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. 2018 Aug 16;8(1):12281.
doi: 10.1038/s41598-018-29934-1.

Predicting postoperative facial swelling following impacted mandibular third molars extraction by using artificial neural networks evaluation

Wei Zhang  1   2 , Jun Li  3 , Zu-Bing Li  1   4 , Zhi Li  5   6
Affiliations

Predicting postoperative facial swelling following impacted mandibular third molars extraction by using artificial neural networks evaluation

Wei Zhang et al. Sci Rep. .

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.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
The architecture of the neural networks model.
Figure 2
Figure 2
(A–D) The different networks training epochs under four different judging value of extreme point (A: 0.1; B: 0.01; C: 0.001; D: 0.0001.).
Figure 3
Figure 3
The test performance of the training network in 5-fold cross-validation.
Figure 4
Figure 4
The confusion matrix of the dataset.
Figure 5
Figure 5
The error vs. epoch for the training, validation, and test performances of the training record.
Figure 6
Figure 6
The error histogram.
See this image and copyright information in PMC

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