Bone age assessment from lateral cephalograms using deep learning algorithms in the Indian population
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 thi...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2022-01-01
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| Series: | Indian Journal of Dental Research |
| Subjects: | |
| Online Access: | http://www.ijdr.in/article.asp?issn=0970-9290;year=2022;volume=33;issue=4;spage=402;epage=407;aulast=Agarwal |
| Summary: | 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 × 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 × 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. |
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| ISSN: | 0970-9290 1998-3603 |