ArticleDiagnostics (Basel, Switzerland)2021
Deep Learning-Based Prediction of Paresthesia after Third Molar Extraction: A Preliminary Study.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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24 citing papers in PubMed, 53 citations in OpenAlex.
- Bi-linguistic performance of large language models in multimodal analysis for differentiating jawbone-destroying malignancy from osteomyelitis.BMC oral health · 2026Article
- Impacted third molars and their influence on second molar pathologies: radiological patterns, statistical analysis, and emerging AI perspectives.Frontiers in dental medicine · 2026Article
- Applications of artificial intelligence in tooth extraction: A systematic review.Journal of dental sciences · 2026Review
- Evaluation of the effectiveness of panoramic radiography in maxillary 3rd molars on an artificial intelligence model developed with findings obtained with cone beam computed tomography.BMC oral health · 2025Article
- Integrating Machine Learning and Deep Learning for Predicting Non-Surgical Root Canal Treatment Outcomes Using Two-Dimensional Periapical Radiographs.Diagnostics (Basel, Switzerland) · 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
- Deep learning-based prediction of possibility for immediate implant placement using panoramic radiography.Scientific reports · 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
- Precision Oral Medicine: A DPR Segmentation and Transfer Learning Approach for Detecting Third Molar Compress Inferior Alveolar Nerve.IEEE journal of translational engineering in health and medicine · 2025Article
- Limitations of panoramic radiographs in predicting mandibular wisdom tooth extraction and the potential of deep learning models to overcome them.Scientific reports · 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
- Comparison of Faster R-CNN, YOLO, and SSD for Third Molar Angle Detection in Dental Panoramic X-rays.Sensors (Basel, Switzerland) · 2024Article
- Post-Traumatic Trigeminal Neuropathic Pain: A Narrative Review of Understanding, Management, and Prognosis.Biomedicines · 2024Review
- Deep learning-based prediction of indication for cracked tooth extraction using panoramic radiography.BMC oral health · 2024Article
- Artificial intelligence-based predictive model for guidance on treatment strategy selection in oral and maxillofacial surgery.Heliyon · 2024Review
- Application of Artificial Intelligence in Clinical Dentistry, a Comprehensive Review of Literature.Journal of dentistry (Shiraz, Iran) · 2023Review
- Deep learning model for the automated evaluation of contact between the lower third molar and inferior alveolar nerve on panoramic radiography.Journal of dental sciences · 2023Article
- Deep learning-based prediction of osseointegration for dental implant using plain radiography.BMC oral health · 2023Article
- Prediction of the as Low as Diagnostically Acceptable CT Dose for Identification of the Inferior Alveolar Canal Using 3D Convolutional Neural Networks with Multi-Balancing Strategies.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
The purpose of this study was to determine whether convolutional neural networks (CNNs) can predict paresthesia of the inferior alveolar nerve using panoramic radiographic images before extraction of the mandibular third molar. The dataset consisted of a total of 300 preoperative panoramic radiographic images of patients who had planned mandibular third molar extraction. A total of 100 images taken of patients who had paresthesia after tooth extraction were classified as Group 1, and 200 images taken of patients without paresthesia were classified as Group 2. The dataset was randomly divided into a training and validation set (n = 150 [50%]), and a test set (n = 150 [50%]). CNNs of SSD300 and ResNet-18 were used for deep learning. The average accuracy, sensitivity, specificity, and area under the curve were 0.827, 0.84, 0.82, and 0.917, respectively. This study revealed that CNNs can assist in the prediction of paresthesia of the inferior alveolar nerve after third molar extraction using panoramic radiographic images.
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