ArticleInternational dental journal2025
Deep Learning for Predicting the Difficulty Level of Removing the Impacted Mandibular Third Molar.
Article in International dental journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.
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Who cites it
5 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Transformer-based models in dentistry: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Predicting alveolar nerve injury and the difficulty level of extraction impacted third molars: a systematic review of deep learning approaches.Frontiers in dental medicine · 2025Pooled it
- Assessment of AI software's diagnostic accuracy in identifying impacted teeth in panoramic radiographs.European journal of orthodontics · 2025Article
- Automated diagnosis for extraction difficulty of maxillary and mandibular third molars and post-extraction complications using deep learning.Scientific reports · 2025Article
- Development of an AI-Supported Clinical Tool for Assessing Mandibular Third Molar Tooth Extraction Difficulty Using Panoramic Radiographs and YOLO11 Sub-Models.Diagnostics (Basel, Switzerland) · 2025Article
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Authors and funding
6 authors.
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Abstract
backgroundPreoperative assessment of the impacted mandibular third molar (LM3) in a panoramic radiograph is important in surgical planning. The aim of this study was to develop and evaluate a computer-aided visualisation-based deep learning (DL) system using a panoramic radiograph to predict the difficulty level of surgical removal of an impacted LM3.
methodsThe study included 1367 LM3 images from 784 patients who presented from 2021-2023 to the University Dental Hospital; images were collected retrospectively. The difficulty level of surgically removing impacted LM3s was assessed via our newly developed DL system, which seamlessly integrated 3 distinct DL models. ResNet101V2 handled binary classification for identifying impacted LM3s in panoramic radiographs, RetinaNet detected the precise location of the impacted LM3, and Vision Transformer performed multiclass image classification to evaluate the difficulty levels of removing the detected impacted LM3.
resultsThe ResNet101V2 model achieved a classification accuracy of 0.8671. The RetinaNet model demonstrated exceptional detection performance, with a mean average precision of 0.9928. Additionally, the Vision Transformer model delivered an average accuracy of 0.7899 in predicting removal difficulty levels.
conclusionsThe development of a 3-phase computer-aided visualisation-based DL system has yielded a very good performance in using panoramic radiographs to predict the difficulty level of surgically removing an impacted LM3.
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