0 58

Cited 0 times in

Subregional pharyngeal changes after orthognathic surgery in skeletal Class III patients analyzed by convolutional neural networks-based segmentation

 Dong-Yul Kim  ;  Seoyeon Woo  ;  Jae-Yon Roh  ;  Jin-Young Choi  ;  Kyung-A Kim  ;  Jung-Yul Cha  ;  Namkug Kim  ;  Su-Jung Kim 
 JOURNAL OF DENTISTRY, Vol.135 : 104565, 2023-08 
Journal Title
Issue Date
Adolescent ; Adult ; Artificial Intelligence ; Cone-Beam Computed Tomography / methods ; Humans ; Malocclusion, Angle Class III* / diagnostic imaging ; Malocclusion, Angle Class III* / surgery ; Neural Networks, Computer ; Orthognathic Surgery* ; Pharynx / diagnostic imaging ; Young Adult
Artificial intelligence ; Cone-beam computed tomography ; Convolutional neural networks (CNNs) model ; Orthognathic surgery ; Pharyngeal airway ; Skeletal Class III
Objectives: To evaluate the accuracy of fully automatic segmentation of pharyngeal volume of interests (VOIs) before and after orthognathic surgery in skeletal Class III patients using a convolutional neural network (CNN) model and to investigate the clinical applicability of artificial intelligence for quantitative evaluation of treatment changes in pharyngeal VOIs.

Methods: 310 cone-beam computed tomography (CBCT) images were divided into a training set (n = 150), validation set (n = 40), and test set (n = 120). The test datasets comprised matched pairs of pre- and post-treatment images of 60 skeletal Class III patients (mean age 23.1 ± 5.0 years; ANB<-2⁰) who underwent bimaxillary orthognathic surgery with orthodontic treatment. A 3D U-Net CNNs model was applied for fully automatic segmentation and measurement of subregional pharyngeal volumes of pre-treatment (T0) and post-treatment (T1) scans. The model's accuracy was compared to semi-automatic segmentation outcomes by humans using the dice similarity coefficient (DSC) and volume similarity (VS). The correlation between surgical skeletal changes and model accuracy was obtained.

Results: The proposed model achieved high performance of subregional pharyngeal segmentation on both T0 and T1 images, representing a significant T1-T0 difference of DSC only in the nasopharynx. Region-specific differences amongst pharyngeal VOIs, which were observed at T0, disappeared on the T1 images. The decreased DSC of nasopharyngeal segmentation after treatment was weakly correlated with the amount of maxillary advancement. There was no correlation between the mandibular setback amount and model accuracy.

Conclusions: The proposed model offers fast and accurate subregional pharyngeal segmentation on both pre-treatment and post-treatment CBCT images in skeletal Class III patients.

Clinical significance: We elucidated the clinical applicability of the CNNs model to quantitatively evaluate subregional pharyngeal changes after surgical-orthodontic treatment, which offers a basis for developing a fully integrated multiclass CNNs model to predict pharyngeal responses after dentoskeletal treatments.
Full Text
Appears in Collections:
2. College of Dentistry (치과대학) > Dept. of Orthodontics (교정과학교실) > 1. Journal Papers
Yonsei Authors
Cha, Jung Yul(차정열)
사서에게 알리기


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