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At present, the images of key components of EMUs collected by TED are mainly recognized manually. In order to improve the efficiency of detection of bolts on apron boards, an improved YOLOv2 based method for detecting missing bolts on apron boards of running EMUs is proposed. Firstly, K-Means clustering is used to analyze the frame size of bolt area to be detected. Secondly, in the case that the target areas are similar and small, Spatial Pyramid Pooling ( SPP ) layer is added to the single-scale detection YOLOv2 model to improve the target perception ability. Finally, on the image sets of bolts on EMU apron boards, the experiment results before and after the improvement are compared. The results show that the mean average precision of the improved YOLOv2-SPP is increased from 94. 12% to 95. 83%. On a single 2080Ti graphics card, the detection speed reaches 73. 6FPS, which is 2.5 times the detection speed of YOLOv3 and 3.3 times that of YOLOv4 under the same environment. The algorithm is able to detect the bolts and bolt missing areas on apron boards of running EMUs, reducing the recognition pressure of the analyst. [ABSTRACT FROM AUTHOR]

Long‐term implantation of biomedical electronics into the human body enables advanced diagnostic and therapeutic functionalities. However, most long‐term resident electronics devices require invasive procedures for implantation as well as a specialized receiver for communication. Here, a gastric resident electronic (GRE) system that leverages the anatomical space offered by the gastric environment to enable residence of an orally delivered platform of such devices within the human body is presented. The GRE is capable of directly interfacing with portable consumer personal electronics through Bluetooth, a widely adopted wireless protocol. In contrast to the passive day‐long gastric residence achieved with prior ingestible electronics, advancement in multimaterial prototyping enables the GRE to reside in the hostile gastric environment for a maximum of 36 d and maintain ≈15 d of wireless electronics communications as evidenced by the studies in a porcine model. Indeed, the synergistic integration of reconfigurable gastric‐residence structure, drug release modules, and wireless electronics could ultimately enable the next‐generation remote diagnostic and automated therapeutic strategies. 3D‐printed orally delivered gastric resident electronic devices allow a needle and surgical‐free synergistic integration of biomedical electronics, the human body, and the digital domain. A maximum gastric residence of 36 and 15.3 d of wireless Bluetooth communication is demonstrated in a hostile gastric environment, enabling the next‐generation remote diagnostic and automated therapeutic strategies. [ABSTRACT FROM AUTHOR]

Objectives: Considering the escalating incidence of strabismus and its consequential jeopardy to binocular vision, there is an imperative demand for expeditious and precise screening methods. This study was to develop an artificial intelligence (AI) platform in the form of an applet that facilitates the screening and management of strabismus on any mobile device., Methods: The Visual Transformer (VIT_16_224) was developed using primary gaze photos from two datasets covering different ages. The AI model was evaluated by 5-fold cross-validation set and tested on an independent test set. The diagnostic performance of the AI model was assessed by calculating the Accuracy, Precision, Specificity, Sensitivity, F1-Score and Area Under the Curve (AUC)., Results: A total of 6194 photos with corneal light-reflection (with 2938 Exotropia, 1415 Esotropia, 739 Vertical Deviation and 1562 Orthotropy) were included. In the internal validation set, the AI model achieved an Accuracy of 0.980, Precision of 0.941, Specificity of 0.979, Sensitivity of 0.958, F1-Score of 0.951 and AUC of 0.994. In the independent test set, the AI model achieved an Accuracy of 0.967, Precision of 0.980, Specificity of 0.970, Sensitivity of 0.960, F1-Score of 0.975 and AUC of 0.993., Conclusions: Our study presents an advanced AI model for strabismus screening which integrates electronic archives for comprehensive patient histories. Additionally, it includes a patient-physician interaction module for streamlined communication. This innovative platform offers a complete solution for strabismus care, from screening to long-term follow-up, advancing ophthalmology through AI technology for improved patient outcomes and eye care quality., (© 2024. The Author(s), under exclusive licence to The Royal College of Ophthalmologists.)