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

Autor: Byung Su Kim, Han Gyeol Yeom, Jong Hyun Lee, Woo Sang Shin, Jong Pil Yun, Seung Hyun Jeong, Jae Hyun Kang, See Woon Kim, Bong Chul Kim
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Diagnostics, Vol 11, Iss 9, p 1572 (2021)
Druh dokumentu: article
ISSN: 2075-4418
DOI: 10.3390/diagnostics11091572
Popis: 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.
Databáze: Directory of Open Access Journals
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