ArticleDentistry journal2022
Machine Learning Predictive Model as Clinical Decision Support System in Orthodontic Treatment Planning.
Article in Dentistry journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI in orthodontic treatment planning: a systematic review comparing learning approaches.BMC oral health · 2026Pooled it
- Assessing the Diagnostic Performance of ChatGPT-5.0 versus Machine Learning in Orthodontics: A Comparative Analysis for Extraction Treatment Planning.Turkish journal of orthodontics · 2026Article
- A novel AI system for preliminary triage support in single-tooth edentulous spaces using intraoral images.Scientific reports · 2026Article
- Deep Learning-Based Evaluation of Maxillary Dental Midline Deviation on Orthodontic Frontal Photographs.Bioengineering (Basel, Switzerland) · 2026Article
- Development and External Validation of an Explainable AHP-ML Model for Orthodontic Tooth Extraction and Anchorage Decision Support.Bioengineering (Basel, Switzerland) · 2026Article
- Machine learning algorithms for predicting quality of life improvements after digital orthodontic treatment: a retrospective analysis.Biomedical engineering online · 2026Article
- Applications of artificial intelligence in diagnosis and treatment planning of orthodontics: a narrative review.The Saudi dental journal · 2025Review
- Machine learning based approach for surface roughness prediction in precision dental prototyping.Scientific reports · 2025Article
- Comparing diagnostic skills in endodontic cases: dental students versus ChatGPT-4o.BMC oral health · 2025Article
- Diagnostic accuracy of artificial intelligence in determining extraction protocol in orthodontic patients: A systematic review.Journal of orthodontic science · 2025Review
- Integrating Artificial Intelligence Into Orthodontic Education and Practice.International dental journal · 2024Article
- Embracing technological revolution: A panorama of machine learning in dentistry.Medicina oral, patologia oral y cirugia bucal · 2024Review
- Radiographic Imaging for the Diagnosis and Treatment of Patients with Skeletal Class III Malocclusion.Diagnostics (Basel, Switzerland) · 2024Review
- Research and application of artificial intelligence in dentistry from lower-middle income countries - a scoping review.BMC oral health · 2024Article
- Assessment of AI Models in Predicting Treatment Outcomes in Orthodontics.Journal of pharmacy & bioallied sciences · 2024Article
- 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
- Review
- Applied artificial intelligence in dentistry: emerging data modalities and modeling approaches.Frontiers in artificial intelligence · 2024Review
- Artificial Intelligence and Its Clinical Applications in Orthodontics: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diagnosis and treatment planning forms the crux of orthodontics, which orthodontists gain with years of expertise. Machine Learning (ML), having the ability to learn by pattern recognition, can gain this expertise in a very short duration, ensuring reduced error, inter-intra clinician variability and good accuracy. Thus, the aim of this study was to construct an ML predictive model to predict a broader outline of the orthodontic diagnosis and treatment plan. The sample consisted of 700 case records of orthodontically treated patients in the past ten years. The data were split into a training and a test set. There were 33 input variables and 11 output variables. Four ML predictive model layers with seven algorithms were created. The test set was used to check the efficacy of the ML-predicted treatment plan and compared with that of the decision made by the expert orthodontists. The model showed an overall average accuracy of 84%, with the Decision Tree, Random Forest and XGB classifier algorithms showing the highest accuracy ranging from 87-93%. Yet in their infancy stages, Machine Learning models could become a valuable Clinical Decision Support System in orthodontic diagnosis and treatment planning in the future.
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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.