Research status of artificial intelligence and bone age assessment based on deep learning / 中华实用儿科临床杂志

Bone age can objectively reflect the human body growth and accurately assess the physical development level.Bone age assessment plays an important role in the growth and development, disease diagnosis and the monitoring of therapeutic efficacy in children and adolescents.In recent years, the artificial intelligence technology has been developed continuously.Applying artificial intelligence technology is expected to realize the automatic assessment of bone age.At present, the artificial intelligence technology of bone age assessment is mainly based on the deep learning (DL) algorithm.Although there have been many research on DL and bone age assessment, most are still in the experimental stage.This study reviews the research and progress of artificial intelligence technology based on DL applied to bone age assessment, aiming to provide reference and research ideas for relevant staff.

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.

Assessment of age in foetus: A medicolegal aspect.

A Medicolegal case of embryo/foetus was received from the department of forensic medicine for the assessment of age of the foetus. The report for the assessment of age was prepared on the basis of gross appearance, radiological and histological examinations. On radiological examination of the limb tissue, the age of the foetus /embryo was approximately 3 months. The ossification centres for metatarsal bones were visible which appear at the 10th week of intrauterine life. Also on USG the femur length was found to be 10 mm which also suggests the age to be around 3 months. The smooth junction between the epidermis and dermis suggests age of the embryo/foetus to be less than 3 months because the epidermal ridges and dermal papillae become prominent at the end of 3rd month. From the basal layer of epidermis down growths were seen which were suggestive of growth of hair follicles. Hair follicles become prominent by the 10th week of intra uterine life which again reveals that the age of foetus is around end of 3rd month. From the observations, the age of the foetus was found to be more than 2 and half months but less than 3 months i.e. between 10-12 weeks.