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Human skeletal development is continuous and staged, and different stages have various morphological characteristics. Therefore, bone age assessment (BAA) can accurately reflect the individual’s growth and development level and maturity. Clinical BAA is time consuming, highly subjective, and lacks consistency. Deep learning has made considerable progress in BAA in recent years by effectively extracting deep features. Most studies use neural networks to extract global information from input images. However, clinical radiologists are highly concerned about the ossification degree in some specific regions of the hand bones. This paper proposes a two-stage convolutional transformer network to improve the accuracy of BAA. Combined with object detection and transformer, the first stage mimics the bone age reading process of the pediatrician, extracts the hand bone region of interest (ROI) in real time using YOLOv5, and proposes hand bone posture alignment. In addition, the previous information encoding of biological sex is integrated into the feature map to replace the position token in the transformer. The second stage extracts features within the ROI by window attention, interacts between different ROIs by shifting the window attention to extract hidden feature information, and penalizes the evaluation results using a hybrid loss function to ensure its stability and accuracy. The proposed method is evaluated on the data from the Pediatric Bone Age Challenge organized by the Radiological Society of North America (RSNA). The experimental results show that the proposed method achieves a mean absolute error (MAE) of 6.22 and 4.585 months on the validation and testing sets, respectively, and the cumulative accuracy within 6 and 12 months reach 71% and 96%, respectively, which is comparable to the state of the art, markedly reducing the clinical workload and realizing rapid, automatic, and high-precision assessment.

Bone age assessment plays a critical role in the investigation of endocrine, genetic, and growth disorders in children. This process is usually conducted manually, with some drawbacks, such as reliance on the pediatrician’s experience and extensive labor, as well as high variations among methods. Most deep learning models use one neural network to extract the global information from the whole input image, ignoring the local details that doctors care about. In this paper, we propose a global-local feature fusion convolutional neural network, including a global pathway to capture the global contextual information and a local pathway to extract the fine-grained information from local patches. The fine-grained information is integrated into the global context information layer-by-layer to assist in predicting bone age. We evaluated the proposed method on a dataset with 11,209 X-ray images with an age range of 4–18 years. Compared with other state-of-the-art methods, the proposed global-local network reduces the mean absolute error of the estimated ages to 0.427 years for males and 0.455 years for females; the average accuracy rate is within 6 months and 12 months, reaching 70% and 91%, respectively. In addition, the effectiveness and rationality of the model were verified on a public dataset.

BACKGROUND:Notch signaling is a highly conserved pathway in multi-cellular organisms ranging from flies to humans. It controls a variety of developmental processes by stimulating the expression of its target genes in a highly specific manner both spatially and temporally. The diversity, specificity and sensitivity of the Notch signaling output are regulated at distinct levels, particularly at the level of ligand-receptor interactions. METHODOLOGY/PRINCIPAL FINDINGS:Here, we report that the Drosophila gene uninflatable (uif), which encodes a large transmembrane protein with eighteen EGF-like repeats in its extracellular domain, can antagonize the canonical Notch signaling pathway. Overexpression of Uif or ectopic expression of a neomorphic form of Uif, Uif*, causes Notch signaling defects in both the wing and the sensory organ precursors. Further experiments suggest that ectopic expression of Uif* inhibits Notch signaling in cis and acts at a step that is dependent on the extracellular domain of Notch. Our results suggest that Uif can alter the accessibility of the Notch extracellular domain to its ligands during Notch activation. CONCLUSIONS/SIGNIFICANCE:Our study shows that Uif can modulate Notch activity, illustrating the importance of a delicate regulation of this signaling pathway for normal patterning.