Xiaoyu Wang1,2, Matthew Pastewait3, Tai-Hsien Wu4, Chunfeng Lian5, Beatriz Tejera6, Yan-Ting Lee7, Feng-Chang Lin8, Li Wang9, Dinggang Shen10,11,12, Song Li1, Ching-Chang Ko4. 1. Department of Orthodontics, Beijing Stomatological Hospital, Capital Medical University, Beijing, China. 2. Department of Stomatology, Beijing Tian Tan Hospital, Capital Medical University, Beijing, China. 3. United States Air Force, Kadena AB, Kadena, Japan. 4. Division of Orthodontics, College of Dentistry, The Ohio State University, Columbus, OH, USA. 5. School of Mathematics and Statistics, Xi'an Jiaotong University, Shaanxi, China. 6. Orthodontics, Nova Southeastern University, Ft. Lauderdale, FL, USA. 7. Oral and Craniofacial Health Sciences Research, Adam School of Dentistry, University of North Carolina, Chapel Hill, NC, USA. 8. Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA. 9. Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA. 10. Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China. 11. School of Biomedical Engineering, ShanghaiTech University, Shanghai, China. 12. Department of Artificial Intelligence, Korea University, Seoul, Korea.
Abstract
OBJECTIVE: This study aimed to quantify the 3D asymmetry of the maxilla in patients with unilateral cleft lip and palate (UCP) and investigate the defect factors responsible for the variability of the maxilla on the cleft side using a deep-learning-based CBCT image segmentation protocol. SETTING AND SAMPLE POPULATION: Cone beam computed tomography (CBCT) images of 60 patients with UCP were acquired. The samples in this study consisted of 39 males and 21 females, with a mean age of 11.52 years (SD = 3.27 years; range of 8-18 years). MATERIALS AND METHODS: The deep-learning-based protocol was used to segment the maxilla and defect initially, followed by manual refinement. Paired t-tests were performed to characterize the maxillary asymmetry. A multiple linear regression was carried out to investigate the relationship between the defect parameters and those of the cleft side of the maxilla. RESULTS: The cleft side of the maxilla demonstrated a significant decrease in maxillary volume and length as well as alveolar length, anterior width, posterior width, anterior height and posterior height. A significant increase in maxillary anterior width was demonstrated on the cleft side of the maxilla. There was a close relationship between the defect parameters and those of the cleft side of the maxilla. CONCLUSIONS: Based on the 3D volumetric segmentations, significant hypoplasia of the maxilla on the cleft side existed in the pyriform aperture and alveolar crest area near the defect. The defect structures appeared to contribute to the variability of the maxilla on the cleft side.
OBJECTIVE: This study aimed to quantify the 3D asymmetry of the maxilla in patients with unilateral cleft lip and palate (UCP) and investigate the defect factors responsible for the variability of the maxilla on the cleft side using a deep-learning-based CBCT image segmentation protocol. SETTING AND SAMPLE POPULATION: Cone beam computed tomography (CBCT) images of 60 patients with UCP were acquired. The samples in this study consisted of 39 males and 21 females, with a mean age of 11.52 years (SD = 3.27 years; range of 8-18 years). MATERIALS AND METHODS: The deep-learning-based protocol was used to segment the maxilla and defect initially, followed by manual refinement. Paired t-tests were performed to characterize the maxillary asymmetry. A multiple linear regression was carried out to investigate the relationship between the defect parameters and those of the cleft side of the maxilla. RESULTS: The cleft side of the maxilla demonstrated a significant decrease in maxillary volume and length as well as alveolar length, anterior width, posterior width, anterior height and posterior height. A significant increase in maxillary anterior width was demonstrated on the cleft side of the maxilla. There was a close relationship between the defect parameters and those of the cleft side of the maxilla. CONCLUSIONS: Based on the 3D volumetric segmentations, significant hypoplasia of the maxilla on the cleft side existed in the pyriform aperture and alveolar crest area near the defect. The defect structures appeared to contribute to the variability of the maxilla on the cleft side.