Prediction of surgery-first approach orthognathic surgery using deep learning models.
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Cefalometría
/
Imagenología Tridimensional
/
Procedimientos Quirúrgicos Ortognáticos
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Aprendizaje Profundo
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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