| Autor: |
Agarwal, Sandhita, Agarwal, Sonahita |
| Předmět: |
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| Zdroj: |
Indian Journal of Dental Research; Oct-Dec2022, Vol. 33 Issue 4, p402-407, 6p |
| Abstrakt: |
Purpose: The assessment of bone age has applications in a wide variety of fields: from orthodontics to immigration. The traditional non-automated methods are time-consuming and subject to inter- and intra-observer variability. This is the first study of its kind done on the Indian population. In this study, we analyse different pre-processing techniques and architectures to determine the degree of maturation (i.e. cervical vertebral maturation [CVM]) from cephalometric radiographs using machine learning algorithms. Methods: Cephalometric radiographs--labelled with the correct CVM stage using Baccetti et al. method--from 383 individuals aged between 10 and 36 years were used in the study. Data expansion and in-place data augmentation were used to handle high data imbalances. Different pre-processing techniques like Sobel filters and canny edge detectors were employed. Several deep learning convolutional neural network (CNN) architectures along with numerous pre-trained models like ResNet-50 and VGG-19 were analysed for their efficacy on the dataset. Results: Models with 6 and 8 convolutional layers trained on 64 x 64--size grayscale images trained the fastest and achieved the highest accuracy of 94%. Pre-trained ResNet-50 with the first 49 layers frozen and VGG-19 with 10 layers frozen to training had remarkable performances on the dataset with accuracies of 91% and 89%, respectively. Conclusions: Custom deep CNN models with 6--8 layers on 64 x 64--sized greyscale images were successfully used to achieve high accuracies to classify the majority classes. This study is a launchpad in the development of an automated method for bone age assessment from lateral cephalograms for clinical purposes. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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