@article{Bianchi2022b,

title = {Automatic multi-anatomical skull structure segmentation of cone-beam computed tomography scans during 3D UNETR},

author = {Gillot M and Baquero B and Le C and R Deleat-Besson and Bianchi J and Gurgel M and Yatabe M and Al Turkestani N and Najarian K},

url = {https://pubmed.ncbi.nlm.nih.gov/36223330/},

doi = {10.1371/journal.pone.0275033},

year  = {2022},

date = {2022-10-12},

journal = {PLoS One},

volume = {17},

issue = {10},

abstract = {The segmentation of medical and dental images is a fundamental step in automated clinical decision support systems. It supports the entire clinical workflow from diagnosis, therapy planning, intervention, and follow-up. In this paper, we propose a novel tool to accurately process a full-face segmentation in about 5 minutes that would otherwise require an average of 7h of manual work by experienced clinicians. This work focuses on the integration of the state-of-the-art UNEt TRansformers (UNETR) of the Medical Open Network for Artificial Intelligence (MONAI) framework. We trained and tested our models using 618 de-identified Cone-Beam Computed Tomography (CBCT) volumetric images of the head acquired with several parameters from different centers for a generalized clinical application. Our results on a 5-fold cross-validation showed high accuracy and robustness with a Dice score up to 0.962±0.02. Our code is available on our public GitHub repository.},

keywords = {3d, CBCT, Cone-beam computed tomography (CBCT), multi-anatomical skull structure, structure segmentation},

pubstate = {published},

tppubtype = {article}

}

@article{Oh2022b,

title = {Automated landmark identification on one cone beam computed tomography: Accuracy and reliability},

author = {A Ghowsi and D Hatcher and H Suh and D Wiled and W Castro and J Krueger and J Park and H Oh},

url = {https://pubmed.ncbi.nlm.nih.gov/35653226/},

doi = {10.2319/122121-928.1},

year  = {2022},

date = {2022-06-02},

urldate = {2022-06-02},

journal = {Angle Orthodontist},

volume = {92},

pages = {642-654},

abstract = {Objectives: To evaluate the accuracy and reliability of a fully automated landmark identification (ALI) system as a tool for automatic landmark location compared with human judges.



Materials and methods: A total of 100 cone-beam computed tomography (CBCT) images were collected. After the calibration procedure, two human judges identified 53 landmarks in the x, y, and z coordinate planes on CBCTs using Checkpoint Software (Stratovan Corporation, Davis, Calif). The ground truth was created by averaging landmark coordinates identified by two human judges for each landmark. To evaluate the accuracy of ALI, the mean absolute error (mm) at the x, y, and z coordinates and mean error distance (mm) between the human landmark identification and the ALI were determined, and a successful detection rate was calculated.



Results: Overall, the ALI system was as successful at landmarking as the human judges. The ALI's mean absolute error for all coordinates was 1.57 mm on average. Across all three coordinate planes, 94% of the landmarks had a mean absolute error of less than 3 mm. The mean error distance for all 53 landmarks was 3.19 ± 2.6 mm. When applied to 53 landmarks on 100 CBCTs, the ALI system showed a 75% success rate in detecting landmarks within a 4-mm error distance range.



Conclusions: Overall, ALI showed clinically acceptable mean error distances except for a few landmarks. The ALI was more precise than humans when identifying landmarks on the same image at different times. This study demonstrates the promise of ALI in aiding orthodontists with landmark identifications on CBCTs.},

keywords = {3D landmark identification, AAOF, accuracy, Automated, CBCT, Landmark error, Reliability},

pubstate = {published},

tppubtype = {article}

}

@article{Oh2022g,

title = {Condylar degeneration in anterior open bite patients: A cone-beam computed tomography (CBCT) study.},

author = {L Phi and B Albertson and D Hatcher and S Rathi and J Park and H Oh },

url = {https://pubmed.ncbi.nlm.nih.gov/34503937/},

doi = {10.1016/j.oooo.2021.07.019},

year  = {2022},

date = {2022-02-01},

journal = {Oral Surgery Oral Med Oral Path Oral Radiology },

volume = {133},

issue = {2},

pages = {221-228},

abstract = {Objectives: The purpose of this study was to investigate the prevalence of condylar degeneration in patients with anterior open bites (AOB).



Study design: Cone beam computed tomography (CBCT) scans of 194 patients with AOB (108 with skeletal open bites and 86 with dental open bites) and 100 patients serving as controls were included in this retrospective study. Two oral and maxillofacial radiologists categorized each of the 588 condyles as normal, degenerative-active, or degenerative-repair. The χ2 analysis with Bonferroni adjustment was used to evaluate the relationship of condylar status (normal vs degenerative) to anterior open bites.



Results: Of the 103 degenerative condyles, there were 59 in the group with skeletal open bites, 14 in the group with dental open bites, and 30 in the control group. Condylar degeneration occurred twice as frequently in patients with skeletal open bites as it did in the control group (P < .0001). Conversely, a greater frequency of normal condyles was found in the group of patients with dental open bites (P = .0002). The group with skeletal open bites also showed a significantly higher frequency of bilateral degenerative condyles (P = .0001). The frequency of condylar degeneration did not differ significantly between female and male individuals.



Conclusions: Degenerative condylar change was significantly more likely in patients with skeletal open bites and less likely in patients with dental open bites.},

keywords = {anterior openbite, CBCT, condylar degeneration, Cone-beam computed tomography},

pubstate = {published},

tppubtype = {article}

}

@article{Bianchi2022f,

title = {Dental long axes using digital dental models compared to cone-beam computed tomography},

author = {Cong A and Massaro C and Ruellas A.C and de O and Barkley M and Yatabe M and Bianchi J and Ioshida M and Alvarez M.A and Aristizabal J.F and Rey D},

url = {https://pubmed.ncbi.nlm.nih.gov/33966340/},

doi = {10.1111/ocr.12489},

year  = {2022},

date = {2022-02-01},

journal = {Orthod Craniofac Res},

volume = {25},

issue = {1},

pages = {64-72},

abstract = {Objective: Standard methods of evaluating tooth long axes are not comparable (digital dental models [DDMs], panoramic and cephalometric radiographs) or expose patients to more radiation (cone-beam computed tomography [CBCT]). This study aimed to compare angular changes in tooth long axes using DDMs vs using CBCTs.



Settings and sample population: Secondary data analysis of DDMs and CBCTs, taken before and after orthodontic treatment with piezocision of 24 patients.



Methods: Angular changes in tooth long axes were evaluated using landmarks on first molars (centre of the occlusal surface and centre of the furcation), canines and incisors (cusp tip and centre of the root at the cementoenamel junction). Wilcoxon test, intraclass correlation coefficient (ICC) and Bland-Altman plots were used to test intra- and inter-rater agreement and compare DDM and CBCT measurements.



Results: The mesiodistal angulation and buccolingual inclination DDM measurements were reproducible. Overall mean differences between DDM and CBCT measurements of mesiodistal angulation, 1.9°±1.5°, and buccolingual inclination, 2.2 ± 2.2°, were not significant for all teeth. ICC between DDM and CBCT measurements ranged from good (0.85 molars) to excellent (0.94 canines; 0.96 incisors). The percentages of measurements outside the range of ±5 were 17.4% for molars, 13.8% for canines and 4.5% for incisors.



Conclusions: DDM assessment of changes in tooth long axes has good reproducibility and yields comparable measurements to those obtained from CBCT within a 5° range. These findings lay the groundwork for machine learning approaches that synthesize crown and root canal information towards planning tooth movement without the need for ionizing radiation scans.},

keywords = {CBCT, Dental long axis, digital dental models},

pubstate = {published},

tppubtype = {article}

}