ArticleScientific reports2024
A retrospective longitudinal assessment of artificial intelligence-assisted radiographic prediction of lower third molar eruption.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 10 citations in OpenAlex.
- Hybrid deep learning-based prognostic classification of maxillary canine impaction on panoramic radiographs.Oral radiology · 2026Article
- Classification Performance of General-Purpose Multimodal AI Chatbots Versus Expert Radiologists in the Winter Classification of Impacted Mandibular Third Molars on CBCT-Derived Images.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence in Oral and Maxillofacial Surgery-A Narrative Review.Journal of clinical medicine · 2026Review
- Prediction of Lower Third Molar Eruption in Panoramic Radiography Using Artificial Intelligence (AI): PDApp.Diagnostics (Basel, Switzerland) · 2026Article
- Applications of artificial intelligence in tooth extraction: A systematic review.Journal of dental sciences · 2026Review
- Artificial Intelligence-Assisted Longitudinal Analysis Reveals a Novel Rare Fainting Wisdom Tooth Phenomenon in Fully Dentate Mandibles.International journal of dentistry · 2025Article
- Artificial Intelligence Application in a Case of Mandibular Third Molar Impaction: A Systematic Review of the Literature.Journal of clinical medicine · 2024Review
- Augmented Reality-Guided Extraction of Fully Impacted Lower Third Molars Based on Maxillofacial CBCT Scans.Bioengineering (Basel, Switzerland) · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prediction of lower third molar eruption is crucial for its timely extraction. Therefore, the primary aim of this study was to investigate the prediction of lower third molar eruption and its uprighting with the assistance of an artificial intelligence (AI) tool. The secondary aim was identifying the incidence of fully erupted lower third molars with hygienic cleansability. In total, 771 patients having two panoramic radiographs were recruited, where the first radiograph was acquired at 8-15 years of age (T1) and the second acquisition was between 16 and 23 years (T2). The predictive model for third molar eruption could not be obtained as few teeth reached full eruption. However, uprighting model at T2 showed that in cases with sufficient retromolar space, an initial angulation of < 32° predicted uprighting. Full eruption was observed for 13.9% of the teeth, and only 1.7% showed hygienic cleansability. The predictions model of third molar uprighting could act as a valuable aid for guiding a clinician with the decision-making process of extracting third molars which fail to erupt in an upright fashion. In addition, a low incidence of fully erupted molars with hygienic cleansability suggest that a clinician might opt for prophylactic extraction.
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Registered trials
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