ArticleKorean journal of orthodontics2022
Use of automated artificial intelligence to predict the need for orthodontic extractions.
Article in Korean journal of orthodontics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of artificial intelligence in orthodontic extraction treatment planning: a systematic review and meta analysis.BMC oral health · 2025Pooled it
- Clinician-accessible automated machine learning in oral healthcare: A systematic review.The Japanese dental science review · 2026Review
- Charting the artificial intelligence revolution in orthodontics : A bibliometric analysis of key players, collaborations, and research directions.Journal of orofacial orthopedics = Fortschritte der Kieferorthopadie : Organ/official journal Deutsche Gesellschaft fur Kieferorthopadie · 2026Review
- Prediction of Orthodontic Extraction Decisions Using Machine Learning Algorithms: A Retrospective Study.Journal of clinical and experimental dentistry · 2026Review
- Present and Future of the Use of Artificial Intelligence in Orthodontics.Bioengineering (Basel, Switzerland) · 2026Review
- A Novel Multimodal Deep Image Analysis Model for Predicting Extraction/Non-Extraction Decision.Orthodontics & craniofacial research · 2026Article
- Rigorous validation of machine learning models for orthodontic extraction prediction using nested cross-validation and smotenc-integrated pipelines.Journal of dental sciences · 2026Article
- Applications of artificial intelligence in diagnosis and treatment planning of orthodontics: a narrative review.The Saudi dental journal · 2025Review
- AI-Driven Risk Stratification of the Lingual Foramen: A CBCT-Based Prevalence and Morphological Analysis.Healthcare (Basel, Switzerland) · 2025Article
- Real-time segmentation and detection of ponticulus posticus in lateral cephalometric radiographs using YOLOv8: a step towards enhanced clinical evaluation.BMC oral health · 2025Article
- Applications of Artificial Intelligence in Dental Medicine: A Critical Review.International dental journal · 2025Review
- Automated Machine Learning in Dentistry: A Narrative Review of Applications, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2025Review
- Diagnostic accuracy of artificial intelligence in determining extraction protocol in orthodontic patients: A systematic review.Journal of orthodontic science · 2025Review
- Evaluation of different machine learning algorithms for extraction decision in orthodontic treatment.Orthodontics & craniofacial research · 2024Article
- Review
- Artificial Intelligence in Orthodontics: Critical Review.Journal of dental research · 2024Review
- Radiographic Imaging for the Diagnosis and Treatment of Patients with Skeletal Class III Malocclusion.Diagnostics (Basel, Switzerland) · 2024Review
- AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning-A Comprehensive Review.Journal of clinical medicine · 2024Review
- Evaluation of four machine learning methods in predicting orthodontic extraction decision from clinical examination data and analysis of feature contribution.Frontiers in bioengineering and biotechnology · 2024Article
- Application of Artificial Intelligence in Orthodontics: Current State and Future Perspectives.Healthcare (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To develop and explore the usefulness of an artificial intelligence system for the prediction of the need for dental extractions during orthodontic treatments based on gender, model variables, and cephalometric records. Methods: The gender, model variables, and radiographic records of 214 patients were obtained from an anonymized data bank containing 314 cases treated by two experienced orthodontists. The data were processed using an automated machine learning software (Auto-WEKA) and used to predict the need for extractions. Results: By generating and comparing several prediction models, an accuracy of 93.9% was achieved for determining whether extraction is required or not based on the model and radiographic data. When only model variables were used, an accuracy of 87.4% was attained, whereas a 72.7% accuracy was achieved if only cephalometric information was used. Conclusions: The use of an automated machine learning system allows the generation of orthodontic extraction prediction models. The accuracy of the optimal extraction prediction models increases with the combination of model and cephalometric data for the analytical process.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.