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Submitted
13 November 2024
Accepted
19 November 2024
Published
25 November 2024
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Abstract

This work presents three automated pre-trained models to predict the difficulty of extracting the mandibular third molar using a dataset of 2414 panoramic radiography images based on pre-processed (shifted and rotated) from left and right mandibular third molar instances. Methods: we employed four distinct architectural models, namely VGG-16, VGG-19, MobileNetV2, and ResNet50 to identify the difficulty of removing a mandibular third molar. We categorized the dataset into four categories of complexity to help in categorization (Normal, Easy, Medium, and difficult). Results: VGG-16, VGG-19, MobileNetV2 and ResNet50 had prediction accuracies of 81 %, 82 %, 79% and 44 %, respectively. Conclusions: the proposed deep learning model using VGG-19 could be a good tool to predict the difficulty of extracting a mandibular third molar using a panoramic radiographic image.

Keywords

Prediction accuracies Difficulty Level Surgical Removal mandibular Third Molar Deep Learning

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Copyright (c) 2024 Journal of Contemporary Dental Sciences

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Elborolosy, S., Salem , W. ., Hamed, M., Sayed , A., Helmy, B. E.- din, & Elngar, A. (2024). Predicting Difficulty Level of Surgical Removal of Impacted Mandibular Third Molar Using Deep Learning Approaches. Journal of Contemporary Dental Sciences, 1(1), 14–22. Retrieved from https://jcds.qu.edu.sa/index.php/JCDS/article/view/2348
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