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 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.