3D morphometric quantification of maxillae and defects for patients with unilateral cleft palate via deep learning‐based CBCT image auto‐segmentation

Wang, Xiaoyu; Pastewait, Matthew; Wu, Tai-Hsien; Tejera, Beatriz; Lee, Yan Ting; Lin, Feng; Wang, Li; Shen, Dinggang; Li, Song; Ko, Ching Chang

doi:10.1111/ocr.12482

Cited by 16 publications

(28 citation statements)

References 25 publications

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“…The anterior and posterior width of the maxilla on cleft sides were larger than those on non-cleft sides. The above results of our study agreed with the previous studies [ 10 , 12 ]. For those asymmetries, Agarwal et al [ 22 ] explained it based on the dysplasia of UCLP: the bone clefts made the lateral incisors and/or the canines absent in the defect sides, along with the alveolar missing and the premaxillary portion of the defect side was skewed along with the incisors, which shortened the alveolar vertical height and maxillary and alveolar length; the deformed pyriform margin derived from the attachment of the accessory cartilages and the lateral crus served to pull down the nose further caudally, leading to a tilting of the nasal tripod toward the cleft sides and then the width of maxillary increased.…”

Section: Discussionsupporting

confidence: 94%

“…For 3D morphometric quantification, we determined landmarks by referring to previous literatures [ 10 ] and made some adjustments according to the characteristics of Spiral CT, as defined in Table 1 and illustrated in Figure 1 A, B. Landmarks were identified on the surface of the 3D segmented model and verified in the multiple planar reformat mode.…”

Section: Methodsmentioning

confidence: 99%

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Machine learning in 3D auto-filling alveolar cleft of CT images to assess the influence of alveolar bone grafting on the development of maxilla

et al. 2023

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Background Machine learning based auto-segmentation of 3D images has been developed rapidly in recent years. However, the application of this new method in the research of patients with unilateral cleft lip and palate (UCLP) is very limited. In this study, a machine learning algorithm utilizing 3D U-net was used to automatically segment the maxilla, fill the cleft and evaluate the alveolar bone graft in UCLP patients. Cleft related factors and the surgery impact on the development of maxilla were analyzed. Methods Preoperative and postoperative computed tomography images of 32 patients (64 images) were obtained. The deep-learning-based protocol was used to segment the maxilla and defect, followed by manual refinement. Paired t-tests and Mann-Whitney tests were performed to reveal the changes of the maxilla after surgery. Two-factor, two-level analysis for repeated measurement was used to examine the different trends of growth on the cleft and non-cleft sides of the maxilla. Pearson and Spearman correlations were used to explore the relationship between the defect and the changes of the maxillary cleft side. Results One-year after the alveolar bone grafting surgery, different growth amount was found on the cleft and non-cleft sides of maxilla. The maxillary length (from 34.64 ± 2.48 to 35.67 ± 2.45 mm) and the alveolar length (from 36.58 ± 3.21 to 37.63 ± 2.94 mm) increased significantly only on the cleft side while the maxillary anterior width (from 11.61 ± 1.61 to 12.01 ± 1.41 mm) and posterior width (from 29.63 ± 2.25 to 30.74 ± 2.63 mm) increased significantly only on the non-cleft side after surgery. Morphology of the cleft was found to be related to the pre-surgical maxillary dimension on the cleft side, while its correlation with the change of the maxilla after surgery was low or not statistically significant. Conclusion The auto-segmentation of the maxilla and the cleft could be performed very efficiently and accurately with the machine learning method. Asymmetric growth was found on the cleft and non-cleft sides of the maxilla after alveolar bone graft in UCLP patients. The morphology of the cleft mainly contributed to the pre-operation variance of the maxilla but had little impact on the maxilla growth after surgery.

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Section: Discussionsupporting

confidence: 94%

Section: Methodsmentioning

confidence: 99%

Machine learning in 3D auto-filling alveolar cleft of CT images to assess the influence of alveolar bone grafting on the development of maxilla

et al. 2023

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“…The latter publication also determined that it is possible to predict the future need for surgery to correct sagittal skeletal discrepancy in patients with repaired unilateral cleft lip and palate at the age of 6 years. 22 For those patients with unilateral cleft lip and palate, AI can be also useful to segment the maxilla and quantify its 3D asymmetry, as was demonstrated by Wang et al (2021) 23 with an intra-class correlation coefficient (ICC) greater than 0.90.…”

Section: Ta B L E 3 (Continued)mentioning

confidence: 97%

Artificial intelligence in orthodontics: Where are we now? A scoping review

Monill-González

Rovira-Calatayud

d'Oliveira

et al. 2021

Orthod Craniofacial Res

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Objective This scoping review aims to determine the applications of Artificial Intelligence (AI) that are extensively employed in the field of Orthodontics, to evaluate its benefits, and to discuss its potential implications in this speciality. Recent decades have witnessed enormous changes in our profession. The arrival of new and more aesthetic options in orthodontic treatment, the transition to a fully digital workflow, the emergence of temporary anchorage devices and new imaging methods all provide both patients and professionals with a new focus in orthodontic care. Materials and methods This review was performed following the Preferred Reporting Items for Systematic reviews and Meta‐Analyses extension for Scoping Reviews (PRISMA‐ScR) guidelines. The electronic literature search was performed through MEDLINE/PubMed, Scopus, Web of Science, Cochrane and IEEE Xplore databases with a 11‐year time restriction: January 2010 till March 2021. No additional manual searches were performed. Results The electronic literature search initially returned 311 records, and 115 after removing duplicate references. Finally, the application of the inclusion criteria resulted in 17 eligible publications in the qualitative synthesis review. Conclusion The analysed studies demonstrated that Convolution Neural Networks can be used for the automatic detection of anatomical reference points on radiological images. In the growth and development research area, the Cervical Vertebral Maturation stage can be determined using an Artificial Neural Network model and obtain the same results as expert human observers. AI technology can also improve the diagnostic accuracy for orthodontic treatments, thereby helping the orthodontist work more accurately and efficiently.

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“…22 Wang et al evaluated reconstructed 3D images of the maxillary bones of cleft lip and palate (CL/P) patients and described their defective phenotypes. 23 Sin et al applied this AI process in the delineation of the airway morphology. 24 Niu et al aslo evaluated the nasal cavity and pharyngeal airway in the Rapid palatal expansion treatment.…”

Section: G U E S T E D I T O R I a L Artificial Intelligence And Mach...mentioning

confidence: 99%

“…In this issue, Lo Giudice et al developed a method that enables the automatic segmentation of the mandible 22 . Wang et al evaluated reconstructed 3D images of the maxillary bones of cleft lip and palate (CL/P) patients and described their defective phenotypes 23 . Sin et al applied this AI process in the delineation of the airway morphology 24 .…”

mentioning

confidence: 99%