Author:
Kwon Dohyun,Ahn Jaemyung,Kim Chang-Soo,Kang Dong ohk,Paeng Jun-Young
Abstract
Abstract
Background
Assessing the time required for tooth extraction is the most important factor to consider before surgeries. The purpose of this study was to create a practical predictive model for assessing the time to extract the mandibular third molar tooth using deep learning. The accuracy of the model was evaluated by comparing the extraction time predicted by deep learning with the actual time required for extraction.
Methods
A total of 724 panoramic X-ray images and clinical data were used for artificial intelligence (AI) prediction of extraction time. Clinical data such as age, sex, maximum mouth opening, body weight, height, the time from the start of incision to the start of suture, and surgeon’s experience were recorded. Data augmentation and weight balancing were used to improve learning abilities of AI models. Extraction time predicted by the concatenated AI model was compared with the actual extraction time.
Results
The final combined model (CNN + MLP) model achieved an R value of 0.8315, an R-squared value of 0.6839, a p-value of less than 0.0001, and a mean absolute error (MAE) of 2.95 min with the test dataset.
Conclusions
Our proposed model for predicting time to extract the mandibular third molar tooth performs well with a high accuracy in clinical practice.
Funder
National Research Foundation of Korea
Publisher
Springer Science and Business Media LLC
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