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Готові списки джерел за темами / Pre-Orthodontic Trainer / Статті в журналах

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Статті в журналах з теми "Pre-Orthodontic Trainer"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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1

Aleksic, Ema, Maja Lalic, Jasmina Milic, Mihajlo Gajic, and Zdenka Stojanovic. "Dentofacial changes after treatment with prefabricated functional appliance T4CII." Serbian Dental Journal 60, no. 2 (2013): 93–98. http://dx.doi.org/10.2298/sgs1302093a.

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Анотація:

Introduction. Functional maxillary orthodontics has a large number of different mobile devices with different effects on craniomandibular system and great capabilities in solving many orthodontic problems. The aim of this article was to show the effects of 9-month treatment of malocclusion class II, division 1 in a 14-year-old female patient using pre-fabricated functional appliance Trainer T4CII. Case Outline. Skeletal distal relation, deep bite, increased overjet, narrowness and irregular position of upper and lower frontal teeth are indicated for orthodontic treatment with fixed appliance.

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2

Ramesh, R., Savitha N S, and Pavithra V Rao. "Case report on pre-orthodontic trainer in a 9-year-old child with 12 months follow up." International Journal of Pedodontic Rehabilitation 8, no. 1 (2023): 1–7. http://dx.doi.org/10.56501/intjpedorehab.v8i1.712.

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Анотація:

BACKGROUND: Pre-fabricated myofunctional appliances for early orthodontic therapy have been designed specifically for the care of children at the time of mixed dentition in growing adolescents with class II division 1 malocclusions. Sophisticated prefabricated removable functional appliances known as pre-orthodontic trainers have been shown to train the orofacial musculature, thus correcting malocclusions. CASE DESCRIPTION: This is a case report of 9-year-old boy diagnosed as Class II division 1 malocclusion with protruded upper anterior teeth, constricted upper arch, space loss in lower arch

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3

Satygo, E. A., A. V. Silin, and G. O. Ramirez-Yañez. "Electromyographic Muscular Activity Improvement in Class II Patients Treated with the Pre-Orthodontic Trainer." Journal of Clinical Pediatric Dentistry 38, no. 4 (2014): 380–84. http://dx.doi.org/10.17796/jcpd.38.4.2vh1603n62878673.

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Objective: A study was designed to determine changes in the amplitude of the EMG muscular activity of the Masseter and Temporalis muscles at clench in children with a Class II, division 1 malocclusion treated with the pre-orthodontic Trainer functional appliance, for 12 months. Study Design: 36 Class II, division 1 malocclusion patients (mean age 7.6 ± 1.3 years) composed the treated group and wore the functional appliance; 22 children with a similar age and malocclusion composed the untreated controls; and, 20 children with no dental malocclusion participated as normal controls. Electromyogra

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4

Bogdanov, Vladimir, Miroslava Dinkova, and Diana Tsakova. "Effect of pre-orthodontic trainer treatment on the masticatory and perioral muscles in patients with class II subdivision 1 malocclusion evaluated by surface EMG." Folia Medica 65, no. 5 (2023): 816–20. http://dx.doi.org/10.3897/folmed.65.e94741.

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Анотація:

Introduction: Myofunctional appliances have been shown to have a positive effect on the muscles in the facial area. Surface electromyography (sEMG) is one of the reliable methods used to investigate these effects. Aim: The aim of this follow-up study is to evaluate the effect of a myofunctional appliance T4K on the perioral and masticatory muscles in patients with class II subdivision 1 malocclusion by sEMG. Materials and methods: Sixteen children were treated with a Trainer appliance (T4K) for 9 months. The appliance should be worn every day for 1 hour and overnight. The EMG recordings were p

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5

Uysal, T., A. Yagci, S. Kara, and S. Okkesim. "Influence of Pre-Orthodontic Trainer treatment on the perioral and masticatory muscles in patients with Class II division 1 malocclusion." European Journal of Orthodontics 34, no. 1 (2011): 96–101. http://dx.doi.org/10.1093/ejo/cjq169.

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6

Khazal, Afrah, Nada Alhafidh, and Lamiaa Hassan. "Pre-Orthodontic Myofunctional Trainers Appliance System: A Review." Al-Rafidain Dental Journal 22, no. 1 (2022): 46–55. http://dx.doi.org/10.33899/rdenj.2022.129848.1096.

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7

Han, Miaomiao, Zhengqun Huo, Jiangyan Ren, et al. "Automated Landmark Detection and Lip Thickness Classification Using a Convolutional Neural Network in Lateral Cephalometric Radiographs." Diagnostics 15, no. 12 (2025): 1468. https://doi.org/10.3390/diagnostics15121468.

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Анотація:

Objective: The objective of this study is to develop a convolutional neural network (CNN) for the automatic detection of soft and hard tissue landmarks and the classification of lip thickness on lateral cephalometric radiographs. Methods: A dataset of 1019 pre-orthodontic lateral cephalograms from patients with diverse malocclusions was utilized. A CNN-based model was trained to automatically detect 22 cephalometric landmarks. Upper and lower lip thicknesses were measured using some of these landmarks, and a pre-trained decision tree model was employed to classify lip thickness into the thin,

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8

El Bsat, Abdul Rehman, Elie Shammas, Daniel Asmar, et al. "Semantic Segmentation of Maxillary Teeth and Palatal Rugae in Two-Dimensional Images." Diagnostics 12, no. 9 (2022): 2176. http://dx.doi.org/10.3390/diagnostics12092176.

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Анотація:

The superimposition of sequential radiographs of the head is commonly used to determine the amount and direction of orthodontic tooth movement. A harmless method includes the timely unlimited superimposition on the relatively stable palatal rugae, but the method is performed manually and, if automated, relies on the best fit of surfaces, not only rugal structures. In the first step, motion estimation requires segmenting and detecting the location of teeth and rugae at any time during the orthodontic intervention. Aim: to develop a process of tooth segmentation that eliminates all manual steps

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9

Mureșanu, Sorana, Mihaela Hedeșiu, Liviu Iacob, et al. "Automating Dental Condition Detection on Panoramic Radiographs: Challenges, Pitfalls, and Opportunities." Diagnostics 14, no. 20 (2024): 2336. http://dx.doi.org/10.3390/diagnostics14202336.

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Background/Objectives: The integration of AI into dentistry holds promise for improving diagnostic workflows, particularly in the detection of dental pathologies and pre-radiotherapy screening for head and neck cancer patients. This study aimed to develop and validate an AI model for detecting various dental conditions, with a focus on identifying teeth at risk prior to radiotherapy. Methods: A YOLOv8 model was trained on a dataset of 1628 annotated panoramic radiographs and externally validated on 180 radiographs from multiple centers. The model was designed to detect a variety of dental cond

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10

Rao, S. K., M. Bhat, and J. David. "Work, Stress, and Diurnal Bruxism: A Pilot Study among Information Technology Professionals in Bangalore City, India." International Journal of Dentistry 2011 (2011): 1–5. http://dx.doi.org/10.1155/2011/650489.

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Анотація:

The study assessed the prevalence of diurnal bruxism among information technology (IT) professionals and explored plausible predictors associated with the parafunctional habit. A cross-sectional study was designed and IT professionals were invited to participate. The inclusion criteria composed of participants in service for at least one year, having natural dentition, no history of cervical or facial injury and not undergoing orthodontic therapy. The participants () were interviewed by a trained interviewer to record information. A pre-tested questionnaire that included questions related to w

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11

Kats, Lazar, Marilena Vered, Johnny Kharouba, and Sigalit Blumer. "Transfer Deep Learning for Dental and Maxillofacial Imaging Modality Classification: A Preliminary Study." Journal of Clinical Pediatric Dentistry 45, no. 4 (2021): 233–38. http://dx.doi.org/10.17796/1053-4625-45.4.3.

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Objective: To apply the technique of transfer deep learning on a small data set for automatic classification of X-ray modalities in dentistry. Study design: For solving the problem of classification, the convolution neural networks based on VGG16, NASNetLarge and Xception architectures were used, which received pre-training on ImageNet subset. In this research, we used an in-house dataset created within the School of Dental Medicine, Tel Aviv University. The training dataset contained anonymized 496 digital Panoramic and Cephalometric X-ray images for orthodontic examinations from CS 8100 Digi

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12

Stetzel, Leah, Florence Foucher, Seung Jin Jang, et al. "Artificial Intelligence for Predicting the Aesthetic Component of the Index of Orthodontic Treatment Need." Bioengineering 11, no. 9 (2024): 861. http://dx.doi.org/10.3390/bioengineering11090861.

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The aesthetic component (AC) of the Index of Orthodontic Treatment Need (IOTN) is internationally recognized as a reliable and valid method for assessing aesthetic treatment need. The objective of this study is to use artificial intelligence (AI) to automate the AC assessment. A total of 1009 pre-treatment frontal intraoral photos with overjet values were collected. Each photo was graded by an experienced calibration clinician. The AI was trained using the intraoral images, overjet, and two other approaches. For Scheme 1, the training data were AC 1–10. For Scheme 2, the training data were eit

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13

Ferreira, FG. "Novel Approaches for Class II Malocclusion Treatment using Myofunctional Orthodontics Therapy: A Systematic Review." July 20, 2017. https://doi.org/10.19070/2377-8075-1700099.

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Анотація:

The aim of this review was to undertake a detailed analysis of the literature concerning the use of a prefabricated functional appliance for Class II malocclusion treatment. From June 2016 to May 2017, a comprehensive and systematic search was performed on the Medline (PubMed) and Google Scholar databases for works published since January 2002, using the following search terms: pre-orthodontic trainer; pre-orthodontic trainer appliance; class II malocclusion; class II malocclusion treatment; prefabricated functional appliance. A supplementary manual searching of other sources was also performe

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14

Preston, Kathryn, Lucia Chen, Tyler Brennan, and Barbara Sheller. "Orthodontic treatment protocols in patients with alveolar clefting: a survey of ACPA–approved cleft teams in the United States." Angle Orthodontist, October 13, 2022. http://dx.doi.org/10.2319/051522-357.1.

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Анотація:

ABSTRACT Objectives To describe pre– and post–alveolar bone graft (ABG) practice protocols of orthodontists associated with American Cleft Palate-Craniofacial Association–approved cleft and cleft/craniofacial teams. Materials and Methods Electronic survey responses from team orthodontists were evaluated regarding pre-ABG orthodontic treatment type(s), timing of post-ABG imaging and post-ABG orthodontic treatment, and craniofacial orthodontic fellowship training status of the team orthodontists. A P value of &lt;.05 was considered significant. Results Of 31 responding orthodontists, 54.8% h

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15

Sabzevari, Berahman, Arezousadat Fatemi, Milad Soleimani, Seyed Masoud Sajedi, and Roksana Babazadehkhoushrodi. "Masticatory performance and oral health related to quality of life before and after orthodontic treatment: a systematic review and meta-analysis." European Journal of Translational Myology, February 15, 2024. http://dx.doi.org/10.4081/ejtm.2024.12101.

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Анотація:

Masticatory performance is considered an important step in the beginning of the digestive process and considering the patient’s aesthetic and functional factors, it is necessary to choose appropriate treatments. Based on the evidence during orthodontic treatment, the restoration of the physiological function of masticatory performance should not be neglected. The present study aimed to evaluate the association between orthodontic treatment and improved masticatory performance and oral health related to quality of life. In the present study, all articles published till the end of August 2023 we

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16

Prinsloo, Rolanda. "Orthopaeddontic Cleft Treatment: Advantages and Disadvantages." Journal of Dental Health and Oral Research, June 10, 2025, 1–7. https://doi.org/10.46889/jdhor.2025.6209.

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Анотація:

Early orthodontic intervention in paediatric patient with a cleft anomaly was first documented in 1956, focussing on dental and dentoalveolar-palatal transverse alignment. Since then, various institutions have adopted varying approaches to orthodontic treatment protocols. Orthopaeddontic treatment represents a progressive and structured treatment approach in paediatrics, specifically designed for young children born with oro-facial cleft anomalies, such as cleft lip-alveolus, cleft lip-alveolus-palate and an isolated hard and soft palate clefts, which includes the Pierre Robin sequence. This a

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17

Ramasubbu, Niranjana, Shakeel Ahmed Valai Kasim, Rooban Thavarajah, and Krishnaswamy Nathamuni Rengarajan. "Applying artificial intelligence to predict the outcome of orthodontic treatment." APOS Trends in Orthodontics, April 8, 2024, 1–9. http://dx.doi.org/10.25259/apos_270_2023.

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Анотація:

Objectives: The study aimed to train an algorithm to predict facial and dental outcomes following orthodontic treatment using artificial intelligence (AI). In addition, the accuracy of the algorithm was evaluated by four distinct groups of evaluators. Material and Methods: The algorithm was trained using pre-treatment and post-treatment frontal smiling and intraoral photographs of 50 bimaxillary patients who underwent all first bicuspid extraction and orthodontic treatment with fixed appliances. A questionnaire was created through Google form and it included 10 actual post-treatment and AI-pre

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18

Baxmann, Martin, Krisztina Kárpáti, and Zoltán Baráth. "A systematic review of the content and delivery of clinical knowledge in orthodontic postgraduate programs." BMC Medical Education 25, no. 1 (2025). https://doi.org/10.1186/s12909-025-07361-x.

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Abstract Orthodontic postgraduate education plays a critical role in developing clinical competence, yet substantial variability exists in how programs structure, deliver, and assess clinical knowledge. This systematic review aimed to evaluate the content and delivery of clinical knowledge in orthodontic postgraduate programs and their reported impact on trainee competence. The review followed PRISMA 2020 guidelines and a pre-specified protocol. A comprehensive search was conducted across PubMed, Cochrane Library, ERIC, and CINAHL for studies published between January 2000 and December 2024. G

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19

S., Jishnu, Binnoy Kurian, and Tony Michael. "Automated Cephalometric Landmark Detection: A Novel Software Model Compared with Manual Annotation Method." Journal of Indian Orthodontic Society, February 3, 2024. http://dx.doi.org/10.1177/03015742231219540.

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Анотація:

Introduction: AI-based automated cephalometric landmark detection streamlines orthodontic diagnosis and treatment planning, providing accurate, efficient, and reliable results. Benefits include saving time, minimizing subjectivity, improving precision, and facilitating continuous improvement. However, they should complement clinician expertise, ensuring qualified orthodontists make the final diagnosis and treatment plan. Aim: To propose a method that automatically detects cephalometric landmarks on the X-ray images and compare these values with the manual annotation method. Methodology: A data

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20

Deli, Roberto, Bruno Oliva, Ludovica Antonella Macrì, et al. "The impact of social context on the perception of dental appearance in 8-9 years old children." Italian Journal of Public Health 6, no. 2 (2024). http://dx.doi.org/10.2427/5792.

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Анотація:

Background: Several studies have tried to clarify the role that malocclusion has on an individual’s selfconcept or on level of satisfaction with one’s dental or facial appearance, but little research on psycho-social attitudes towards malocclusion has been conducted on pre-adolescent children. The aim of our study was to investigate the impact of socio-economic status (SES) on the perception of dental appearance in third-grade school-children. Methods: A pilot survey was conducted in 2008 in order to collect data from a representative sample of 101 eight-nine years-old children (62 M - 39 F),

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