Which Risk Factors Are Associated With Neurosensory Deficits of Inferior Alveolar Nerve After Mandibular Third Molar Extraction?

2012 ◽  
Vol 70 (11) ◽  
pp. 2508-2514 ◽  
Author(s):  
Jin-Woo Kim ◽  
In-Ho Cha ◽  
Sun-Jong Kim ◽  
Myung-Rae Kim
Keyword(s):  
Risk Factors ◽  
Inferior Alveolar Nerve ◽  
Third Molar ◽  
Third Molar Extraction ◽  
Mandibular Third Molar ◽  
Molar Extraction

Risk factors for lingual plate fracture during mandibular third molar extraction

2020 ◽  
Vol 24 (11) ◽  
pp. 4133-4142 ◽  
Author(s):  
Cheng Huang ◽  
Chun Zhou ◽  
Minhua Xu ◽  
Derong Zou
Keyword(s):  
Risk Factors ◽  
Third Molar ◽  
Third Molar Extraction ◽  
Plate Fracture ◽  
Mandibular Third Molar ◽  
Molar Extraction

Panoramic radiographic risk factors for inferior alveolar nerve injury after third molar extraction

2003 ◽  
Vol 61 (4) ◽  
pp. 417-421 ◽  
Author(s):  
Bart F. Blaeser ◽  
Meredith A. August ◽  
R.Bruce Donoff ◽  
Leonard B. Kaban ◽  
Thomas B. Dodson
Keyword(s):  
Risk Factors ◽  
Nerve Injury ◽  
Inferior Alveolar Nerve ◽  
Third Molar ◽  
Third Molar Extraction ◽  
Inferior Alveolar Nerve Injury ◽  
Molar Extraction

scholarly journals Deep Learning-Based Prediction of Paresthesia after Third Molar Extraction: A Preliminary Study

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1572
Author(s):  
Byung Su Kim ◽  
Han Gyeol Yeom ◽  
Jong Hyun Lee ◽  
Woo Sang Shin ◽  
Jong Pil Yun ◽  
...  
Keyword(s):  
Deep Learning ◽  
Inferior Alveolar Nerve ◽  
Third Molar ◽  
Area Under The Curve ◽  
Radiographic Images ◽  
Third Molar Extraction ◽  
Mandibular Third Molar ◽  
Group 2 ◽  
Sensitivity Specificity ◽  
Molar Extraction

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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