Motrix precision performance5/18/2023 When limited to dentistry, deep and convolutional neural networks have rapidly become the methodology of choice for two- and three-dimensional dental image analyses 13, 14, 15, 16. The clinical feasibility of the automated DL algorithm requires further confirmation using additional clinical datasets.ĭeep learning (DL), a subfield of artificial intelligence (AI), has a wide range of applications in medicine this unique technology is associated with high accuracy in medical image analysis for edge detection, classification, or segmentation based on a cascade of multiple computational and hidden layers in a deep neural network 12. Moreover, we observed no statistically significant difference in accuracy performance between the panoramic and periapical images. Within the study limitations, automated DL shows a reliable classification accuracy based on large-scale and comprehensive datasets. Using only panoramic images, the DL algorithm achieved 87.89% accuracy, 85.20% precision, 81.10% recall, and 83.10% F1 score, whereas the corresponding values using only periapical images achieved 86.87% accuracy, 84.40% precision, 81.70% recall, and 83.00% F1 score, respectively. The performance metrics of the automated DL based on accuracy, precision, recall, and F1 score for 116,756 panoramic and 40,209 periapical radiographic images were 88.53%, 85.70%, 82.30%, and 84.00%, respectively. ![]() The accuracy, precision, recall, F1 score, and confusion matrix were calculated to evaluate the classification performance of the automated DL algorithm. The dataset contained a total of 156,965 panoramic and periapical radiographic images and comprised 10 manufacturers and 27 different types of DIS. Dental implant radiographs of pos-implant surgery were collected from five college dental hospitals and 10 private dental clinics, and validated by the National Information Society Agency and the Korean Academy of Oral and Maxillofacial Implantology. ![]() This study aimed to evaluate the accuracy of automated deep learning (DL) algorithm for identifying and classifying various types of dental implant systems (DIS) using a large-scale multicenter dataset.
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