用于肝创伤分诊的决策支持系统
Decision Support System to triage of liver trauma
AI总结：
针对创伤患者肝脏出血和撕裂伤快速诊断的需求，提出基于GAN Pix2Pix模型的决策支持系统，在CT扫描检测中分别达到97%和93%的准确率，显著提升了诊断精度与速度。
摘要：
创伤对全球健康影响重大，每年导致超过500万人死亡，与结核病、艾滋病和疟疾等疾病的死亡率相当。在伊朗，交通事故造成的经济损失约占该国每年国民生产总值的2%。出血是创伤患者在受伤后24小时内死亡的主要原因，因此快速诊断和严重程度评估至关重要。创伤患者需要对所有器官进行全面扫描，这产生了大量数据。评估全身的CT图像非常耗时，且需要丰富的专业知识，凸显了诊断中高效时间管理的必要性。高效的诊断流程能显著降低治疗成本并减少继发并发症的可能性。在此背景下，开发一个可靠的创伤分诊决策支持系统（DSS），特别是针对腹部区域，显得尤为重要。本文提出了一种利用CT扫描检测肝脏出血和撕裂伤的新方法，该方法使用了GAN Pix2Pix转换模型。该方法的有效性通过Dice评分指标进行量化，模型在肝脏出血检测中达到了97%的准确率，在肝脏撕裂伤检测中达到了93%的准确率。这些结果相较于当前最先进技术有显著提升。该系统的设计与现有医学成像技术无缝集成，使其成为紧急医疗服务的实用补充。本研究强调了如GAN Pix2Pix等先进图像转换模型在提升重症监护场景下医疗诊断精度和速度方面的潜力。
英文摘要：
Trauma significantly impacts global health, accounting for over 5 million deaths annually, which is comparable to mortality rates from diseases such as tuberculosis, AIDS, and malaria. In Iran, the financial repercussions of road traffic accidents represent approximately 2% of the nation's Gross National Product each year. Bleeding is the leading cause of mortality in trauma patients within the first 24 hours following an injury, making rapid diagnosis and assessment of severity crucial. Trauma patients require comprehensive scans of all organs, generating a large volume of data. Evaluating CT images for the entire body is time-consuming and requires significant expertise, underscoring the need for efficient time management in diagnosis. Efficient diagnostic processes can significantly reduce treatment costs and decrease the likelihood of secondary complications. In this context, the development of a reliable Decision Support System (DSS) for trauma triage, particularly focused on the abdominal area, is vital. This paper presents a novel method for detecting liver bleeding and lacerations using CT scans, utilising the GAN Pix2Pix translation model. The effectiveness of the method is quantified by Dice score metrics, with the model achieving an accuracy of 97% for liver bleeding and 93% for liver laceration detection. These results represent a notable improvement over current state-of-the-art technologies. The system's design integrates seamlessly with existing medical imaging technologies, making it a practical addition to emergency medical services. This research underscores the potential of advanced image translation models like GAN Pix2Pix in improving the precision and speed of medical diagnostics in critical care scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
