@article{Bianchi2023l,

title = {Automated artificial intelligence‐based three‐dimensional comparison of orthodontic treatment outcomes with and without piezocision surgery.},

author = {Gurgel M and Alvarez M.A and Aristizabal J.F and Baquero B and Gillot M and Al Turkestani N and et al},

url = {https://pubmed.ncbi.nlm.nih.gov/38009409/},

doi = {10.1111/ocr.12737},

year  = {2023},

date = {2023-11-27},

journal = {Orthod Craniofac Res},

abstract = {Objective(s): This study aims to evaluate the influence of the piezocision surgery in the orthodontic biomechanics, as well as in the magnitude and direction of tooth movement in the mandibular arch using novel artificial intelligence (AI)-automated tools.



Materials and methods: Nineteen patients, who had piezocision performed in the lower arch at the beginning of treatment with the goal of accelerating tooth movement, were compared to 19 patients who did not receive piezocision. Cone beam computed tomography (CBCT) and intraoral scans (IOS) were acquired before and after orthodontic treatment. AI-automated dental tools were used to segment and locate landmarks in dental crowns from IOS and root canals from CBCT scans to quantify 3D tooth movement. Differences in mesial-distal, buccolingual, intrusion and extrusion linear movements, as well as tooth long axis angulation and rotation were compared.



Results: The treatment time for the control and experimental groups were 13.2 ± 5.06 and 13 ± 5.52 months respectively (P = .176). Overall, anterior and posterior tooth movement presented similar 3D linear and angular changes in the groups. The piezocision group demonstrated greater (P = .01) mesial long axis angulation of lower right first premolar (4.4 ± 6°) compared with control group (0.02 ± 4.9°), while the mesial rotation was significantly smaller (P = .008) in the experimental group (0.5 ± 7.8°) than in the control (8.5 ± 9.8°) considering the same tooth.



Conclusion: The open source-automated dental tools facilitated the clinicians' assessment of piezocision treatment outcomes. The piezocision surgery prior to the orthodontic treatment did not decrease the treatment time and did not influence in the orthodontic biomechanics, leading to similar tooth movements compared to conventional treatment.},

keywords = {computer-assisted, Cone-beam computed tomography (CBCT), Damon system, Dental long axis, Image processing, imaging, self-ligating braces, three-dimensional},

pubstate = {published},

tppubtype = {article}

}

@article{Bianchi2023,

title = {Automated Orientation and Registration of Cone-Beam Computed Tomography Scans.},

author = {Anchling L and Hutin N and Huang Y and Barone S and Roberts S and Miranda F and et al},

url = {https://doi.org/10.1007/978-3-031-45249-9_5},

doi = {10.1007/978-3-031-45249-9_5},

isbn = {978-3-031-45249-9},

year  = {2023},

date = {2023-10-09},

urldate = {2023-10-09},

journal = {Lecture Notes in Computer Science},

volume = {14242},

abstract = {Automated clinical decision support systems rely on accurate analysis of three-dimensional (3D) medical and dental images to assist clinicians in diagnosis, treatment planning, intervention, and assessment of growth and treatment effects. However, analyzing longitudinal 3D images requires standardized orientation and registration, which can be laborious and error-prone tasks dependent on structures of reference for registration. This paper proposes two novel tools to automatically perform the orientation and registration of 3D Cone-Beam Computed Tomography (CBCT) scans with high accuracy (<3 and <2 mm of angular and linear errors when compared to expert clinicians). These tools have undergone rigorous testing and are currently being evaluated by clinicians who utilize the 3D Slicer open-source platform. Our work aims to reduce the sources of error in the 3D medical image analysis workflow by automating these operations. These methods combine conventional image processing approaches and Artificial Intelligence (AI) based models trained and tested on de-identified CBCT volumetric images. Our results showed robust performance for standardized and reproducible image orientation and registration that provide a more complete understanding of individual patient facial growth and response to orthopedic treatment in less than 5 min.},

keywords = {3D CBCT scans, Deep Learning, Image processing, medical image registration, standardized orientation},

pubstate = {published},

tppubtype = {article}

}