Given the increasing demand for assessment of adolescent growth and development, prediction of adolescent hand-bone age has become important in the field of medical image analysis. Recent convolutional neural networks(CNNs), especially the EfficientNetV2 model, have significantly improved the accuracy of bone-age prediction. In this study, we trained a CNN model based on EfficientNetV2 to predict bone age based on 10,000 X-ray images of adolescents hand bones. The model extracted deep features from X-ray images, and after training, predicted bone age with remarkable accuracy. Moreover, when tested on real dataset, the model reduced the mean absolute error (MAE) of predicted bone age to 0.752 years. Our study confirms that deep learning methods aid in medical image analysis. Our bone-age prediction model is both objective and quantitative, and will find applications in clinical practice and when inferring the developmental cycle of minors.

Bone age assessment is a vital indicator for evaluating the growth and development of children and adolescents. Traditional manual evaluation methods are inefficient and highly subjective, failing to meet modern clinical demands. Recent advancements in deep learning, especially in image recognition and medical image analysis, have provided new opportunities for automated and precise bone age assessment. This paper presents a comprehensive study on deep learning-based bone age prediction, covering data preprocessing, model architecture, training strategies, and evaluation methods. The proposed method, leveraging data augmentation, U-Net segmentation, and the EfficientNet V2 architecture, achieves high accuracy and robustness in bone age prediction, demonstrating significant potential for clinical applications and providing valuable insights for future research.

Bone age assessment (BAA) is a critical research topic in pediatric radiology, with growing interest in developing automated BAA methods. This study proposes a bone age prediction model integrating cluster analysis and convolutional neural network (CNN) regression, further enhanced by a multi-scale attention mechanism to construct a "divide-and-focus" dual-driven deep learning framework. Targeting age-sensitive regional features in hand radiographs, we innovatively design an adaptive spatial attention module that achieves hierarchical anatomical feature enhancement through saliency detection of attention-guided regions of interest (ROI). The algorithm first uses multiconstrained clustering of K methods to generate age-specific subsets, followed by parallel execution on each subset: 1) attention-guided ROI segmentation and feature enhancement; 2) validation of the base CNN regression networks (including ResNet, DenseNet and EfficientNetV2); 3) set of cross-subset models with Bayesian-optimized weighting strategies for final prediction. By synergistically integrating the data distribution priors with attention-driven anatomical priors, the method delivers interpretable solutions when performing medical image regression tasks. The modular design ensures compatibility with mainstream CNN architectures. The method will aid pediatric growth monitoring and the diagnosis of endocrine disorders.

Summarising from the Web could be formed as a problem of multi-document Summarisaiton (MDS) from multiple sources. In contrast to the current MDS problem that involves working on benchmark datasets which provide well clustered set of documents, we envisage to build a pipeline for content Summarisaiton from the Web, but narrow down to the Social Internet of Things (SIoT) paradigm, starting at data collection from the IoT objects, then applying natural language processing techniques for grouping and summarising the data, to distributing summaries back to the IoT objects. In this paper, we present our simulation tool, SimSumIoT, that simulates the process of data sharing, receiving, clustering, and Summarisaiton. A Web-based interface is developed for this purpose allowing users to visualize the process through a set of interactions. The Web interface is accessible via http://simsumlot.tk.