ArticleScientific reports2022
Evaluation of multi-task learning in deep learning-based positioning classification of mandibular third molars.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning for tooth detection and segmentation in panoramic radiographs: a systematic review and meta-analysis.BMC oral health · 2025Pooled it
- Validation of artificial intelligence-assisted CBCT analysis for predicting inferior alveolar nerve proximity to impacted mandibular third molars: a diagnostic accuracy study.BMC oral health · 2026Article
- Automated classification of maxillary and mandibular dental views on intraoral photographs: a comparative benchmark study and mobile proof-of-concept.BMC oral health · 2026Article
- Applications of artificial intelligence in tooth extraction: A systematic review.Journal of dental sciences · 2026Review
- Application of deep learning in evaluating the anatomical relationship between the mandibular third molar and inferior alveolar nerve: A scoping review.Medicina oral, patologia oral y cirugia bucal · 2026Article
- Automated diagnosis for extraction difficulty of maxillary and mandibular third molars and post-extraction complications using deep learning.Scientific reports · 2025Article
- Explainable deep learning for age and gender estimation in dental CBCT scans using attention mechanisms and multi task learning.Scientific reports · 2025Article
- Evaluation of the effectiveness of panoramic radiography in impacted mandibular third molars on deep learning models developed with findings obtained with cone beam computed tomography.Oral radiology · 2025Article
- Automatic mandibular third molar and mandibular canal relationship determination based on deep learning models for preoperative risk reduction.Clinical oral investigations · 2025Article
- Development of a diagnostic classification model for lateral cephalograms based on multitask learning.BMC oral health · 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
- Deep Learning for Predicting the Difficulty Level of Removing the Impacted Mandibular Third Molar.International dental journal · 2025Article
- Impacted lower third molar classification and difficulty index assessment: comparisons among dental students, general practitioners and deep learning model assistance.BMC oral health · 2025Article
- Decoding wisdom: Evaluating ChatGPT's accuracy and reproducibility in analyzing orthopantomographic images for third molar assessment.Computational and structural biotechnology journal · 2025Article
- Mapping the Use of Artificial Intelligence-Based Image Analysis for Clinical Decision-Making in Dentistry: A Scoping Review.Clinical and experimental dental research · 2024Article
- A two-stage deep-learning model for determination of the contact of mandibular third molars with the mandibular canal on panoramic radiographs.BMC oral health · 2024Article
- A dual-labeled dataset and fusion model for automatic teeth segmentation, numbering, and state assessment on panoramic radiographs.BMC oral health · 2024Article
- Impacted mandibular third molars: a comparison of orthopantomography and cone-beam computed tomography imaging in predicting surgical difficulty.Archives of craniofacial surgery · 2024Article
- A fully automated classification of third molar development stages using deep learning.Scientific reports · 2024Article
- Artificial Intelligence in Adult and Pediatric Dentistry: A Narrative Review.Bioengineering (Basel, Switzerland) · 2024Review
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11 authors.
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Abstract
Pell and Gregory, and Winter's classifications are frequently implemented to classify the mandibular third molars and are crucial for safe tooth extraction. This study aimed to evaluate the classification accuracy of convolutional neural network (CNN) deep learning models using cropped panoramic radiographs based on these classifications. We compared the diagnostic accuracy of single-task and multi-task learning after labeling 1330 images of mandibular third molars from digital radiographs taken at the Department of Oral and Maxillofacial Surgery at a general hospital (2014-2021). The mandibular third molar classifications were analyzed using a VGG 16 model of a CNN. We statistically evaluated performance metrics [accuracy, precision, recall, F1 score, and area under the curve (AUC)] for each prediction. We found that single-task learning was superior to multi-task learning (all p < 0.05) for all metrics, with large effect sizes and low p-values. Recall and F1 scores for position classification showed medium effect sizes in single and multi-task learning. To our knowledge, this is the first deep learning study to examine single-task and multi-task learning for the classification of mandibular third molars. Our results demonstrated the efficacy of implementing Pell and Gregory, and Winter's classifications for specific respective tasks.
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