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.

Transverse Discrepancy in Orthodontics: A Review.

In today’s orthodontic practice esthetics is a primary concern both for patient and the orthodontist. The perception of esthetics for a lay person largely depends on the symmetry of the face. Hence, diagnosis and correction of the transverse discrepancy are imperative for optimum result. Some occlusal relations can result from skeletal jaw relation or from tooth positions. Malocclusion can occur in three planes of space, i.e., sagittal, transverse, and vertical plane. Transverse discrepancy is “An abnormality of development in transverse plane.” In orthodontic diagnosis and treatment planning, the emphasis is placed on recognizing asymmetry and achieving symmetric results with dental midlines coincident with each other and with the facial midline. Humans, like most other animals, are considered to display bilateral symmetry. By strict definition, this implies that mirror-image mathematical identity exists between right and left halves. In orthodontic diagnosis and treatment planning, emphasis is placed on recognizing asymmetry and achieving symmetric results. Treatment of an asymmetry can be challenging. The 1st treatment step is to diagnose if the asymmetry is of functional, dental or skeletal cause. The treatment options for transverse problem may include arch coordination, asymmetric extraction, asymmetric mechanics, and skeletal correction by orthopedic or surgical procedure. However, it is crucial to determine that the observed asymmetry is genuine and not the product of a functional or habitual shift of the mandible as is often the case with unilateral cross-bites due to reduced width of the maxillary arch.