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Objectives: Emergency treatment of patients with severe subphrenic vascular trauma often adopts resuscitative thoracotomy or endovascular balloon. This case report proposes a non-invasive method to treat patients with vascular trauma, mainly through ultrasound-guided positioning of the proximal aorta and applying pressure to occlude the aorta and limit the distal blood flow, using bedside ultrasound to achieve accurate compression, continuous monitoring of its efficacy, and early detection of the recovery of autonomic circulation in patients with cardiac arrest. Methods: We introduced a case of left iliac artery injury caused by a knife wound and subsequent cardiac arrest. Results We tried to externally compress the proximal aorta under bedside US guidance to achieve and maintain the recovery of the autonomic circulation. This allowed the patient to be transferred from the emergency department to the operating room. Conclusion: This case demonstrated that ultrasound-guided proximal external aortic compression can be used as a bridge for further treatment of patients with vascular trauma, such as resuscitative thoracotomy or endoaortic balloon or covered stent occlusion. [ABSTRACT FROM AUTHOR]

Objective: To summarize the current research progress of machine learning and venous thromboembolism. Methods: The literature on risk factors, diagnosis, prevention and prognosis of machine learning and venous thromboembolism in recent years was reviewed. Results: Machine learning is the future of biomedical research, personalized medicine, and computer-aided diagnosis, and will significantly promote the development of biomedical research and healthcare. However, many medical professionals are not familiar with it. In this review, we will introduce several commonly used machine learning algorithms in medicine, discuss the application of machine learning in venous thromboembolism, and reveal the challenges and opportunities of machine learning in medicine. Conclusion: The incidence of venous thromboembolism is high, the diagnostic measures are diverse, and it is necessary to classify and treat machine learning, and machine learning as a research tool, it is more necessary to strengthen the special research of venous thromboembolism and machine learning. [ABSTRACT FROM AUTHOR]