[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121823-en":3,"doc-seo-121823-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121823,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Machine learning for the classification of surgical patients in orthodontics - Presentation slides","Dentofacial anomalies (malocclusions) arise from congenital, traumatic, or growth-related causes and can lead to functional impairment, aesthetic concerns, and psychosocial effects. Clinical diagnosis and treatment selection require extensive cephalometric and clinical data and are still influenced by clinician experience, with no standardized classification process for deciding between orthodontic-only care and surgical intervention. This study proposes a digital tool using machine learning to support treatment selection for patients beginning orthodontic care.","ORCA – Online Research @ Cardiff  \nThis is an Open Access document downloaded from ORCA, Cardiff University' s institution al r epo sitory: [http s://orc a . c ardiff. ac. uk/id/ e print/ 1 6 7 0 6 2 /](http s://orc a . c ardiff. ac. uk/id/ e print/ 1 6 7 0 6 2 /)  \nThis is the author’s version of a work that was submitted to / accepted for  \npublication .  \nCitation for final published version:  \nFerro-S á nchez , Carlos Andr é s , Dí az-Laverde , Christian Orlando , Romero Cano, Victor, Campo , Oscar and Gonz ález-Vargas, Andr é s Mauricio 2 0 2 4 . Machine learning for the cla ssification of surgical p atient s in orthodontics. Pre sente d at : IX Latin American Congress on Biomedical Engineering and XXVIII Brazilian Congress on Biomedical Engineering , Florian ó polis, Brazil, 2 4-2 8 October 2 0 2 2 . IX Latin American Congress on Biomedical Engineering and XXVIII Brazilian Congress on Biomedical Engineering. CLAIB CBEB 2 0 2 2 2 0 2 2 . IF MBE Proceedings, vol 9 9 . , vol. 9 9 Cham: Springer, pp . 2 0 7-2 1 7 . 1 0 . 1 0 0 7 / 9 7 8-3-0 3 1-4 9 4 0 4-8 _ 2 1  \nPublishers p age : [http :// dx. doi.org / 1 0. 1 0 0 7 / 9 7 8-3-0 3 1-4 9 4 0 4-8_ 2 1](http :// dx. doi.org / 1 0. 1 0 0 7 / 9 7 8-3-0 3 1-4 9 4 0 4-8_ 2 1)  \nPlea se note :  \nChange s m ade a s a result of publishing processes such a s copy-editing, formatting and p age numbers m ay not be reflected in this version. For the definitive version of this publication, plea se refer to the published source. You are advised to consult the  \npublisher’s version if you wish to cite this p aper.  \nThis version is being m ade available in accord ance with publisher policies. See [http://orca . cf. ac. uk/ policies. html](http://orca . cf. ac. uk/ policies. html) for u s age policies. Copyright and mor al right s for publications m ade available in ORCA are retained by the copyright holders.  \nMachine Learning for the Classi􀀂cation of Surgical Patients in Orthodontics  \nCarlos Andr´es Ferro-S´anchez1 [0009􀀀0004􀀀4872􀀀9869], Christian Orlando  \nD´􀀑az-Laverde2 [0000􀀀0003􀀀0776􀀀5404], Victor Romero-Cano1 [0000􀀀0003􀀀2910􀀀5116], Oscar Campo1 [0000􀀀0002􀀀5007􀀀9613] and Andr´es Mauricio Gonz´alez-Vargas1 [0000􀀀0001􀀀6393􀀀7130]  \n1 Universidad Aut´onoma de Occidente, Faculty of Engineering, Cali, Colombia, 2 Universidad del Valle, Health Faculty, Cali, Colombia,  \n1 Corresponding Author: [amgonzalezv@uao.edu.co](amgonzalezv@uao.edu.co)  \nAbstract. Dentofacial anomalies, also known as malocclusions, are alterations with a congenital, traumatic, or growth origin. These anomalies can generate functional and aesthetic problems in those who suffer from them and have been reported by the World Health Organization as the third most prevalent oral disease. The most commonly used methods for correcting these anomalies are orthodontics and orthognathic surgery. The diagnosis, and the correct selection of the treatment to be carried out, are part of an extensive process that involves collecting different cephalometric and clinical data, and depend on the clinician’s experience. Therefore, no standardized process allows the classi􀀂cation or diagnosis among patients who achieve the best result with orthodontics, that is, non-surgical procedures or if surgical intervention is necessary. This study aims to propose a digital tool based on machine learning algorithms that may help the clinician to select an orthodontics or surgical treatment for patients who are about to start their treatment.  \nKeywords: Orthodontic, Machine learning, Malocclusion, Surgical, Cephalometric, Classi􀀂cation  \n1 Introduction  \nMalocclusion is an anomaly that is characterized by the alteration of craniofacial growth, or the presence of a poor relationship or misalignment between the upper and lower dental arches concerning the transverse or vertical anteroposterior planes [1], which can generate functional problems, aesthetic and psychosocial, and affect social development or emotional wellbeing in both children and adults","cbCaia4fEEmZGGsb","https://ap.wps.com/l/cbCaia4fEEmZGGsb","pdf",263355,1,13,"English","en",105,"# Abstract\n# 1 Introduction\n## Malocclusion description and impacts\n## Current correction methods and Angle classification\n## Prevalence of malocclusion and regional reports\n## Diagnosis, treatment planning, and limitations","[{\"question\":\"What problem does the document address in orthodontics?\",\"answer\":\"It addresses the need to improve classification and diagnosis of malocclusion patients to decide whether orthodontic treatment alone is sufficient or surgery is necessary.\"},{\"question\":\"Why is treatment selection difficult using traditional processes?\",\"answer\":\"Diagnosis and treatment planning rely on collecting multiple cephalometric and clinical data and are dependent on clinician experience, with no standardized process to guide classification and decision-making.\"},{\"question\":\"What solution does the study propose?\",\"answer\":\"It proposes a digital tool based on machine learning algorithms to help clinicians select orthodontic or surgical treatment for patients about to start treatment.\"}]","Machine learning for the classification of surgical patients in orthodontics - 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