Radiologists and doctors make use of X-ray images of the non-dominant hands of children and infants to assess the possibility of genetic conditions and growth abnormalities. This is done by assessing the difference between the actual extent of growth found using the X-rays and the chronological age of the subject. The assessment was done conventionally using The Greulich Pyle (GP) or Tanner Whitehouse (TW) approach. These approaches require a high level of expertise and may often lead to observer bias. Hence, to automate the process of assessing the X-rays, and to increase its accuracy and efficiency, several machine learning models have been developed. These machine-learning models have several differences in their accuracy and efficiencies, leading to an unclear choice for the suitable model depending on their needs and available resources. Methods: In this study, we have analyzed the 3 most widely used models for the automation of bone age prediction, which are the Xception model, VGG model and CNN model. These models were trained on the preprocessed dataset and the accuracy was measured using the MAE in terms of months for each model. Using this, the comparison between the models was done. Results: The 3 models, Xception, VGG, and CNN models have been tested for accuracy and other relevant factors.

Bone age assessment is challenging in clinical practice due to the complicated bone age assessment process. Current automatic bone age assessment methods were designed with rare consideration of the diagnostic logistics and thus may yield certain uninterpretable hidden states and outputs. Consequently, doctors can find it hard to cooperate with such models harmoniously because it is difficult to check the correctness of the model predictions. In this work, we propose a new graph-based deep learning framework for bone age assessment with hand radiographs, called Doctor Imitator (DI). The architecture of DI is designed to learn the diagnostic logistics of doctors using the scoring methods (e.g., the Tanner-Whitehouse method) for bone age assessment. Specifically, the convolutions of DI capture the local features of the anatomical regions of interest (ROIs) on hand radiographs and predict the ROI scores by our proposed Anatomy-based Group Convolution, summing up for bone age prediction. Besides, we develop a novel Dual Graph-based Attention module to compute patient-specific attention for ROI features and context attention for ROI scores. As far as we know, DI is the first automatic bone age assessment framework following the scoring methods without fully supervised hand radiographs. Experiments on hand radiographs with only bone age supervision verify that DI can achieve excellent performance with sparse parameters and provide more interpretability.

See also the article by Pan et al in this issue. Safwan S. Halabi, MD, is a clinical associate professor of radiology at the Stanford University School of Medicine and serves as the medical director for radiology informatics at Stanford Children's Health. Dr Halabi's clinical and administrative leadership roles are directed at improving quality of care, efficiency, and patient safety. His current academic and research interests include imaging informatics, deep/machine learning in imaging, artificial intelligence in medicine, clinical decision support, and patient-centric health care delivery. Bone age assessment became an early AI "poster child" that demonstrated the power of applying regression and machine learning techniques to a mundane and monotonous radiologic diagnostic task.

Skeletal bone age assessment is a common clinical practice to diagnose endocrine and metabolic disorders in child development. In this paper, we describe a fully automated deep learning approach to the problem of bone age assessment using data from the 2017 Pediatric Bone Age Challenge organized by the Radiological Society of North America. The dataset for this competition is consisted of 12.6k radiological images. Each radiograph in this dataset is an image of a left hand labeled by the bone age and the sex of a patient. Our approach utilizes several deep neural network architectures trained end-to-end.

In a Journal of Digital Imaging paper published online in March 2017, researchers at Massachusetts General Hospital described a deep learning system for bone age assessment that addresses this limitation and provides a fully automated approach for clinical implementation. Here, AI infers the bone age from an X-ray image with no other patient information, trained by a convolutional neural network (CNN) using a data set including age and associated ideal X-rays. A CNN is a class of deep learning networks that mimics the neural connectivity patterns found in the animal visual cortex. It can provide confidence level and predicted age for any X-ray within seconds. The researchers tested the new deep learning system by applying it to more than 10,000 radiographs obtained at Mass General between 2005 and 2015.