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Clinical References

Evidence-based research papers, growth prediction studies, and clinical analysis guides.

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Research Papers and Studies

Peer-reviewed research papers and clinical studies supporting Bioprogressive methodology. Available for free download.

Evaluation of Ricketts' and Bolton's growth prediction algorithms embedded in two diagnostic imaging and cephalometric software

PDF Document 2015 Growth Prediction

This study evaluates the accuracy and reliability of Ricketts and Bolton growth prediction algorithms implemented in two popular diagnostic imaging and cephalometric software systems.

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Change in the vertical dimension of Class II Division 1 patients after use of cervical or high-pull headgear

PDF Document 2016 Headgear Therapy

Clinical study examining the vertical dimensional changes in Class II Division 1 patients treated with cervical headgear versus high-pull headgear appliances.

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Vertical characteristics of posterior teeth in untreated malocclusions

PDF Document 2018 Clinical Research

Study analyzing the vertical characteristics of posterior teeth in patients with various types of untreated malocclusions, providing baseline data for treatment planning.

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Assessment of vertical changes during maxillary expansion using quad helix or bonded rapid maxillary expander

PDF Document 2018 Expansion Therapy

Comparative analysis of vertical skeletal and dental changes resulting from treatment with quad helix appliances versus bonded rapid maxillary expanders.

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Dr Annie Analysis Sample

PDF Document AI Analysis Dr. ANNIE

Sample analysis documentation demonstrating the Dr. ANNIE AI-powered orthodontic diagnostic system for automated cephalometric landmark identification and analysis.

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Improving the accuracy of publicly available search engines in recognizing and classifying dental visual assets using convolutional neural networks

PDF Document 2020 Machine Learning

Research on applying convolutional neural networks to improve the accuracy of search engines in identifying and classifying dental visual assets including radiographs and photographs.

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Utilization of Machine Learning Methods for Predicting Orthodontic Treatment Length

PDF Document 2021 Treatment Planning

Study developing and validating machine learning models for predicting orthodontic treatment duration based on patient characteristics and treatment parameters.

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The validity of an artificial intelligence application for assessment of orthodontic treatment need from clinical images

PDF Document 2021 AI Assessment

Validation study of an AI-powered application for assessing orthodontic treatment need based on clinical photographs and intraoral images.

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Evidence-Based Literature

Peer-Reviewed Publications

Published research from 2015-2021 supporting Bioprogressive clinical methods.

Year Authors Title Journal
2015 Owes T, Kusnoto B, BeGole EA, Obrez A, Oppermann N, Sanchez F Skeletal growth changes in Class II subjects treated with slow palatal expansion World Federation of Orthodontists 4: 8-13
2016 Sagun M, Kusnoto B, Galang MT, Viana G, Evan CA Comparison of computerized cephalometric growth prediction: a study of three methods J World Fed Orthod 4: 146-150
2017 Dobbins-Zervas E, Kusnoto B, Galang MT, Viana G, Oppermann N, Sanchez F, Obrez A, Romero EG Change in the Vertical Dimension of Class II Division I Patients After Use of Cervical-or-High-Pull Headgear Am J Orthod Dentofac Orthop (Dec 2016)
2018 Piskai-Conroy C, Galang MT, Obrez A, Viana G, Oppermann N, Sanchez F, Edgren B, Kusnoto B Assessment of Vertical Changes During Palatal Expansion using Quad Helix or Bonded Rapid Palatal Expander Angle Orthod; DOI: 10.2319/112315-799
2019 Nation L, Sanchez F, Oppermann N, Galang-Boquiren M, Viana M, Kusnoto B Vertical characteristics of posterior teeth in untreated malocclusions World Federation of Orthodontists; DOI: 10.1016/j.ejwf.2019.03.001
2021 Bulatova G, Kusnoto B, Viana G, Tsay TP, Avenetti DM, Sanchez F Assessment of automatic cephalometric landmark identification using artificial intelligence Orthodontics and Craniofacial Research 24(S2): 37-42; DOI: 10.1111/ocr.12542
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