@article{Bandao2024,

title = {Evaluation of force systems generated by Memory Titanol springs with different preactivation bends},

author = {Henrique Barcelos Bandao and Jonas Bianchi and Lucas Arrais de Campos and Alyssa Schiavon Gandini and Luiz Gonzaga Gandini Junior},

url = {https://doi.org/10.1590/2177-6709.29.5.e242430.oar},

doi = {10.1590/2177-6709.29.5.e242430.oar },

year  = {2024},

date = {2024-10-04},

urldate = {2024-10-04},

journal = {Dental Press Journal of Orthodontics},

volume = {29},

number = {5},

abstract = {Objective: This study evaluated the force system generated by the Memory Titanol® spring (MTS) with different preactivation bends using an orthodontic force tester (OFT). Methods: Three preactivations were tested using a 0.017 × 0.022-in stainless steel (SS) wire and a 0.018 × 0.025-in NiTi segment, with an activation of 30º in the posterior segment (β), with 0º (Group 1 [G1]), 45º (Group 2 [G2]), and 60º (Group 3 [G3]) in the anterior segment (α). Results:   The molars showed extrusion values of −1.33 N for G1 and −0.78 N for G2, and an intrusion value of 0.33 N for G3. The force in the premolars was intrusive with a variation of 1.34 N for G1 and 0.77 N for G2; and extrusive with a variation of −0.31 N for G3. Regarding the upright moment (Ty) of the molar, a distal moment was observed with values of 53.45 N.mm for G1 and 19.87 N.mm for G2, while G3 presented a mesial moment of −6.23 N.mm. G1, G2, and G3 all exhibited distal premolar moments (Ty) of 3.58, 2.45, and 0.68 N.mm, respectively. Conclusions: The tested preactivations exerted an extrusive force in G1 and G2 and an intrusive force in G3 during molar vertical movement. The premolar region in G1 and G2 showed intrusive force and distal moment.},

keywords = {Biomechanical phenomena, Corrective, orthodontics, tooth movement technique},

pubstate = {published},

tppubtype = {article}

}

@article{deOliveira2024,

title = {Artificial intelligence as a prediction tool for orthognathic surgery assessment},

author = {Pedro Henrique José de Oliveira and Tengfei Li and Haoyue Li and João Roberto Gonçalves and Ary Santos-Pinto and Luiz Gonzaga Gandini Junior and Lucia Soares Cevidanes and Claudia Toyama and Guilherme Paladini Feltrin and Antonio Augusto Campanha and Melchiades Alves de Oliveira Junior and Jonas Bianchi},

url = {https://doi.org/10.1111/ocr.12805},

doi = {10.1111/ocr.12805},

issn = {1601-6335},

year  = {2024},

date = {2024-04-21},

journal = {Orthodontics & Craniofacial Research},

volume = {27},

issue = {5},

pages = {785-794},

abstract = {Introduction: An ideal orthodontic treatment involves qualitative and quantitative measurements of dental and skeletal components to evaluate patients' discrepancies, such as facial, occlusal, and functional characteristics. Deciding between orthodontics and orthognathic surgery remains challenging, especially in borderline patients. Advances in technology are aiding clinical decisions in orthodontics. The increasing availability of data and the era of big data enable the use of artificial intelligence to guide clinicians' diagnoses. This study aims to test the capacity of different machine learning (ML) models to predict whether orthognathic surgery or orthodontics treatment is required, using soft and hard tissue cephalometric values. Methods: A total of 920 lateral radiographs from patients previously treated with either conventional orthodontics or in combination with orthognathic surgery were used, comprising n = 558 Class II and n = 362 Class III patients, respectively. Thirty-two measures were obtained from each cephalogram at the initial appointment. The subjects were randomly divided into training (n = 552), validation (n = 183), and test (n = 185) datasets, both as an entire sample and divided into Class II and Class III sub-groups. The extracted data were evaluated using 10 machine learning models and by a four-expert panel consisting of orthodontists (n = 2) and surgeons (n = 2). Results: The combined prediction of 10 models showed top-ranked performance in the testing dataset for accuracy, F1-score, and AUC (entire sample: 0.707, 0.706, 0.791; Class II: 0.759, 0.758, 0.824; Class III: 0.822, 0.807, 0.89). Conclusions: The proposed combined 10 ML approach model accurately predicted the need for orthognathic surgery, showing better performance in Class III patients.},

keywords = {artificial intelligence, Class II, Class III, orthodontics, Orthognathic Surgery},

pubstate = {published},

tppubtype = {article}

}

@article{Bianchi2023f,

title = {Artificial intelligence applications in orthodontics. },

author = {Miranda F and Barone S and Gillot M and Baquero B and Anchling L and Hutlin B and et al},

url = {https://doi.org/10.1080/19424396.2023.2195585},

year  = {2023},

date = {2023-04-13},

urldate = {2023-04-13},

journal = {Journal of the California Dental Association },

volume = {51},

issue = {1},

abstract = {Objective

This manuscript describes strategies for assessment of precision of several diagnostic artificial intelligence (AI) tools in orthodontics, available open-source image analysis platforms, as well as the use of three-dimensional (3D) surface models and superimpositions.



Results

The advances described in this manuscript present perspectives on the controversies of whether AI is smarter than clinicians and may replace human clinical decisions. A thorough orthodontic diagnosis requires comprehensive 3D analysis of the interrelationships among the dentition, craniofacial skeleton and soft tissues. Forecasts have indicated that 3D printing technology will provide more than 60% of all dental treatment needs by 2025, and orthodontic companies as well as remote monitoring companies are already using AI technology, being it essential that the clinicians are prepared and knowledgeable with the technology advances now available.



Conclusions

The AI applications in orthodontics rely on the implementation into diagnostic image records, data analysis for clinical practice and research applications. Continuous training and validation of the AI orthodontic image tools are essential for improving the performance and generalizability of these methods.},

keywords = {artificial intelligence, imaging, orthodontics, three-dimensional},

pubstate = {published},

tppubtype = {article}

}

@article{Oh2022h,

title = {The Significance of Utilizing a Corticotomy on Periodontal and Orthodontic Outcomes: A Systematic Review and Meta-Analysis},

author = {Gao J and Nguyen T and Oberoi S and Oh H and Kapila S Kao RT and Lin GH},

url = {https://pubmed.ncbi.nlm.nih.gov/34440034/},

doi = {10.3390/biology10080803},

year  = {2021},

date = {2021-08-19},

urldate = {2022-08-19},

journal = {Biology (Basel)},

volume = {10},

issue = {8},

pages = {803},

abstract = {Purpose: This systematic review compares the clinical and radiographic outcomes for patients who received only a corticotomy or periodontal accelerated osteogenic orthodontics (PAOO) with those who received a conventional orthodontic treatment.



Methods: An electronic search of four databases and a hand search of peer-reviewed journals for relevant articles published in English between January 1980 and June 2021 were performed. Human clinical trials of ≥10 patients treated with a corticotomy or PAOO with radiographic and/or clinical outcomes were included. Meta-analyses were performed to analyze the weighted mean difference (WMD) and confidence interval (CI) for the recorded variables.



Results: Twelve articles were included in the quantitative analysis. The meta-analysis revealed a localized corticotomy distal to the canine can significantly increase canine distalization (WMD = 1.15 mm, 95% CI = 0.18-2.12 mm, p = 0.02) compared to a conventional orthodontic treatment. In addition, PAOO also showed a significant gain of buccal bone thickness (WMD = 0.43 mm, 95% CI = 0.09-0.78 mm, p = 0.01) and an improvement of bone density (WMD = 32.86, 95% CI = 11.83-53.89, p = 0.002) compared to the corticotomy group.



Conclusion: Based on the findings of the meta-analyses, the localized use of a corticotomy can significantly increase the amount of canine distalization during orthodontic treatment. Additionally, the use of a corticotomy as a part of a PAOO procedure significantly increases the rate of orthodontic tooth movement and it is accompanied by an increased buccal bone thickness and bone density compared to patients undergoing a conventional orthodontic treatment.},

keywords = {acceleration, bone remodeling, orthodontics, peridontics, tooth movement technique},

pubstate = {published},

tppubtype = {article}

}

@article{Baumrind2015,

title = {Overview of a powerful tool for orthodontic research and teaching},

author = {Sheldon Baumrind and Sean Curry},

url = {http://162.214.24.32/~crilorg/wp-content/uploads/2018/12/American-Association-of-Orthodontists-Foundation-Craniofacial-Growth-Legacy-Collection-Overview-of-a-powerful-tool-for-orthodontic-research-and-teaching-S.-Baumrind-S.-Curry.pdf},

year  = {2015},

date = {2015-01-01},

journal = {Am. J. Orthodontics},

volume = {148},

number = {2},

pages = {217-225},

abstract = {This article reports on the current status of the American Association of Orthodontists Foundation (AAOF)

Craniofacial Growth Legacy Collection—an AAOF-supported multi-institutional project that uses the Internet

and cloud computing to collect and share craniofacial images and data for orthodontic research and education.

The project gives investigators and clinicians all over the world online access to longitudinal information on

craniofacial development in untreated children with malocclusions of various types. It also is a unique source

of control samples for testing the validity of consensually accepted beliefs about the effects of orthodontic treatment

or of failure to treat.},

keywords = {orthodontics, research, teaching, tool},

pubstate = {published},

tppubtype = {article}

}