillofac Surg. 2020;48:1.","journal-title":"J Craniomaxillofac Surg"},{"key":"4063_CR14","doi-asserted-by":"publisher","first-page":"3115","DOI":"10.1007\/s00784-018-2732-3","volume":"23","author":"D Schneider","year":"2019","unstructured":"Schneider D, K\u00e4mmerer PW, Hennig M, Sch\u00f6n G, Thiem DGE, Bschorer R. Customized virtual surgical planning in bimaxillary orthognathic surgery: a prospective randomized trial. Clin Oral Investig. 2019;23:3115\u201322.","journal-title":"Clin Oral Investig"},{"key":"4063_CR15","doi-asserted-by":"crossref","unstructured":"Kato RM, Parizotto J, de OL, Oliveira PHJ, Gon\u00e7alves JR. Artificial Intelligence in Orthognathic surgery \u2013 a narrative review of Surgical Digital Tools and 3D Orthognathic Surgical Planning. J Calif Dent Assoc. 2023;51.","DOI":"10.1080\/19424396.2023.2202444"},{"key":"4063_CR16","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1177\/0022034520901715","volume":"99","author":"HJ Yu","year":"2020","unstructured":"Yu HJ, Cho SR, Kim MJ, Kim WH, Kim JW, Choi J. Automated skeletal classification with lateral cephalometry based on Artificial Intelligence. J Dent Res. 2020;99:249\u201356.","journal-title":"J Dent Res"},{"key":"4063_CR17","doi-asserted-by":"publisher","first-page":"1986","DOI":"10.1097\/SCS.0000000000005650","volume":"30","author":"H-I Choi","year":"2019","unstructured":"Choi H-I, Jung S-K, Baek S-H, Lim WH, Ahn S-J, Yang I-H, et al. Artificial Intelligent Model with neural network machine learning for the diagnosis of orthognathic surgery. J Craniofac Surg. 2019;30:1986\u20139.","journal-title":"J Craniofac Surg"},{"key":"4063_CR18","doi-asserted-by":"publisher","first-page":"957","DOI":"10.1016\/j.jds.2020.10.012","volume":"16","author":"S Takeda","year":"2021","unstructured":"Takeda S, Mine Y, Yoshimi Y, Ito S, Tanimoto K, Murayama T. Landmark annotation and mandibular lateral deviation analysis of posteroanterior cephalograms using a convolutional neural network. J Dent Sci. 2021;16:957\u201363.","journal-title":"J Dent Sci"},{"key":"4063_CR19","doi-asserted-by":"publisher","first-page":"356","DOI":"10.3390\/jpm11050356","volume":"11","author":"Y-H Kim","year":"2021","unstructured":"Kim Y-H, Park J-B, Chang M-S, Ryu J-J, Lim WH, Jung S-K. Influence of the depth of the convolutional neural networks on an Artificial Intelligence Model for diagnosis of orthognathic surgery. J Pers Med. 2021;11:356.","journal-title":"J Pers Med"},{"key":"4063_CR20","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1016\/j.patrec.2020.05.035","volume":"136","author":"C Wang","year":"2020","unstructured":"Wang C, Deng C, Wang S. Imbalance-XGBoost: leveraging weighted and focal losses for binary label-imbalanced classification with XGBoost. Pattern Recognit Lett. 2020;136:190\u20137.","journal-title":"Pattern Recognit Lett"},{"key":"4063_CR21","doi-asserted-by":"publisher","first-page":"JAMDSM0075","DOI":"10.1299\/jamdsm.2021jamdsm0075","volume":"15","author":"M Ding","year":"2021","unstructured":"Ding M, Zhao L, Pei H, Song M. An XGBoost based evaluation methodology of product color emotion design. JAMDSM. 2021;15:JAMDSM0075\u20135.","journal-title":"JAMDSM"},{"key":"4063_CR22","doi-asserted-by":"publisher","first-page":"5049","DOI":"10.1007\/s00784-023-05124-w","volume":"27","author":"EC Olivetti","year":"2023","unstructured":"Olivetti EC, Marcolin F, Moos S, Vezzetti E, Borbon C, Zavattero E, et al. Do facial soft tissue thicknesses change after surgeries correcting dental malocclusions? An intra- and inter-patient statistical analysis on soft-tissue thicknesses in BSSO\u2009+\u2009LFI surgeries. Clin Oral Invest. 2023;27:5049\u201362.","journal-title":"Clin Oral Invest"},{"key":"4063_CR23","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1016\/j.cor.2011.06.007","volume":"39","author":"W Gao","year":"2012","unstructured":"Gao W, Liu S. A modified artificial bee colony algorithm. COMPUT OPER RES. 2012;39:687\u201397.","journal-title":"COMPUT OPER RES"},{"key":"4063_CR24","doi-asserted-by":"publisher","first-page":"3695","DOI":"10.1007\/s10489-020-01741-0","volume":"50","author":"F Boudardara","year":"2020","unstructured":"Boudardara F, Gorkemli B. Solving artificial ant problem using two artificial bee colony programming versions. Appl Intell. 2020;50:3695\u2013717.","journal-title":"Appl Intell"},{"key":"4063_CR25","doi-asserted-by":"publisher","first-page":"1271","DOI":"10.1007\/s12065-019-00231-8","volume":"14","author":"S Sharma","year":"2021","unstructured":"Sharma S, Kumar S, Sharma K. Improved gbest artificial bee colony algorithm for the constraints optimization problems. Evol Intel. 2021;14:1271\u20137.","journal-title":"Evol Intel"},{"key":"4063_CR26","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1016\/j.asoc.2018.04.015","volume":"68","author":"H Liu","year":"2018","unstructured":"Liu H, Xu B, Lu D, Zhang G. A path planning approach for crowd evacuation in buildings based on improved artificial bee colony algorithm. Appl Soft Comput. 2018;68:360\u201376.","journal-title":"Appl Soft Comput"},{"key":"4063_CR27","doi-asserted-by":"publisher","first-page":"5227","DOI":"10.1007\/s00784-021-03830-x","volume":"25","author":"W Lu","year":"2021","unstructured":"Lu W, Song G, Sun Q, Peng L, Zhang Y, Wei Y, et al. Analysis of facial features and prediction of lip position in skeletal class III malocclusion adult patients undergoing surgical-orthodontic treatment. Clin Oral Invest. 2021;25:5227\u201338.","journal-title":"Clin Oral Invest"},{"key":"4063_CR28","doi-asserted-by":"publisher","first-page":"7752","DOI":"10.3390\/s22207752","volume":"22","author":"A Thurzo","year":"2022","unstructured":"Thurzo A, Strunga M, Havl\u00ednov\u00e1 R, Reh\u00e1kov\u00e1 K, Urban R, Surovkov\u00e1 J, et al. Smartphone-based facial scanning as a viable Tool for Facially Driven Orthodontics? Sensors (Basel. Switzerland). 2022;22:7752.","journal-title":"Switzerland)"}],"container-title":["BMC Oral Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12903-024-04063-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12903-024-04063-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12903-024-04063-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,2]],"date-time":"2024-03-02T10:04:54Z","timestamp":1709373894000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcoralhealth.biomedcentral.com\/articles\/10.1186\/s12903-024-04063-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,28]]},"references-count":28,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["4063"],"URL":"https:\/\/doi.org\/10.1186\/s12903-024-04063-6","relation":{},"ISSN":["1472-6831"],"issn-type":[{"value":"1472-6831","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,28]]},"assertion":[{"value":"6 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 February 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 February 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was conducted in the West China Hospital of Stomatology of Sichuan University and was approved by the Investigational Review Board of West China Hospital of Stomatology (WCHSIRB-OT-2019-125). All procedures were in accordance with relevant guidelines and regulations. The need for informed consent was waived by the Investigational Review Board of West China Hospital of Stomatology.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"286"}}