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Prediction of surgery-first approach orthognathic surgery using deep learning models.
Chang, J-S; Ma, C-Y; Ko, E W-C.
Afiliación
  • Chang JS; Graduate Institute of Dental and Craniofacial Science, Chang Gung University, Taoyuan, Taiwan; Department of Craniofacial Orthodontics, Chang Gung Memorial Hospital, Taipei, Taiwan.
  • Ma CY; Department of Artificial Intelligence, Chang Gung University, Taoyuan, Taiwan; Artificial Intelligence Research Center, Chang Gung University, Taoyuan, Taiwan; Division of Rheumatology, Allergy and Immunology, Chang Gung Memorial Hospital, Taoyuan, Taiwan.
  • Ko EW; Graduate Institute of Dental and Craniofacial Science, Chang Gung University, Taoyuan, Taiwan; Department of Craniofacial Orthodontics, Chang Gung Memorial Hospital, Taipei, Taiwan; Craniofacial Research Center, Chang Gung Memorial Hospital, Linkou, Taiwan. Electronic address: ellenko.wc@msa.hinet.net.
Int J Oral Maxillofac Surg ; 53(11): 942-949, 2024 Nov.
Article en En | MEDLINE | ID: mdl-38821731
ABSTRACT
The surgery-first approach (SFA) orthognathic surgery can be beneficial due to reduced overall treatment time and earlier profile improvement. The objective of this study was to utilize deep learning to predict the treatment modality of SFA or the orthodontics-first approach (OFA) in orthognathic surgery patients and assess its clinical accuracy. A supervised deep learning model using three convolutional neural networks (CNNs) was trained based on lateral cephalograms and occlusal views of 3D dental model scans from 228 skeletal Class III malocclusion patients (114 treated by SFA and 114 by OFA). An ablation study of five groups (lateral cephalogram only, mandible image only, maxilla image only, maxilla and mandible images, and all data combined) was conducted to assess the influence of each input type. The results showed the average validation accuracy, precision, recall, F1 score, and AUROC for the five folds were 0.978, 0.980, 0.980, 0.980, and 0.998 ; the average testing results for the five folds were 0.906, 0.986, 0.828, 0.892, and 0.952. The lateral cephalogram only group had the least accuracy, while the maxilla image only group had the best accuracy. Deep learning provides a novel method for an accelerated workflow, automated assisted decision-making, and personalized treatment planning.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Cefalometría / Imagenología Tridimensional / Procedimientos Quirúrgicos Ortognáticos / Aprendizaje Profundo / Maloclusión de Angle Clase III Límite: Female / Humans / Male Idioma: En Revista: Int J Oral Maxillofac Surg Asunto de la revista: ODONTOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Taiwán Pais de publicación: Dinamarca

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Cefalometría / Imagenología Tridimensional / Procedimientos Quirúrgicos Ortognáticos / Aprendizaje Profundo / Maloclusión de Angle Clase III Límite: Female / Humans / Male Idioma: En Revista: Int J Oral Maxillofac Surg Asunto de la revista: ODONTOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Taiwán Pais de publicación: Dinamarca