ArticleDento maxillo facial radiology2024
Artificial intelligence-based automated preprocessing and classification of impacted maxillary canines in panoramic radiographs.
Article in Dento maxillo facial radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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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
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic, predictive, and therapeutic approaches for impacted canines: a systematic review and meta-analysis.BMC oral health · 2026Pooled it
- External root resorption prevalence on adjacent teeth associated with maxillary impacted canines in CBCT images.Journal of orofacial orthopedics = Fortschritte der Kieferorthopadie : Organ/official journal Deutsche Gesellschaft fur Kieferorthopadie · 2026Article
- Hybrid deep learning-based prognostic classification of maxillary canine impaction on panoramic radiographs.Oral radiology · 2026Article
- Reliability of a Deep Learning Model in Predicting Permanent Maxillary Canine Eruption for Preventive Orthodontics: A Comparative Study with Orthodontists.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence in the Radiological Diagnosis of Impacted Maxillary Canines: A Systematic Review.Journal of clinical medicine · 2026Review
- Artificial Intelligence-Assisted Detection of Canine Impaction, Localization, and Classification from Panoramic Images: A Diagnostic Accuracy Comparative Study with CBCT.Children (Basel, Switzerland) · 2026Article
- Comparative evaluation of ChatGPT, Gemini, and Grok in clinical decision-making and general knowledge assessment for impacted maxillary canines.Korean journal of orthodontics · 2026Article
- Fully automatic AI diagnosis of alveolar bone defect in anterior teeth on CBCT images.Clinical oral investigations · 2026Article
- Artificial intelligence performance in maxillary canine impaction: a systematic review.European journal of medical research · 2026Review
- Comparative Evaluation of Deep Learning Models for the Classification of Impacted Maxillary Canines on Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- Buccal or palatal? AI-based localization of impacted maxillary canines using panoramic radiographs.BMC medical imaging · 2026Article
- Educational support and weekly telemonitoring during rapid maxillary expansion in children: a prospective preference-based comparative cohort study.Frontiers in dental medicine · 2026Article
- Deep learning-based 3D automatic segmentation of impacted canines in CBCT scans.BMC oral health · 2025Article
- Deep learning-based prediction of buccal, middle and palatal orientations of impacted maxillary canines using panoramic radiographs: a pilot study.BMC oral health · 2025Article
- Applications of artificial intelligence in diagnosis and treatment planning of orthodontics: a narrative review.The Saudi dental journal · 2025Review
- Diagnostic accuracy of generative large language artificial intelligence models for the assessment of dental crowding.BMC oral health · 2025Article
- Assessment of AI software's diagnostic accuracy in identifying impacted teeth in panoramic radiographs.European journal of orthodontics · 2025Article
- Diagnostic accuracy of an artificial intelligence-based software in detecting supernumerary and congenitally missing teeth in panoramic radiographs.European journal of orthodontics · 2025Article
- An Overview of Cone-Beam Computed Tomography and Dental Panoramic Radiography in Dentistry in the Community.Tomography (Ann Arbor, Mich.) · 2024Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
objectivesAutomating the digital workflow for diagnosing impacted canines using panoramic radiographs (PRs) is challenging. This study explored feature extraction, automated cropping, and classification of impacted and nonimpacted canines as a first step.
methodsA convolutional neural network with SqueezeNet architecture was first trained to classify two groups of PRs (91with and 91without impacted canines) on the MATLAB programming platform. Based on results, the need to crop the PRs was realized. Next, artificial intelligence (AI) detectors were trained to identify specific landmarks (maxillary central incisors, lateral incisors, canines, bicuspids, nasal area, and the mandibular ramus) on the PRs. Landmarks were then explored to guide cropping of the PRs. Finally, improvements in classification of automatically cropped PRs were studied.
resultsWithout cropping, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve for classifying impacted and nonimpacted canine was 84%. Landmark training showed that detectors could correctly identify upper central incisors and the ramus in ∼98% of PRs. The combined use of the mandibular ramus and maxillary central incisors as guides for cropping yielded the best results (∼10% incorrect cropping). When automatically cropped PRs were used, the AUC-ROC improved to 96%.
conclusionsAI algorithms can be automated to preprocess PRs and improve the identification of impacted canines.
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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.