Abstract
Patients’ postoperative facial swelling following third molars extraction may have both biological impacts and social impacts. The purpose of this study was to evaluate the accuracy of artificial neural networks in the prediction of the postoperative facial swelling following the impacted mandibular third molars extraction. The improved conjugate grads BP algorithm combining with adaptive BP algorithm and conjugate gradient BP algorithm together was used. In this neural networks model, the functional projective relationship was established among patient’s personal factors, anatomy factors of third molars and factors of surgical procedure to facial swelling following impacted mandibular third molars extraction. This neural networks model was trained and tested based on the data from 400 patients, in which 300 patients were made as the training samples, and another100 patients were assigned as the test samples. The improved conjugate grads BP algorithm was able to not only avoid the problem of local minimum effectively, but also improve the networks training speed greatly. 5-fold cross-validation was used to get a better sense of the predictive accuracy of the neural network and early stopping was used to improve generalization. The accuracy of this model was 98.00% for the prediction of facial swelling following impacted mandibular third molars extraction. This artificial intelligence model is approved as an accurate method for prediction of the facial swelling following impacted mandibular third molars extraction.
Highlights
The extraction of impacted mandibular third molars is one of the most common surgical events
When adaptive BP algorithm was applied, the learning curve would be apt to be trapped in infinitesimal, and there would be still no convergence until training epoch was over 100000
The network’s performance was measured according to the mean of squared errors, error vs. epoch for the training, validation, and test performances of the training record was shown in Fig. 5, the training stops after 28 consecutive increases in validation error, and the best performance is taken from the epoch with the lowest validation error
Summary
The extraction of impacted mandibular third molars is one of the most common surgical events. Patients’ postoperative facial swelling following third molars extraction may have both biological impacts and social impacts. The facial swelling is more likely occurring in those patients who received impacted mandibular third molars extraction with utilizing flap operation and bone removal. The incidence of facial swelling is related to the type of third molar, the degree of impaction, and ease of extraction operations[1,2,3,4,5,6,7,8]. Neural networks have the capacity to “learn” how to make a diagnosis through the information presented to them[19,20,21] In this present study, an AI model was established with improved conjugate grads backpropagation (BP) algorithm to predict facial swelling following impacted mandibular third molars extraction. The purpose of this study was to evaluate the accuracy of this improved neural networks model in predicting
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