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［Objective］ In the ZDJ9 double-machine traction speed-up turnout electric switch control circuit, 1DQJ is an instantaneous suction relay. When the outdoor switch circuit does not form a loop, that is, in the event of a fault, the 1DQJ relay only maintains a short-term suction state for about 1s. When dealing with the fault point, technicians usually use a multimeter to measure. A digital multimeter can only flash once for a short period of time within 1 second, and the maximum displayed data is about 170 V; the pointer of a pointer multimeter can only swing slightly in 1 second, and it only swings to about 120 V, making it impossible to conduct a detailed search for the fault point. On the other hand, the control circuit adopts a 380 V three-phase AC power supply, which may ignite during the troubleshooting process. Due to the fear of technicians, the troubleshooting is slow, time-consuming, labor-intensive, and safety is greatly compromised. Innovative research is conducted on the common problems currently existing, and targeted fault diagnosis devices and methods are proposed. ［Method］ Three 200W light bulbs are combined to form a bulb diagnostic device, sharing one grounding electrode, and the other polarity is connected to the A, B, and C three-phase power supply. The bulbs are connected by the instantaneous suction of the 1DQJ relay. If the light is on, it means that there is no fault in the circuit. If the light is off, it means that there is a fault. The method of finding faults using the A, B, and C three-phase power supply is explained in detail. ［Result & Conclusion］ The proposed fault point diagnosis device and method can accurately and quickly diagnose the fault point of the control circuit and greatly improve the safety in fault diagnosis.

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Background: Echocardiography can conveniently, rapidly, and economically evaluate the structure and function of the heart, and has important value in the diagnosis and evaluation of cardiovascular diseases (CVDs). However, echocardiography still exhibits significant variability in image acquisition and diagnosis, with a heavy dependency on the operator's experience. Image quality affects disease diagnosis in the later stage, and even image quality assessment still has variability in human evaluation. This study aimed to develop an automated and real-time quality assessment system using deep learning (DL) techniques while decreasing the measurement error of left ventricular ejection fraction (LVEF)., Methods: This study involved over 5,000 echocardiography datasets from 2,461 participants across 10 medical centers in China to build the model. A 5-point quality scoring system was used to assess the integrity, clarity, and alignment of anatomical structures in each echocardiogram view. Additionally, an innovative DL model was developed to autonomously detect these essential cardiac anatomical structures in real-time, subsequently providing quality score estimations and LVEF. A total of 175 participants from two distinct external medical centers were enrolled for model validation. This dataset was employed to assess the consistency and repeatability of quality score and ejection fraction (EF) measurements, and the assessments made by human experts were compared with those of our model., Results: The developed model demonstrated exceptional performance, achieving Intersection over Union (IoU) scores exceeding 0.8 for left ventricular (LV) segmentation, a mean average precision when IoU >0.5 (mAP50) of 0.91 for cardiac anatomical structures detection, and a 0.96±0.05 accuracy in view classification. The quality scores assessed by the model closely matched those of human experts, indicating strong agreement. The weighted average precision and weighted average recall scores fell within the range of 0.5 to 0.6. Notably, there was no statistically significant difference in LVEF assessments between human experts and our model (P=0.09), as demonstrated by an intraclass correlation coefficient (ICC) analysis of 0.821, reflecting high-level consistency. When assessing echocardiograms with high-quality scores, the model demonstrated a significantly closer alignment and a higher correlation coefficient with human experts (R=0.90±0.04)., Conclusions: This study demonstrates that artificial intelligence-assisted echocardiography scoring system aligns well with manual quality scoring. Through the supervision of real-time echocardiogram quality, the artificial intelligence model can assist doctors in providing more reproducible and consistent assessments of cardiac function., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-512/coif). Y.J. is from Shenzhen Yingzhichuangsi Technology Co., Ltd. C.C. is from Shenzhen MicroPort Xinsuanzi Medical Technology Co., Ltd. B.S. reports that this work was supported by the Commission of Science and Technology of Shenzhen (No. GJHZ20210705142204014). H.F. reports that this research was supported by the National Natural Science Foundation of China (No. 82371963), the Guangdong Basic and Applied Basic Research Foundation (No. 2023A1515011366), and the NSFC launching fund of the Guangdong Provincial People’s Hospital (Nos. 8207070477 and 8227070211). The other authors have no conflicts of interest to declare., (2025 AME Publishing Company. All rights reserved.)