Abstract：
OBJECTIVE: Central venous catheterization (CVC) carries inherent risks which can be mitigated through the use of appropriate ultrasound-guidance during needle insertion. This study aims to comprehensively understand patient anatomy as it is visualized during CVC by employing a semi-automated image analysis method to track the internal jugular vein and carotid artery throughout recorded ultrasound videos.
METHODS: The ultrasound visualization of 50 CVC procedures were recorded at Penn State Health Milton S. Hershey Medical Center. The developed algorithm was used to detect the vessel edges, calculating metrics such as area, position, and eccentricity.
RESULTS: Results show typical anatomical variations of the vein and artery, with the artery being more circular and posterior to the vein in most cases. Notably, two cases revealed atypical artery positions, emphasizing the algorithm\'s precision in detecting anomalies. Additionally, dynamic vessel properties were analyzed, with the vein compressing on average to 13.4% of its original size and the artery expanding by 13.2%.
CONCLUSIONS: This study provides valuable insights which can be used to increase the accuracy of training simulations, thus enhancing medical education and procedural expertise. Furthermore, the novel approach of employing automated data analysis techniques to clinical recordings showcases the potential for continual assessment of patient anatomy, which could be useful in future advancements.
METHODS: The ultrasound visualization of 50 CVC procedures were recorded at Penn State Health Milton S. Hershey Medical Center. The developed algorithm was used to detect the vessel edges, calculating metrics such as area, position, and eccentricity.
RESULTS: Results show typical anatomical variations of the vein and artery, with the artery being more circular and posterior to the vein in most cases. Notably, two cases revealed atypical artery positions, emphasizing the algorithm\'s precision in detecting anomalies. Additionally, dynamic vessel properties were analyzed, with the vein compressing on average to 13.4% of its original size and the artery expanding by 13.2%.
CONCLUSIONS: This study provides valuable insights which can be used to increase the accuracy of training simulations, thus enhancing medical education and procedural expertise. Furthermore, the novel approach of employing automated data analysis techniques to clinical recordings showcases the potential for continual assessment of patient anatomy, which could be useful in future advancements.
摘要：
目的：中心静脉导管插入术（CVC）具有固有的风险，可以通过在插入针时使用适当的超声引导来减轻。这项研究旨在通过采用半自动图像分析方法在整个记录的超声视频中跟踪颈内静脉和颈动脉，全面了解CVC过程中可视化的患者解剖结构。
方法：在宾夕法尼亚州立健康MiltonS.Hershey医学中心记录了50例CVC程序的超声可视化。所开发的算法用于检测血管边缘，计算度量，如面积，position,和偏心。
结果：结果显示静脉和动脉的典型解剖变异，在大多数情况下，动脉更呈圆形，在静脉后方。值得注意的是,两例显示不典型动脉位置,强调算法在检测异常时的精度。此外,对动态血管特性进行了分析，静脉平均压缩至原始大小的13.4％，动脉扩张13.2％。
结论：这项研究提供了有价值的见解，可用于提高训练模拟的准确性，从而提高医学教育和程序专业知识。此外，采用自动数据分析技术的临床记录的新方法展示了持续评估患者解剖结构的潜力，这可能对未来的进步有用。
方法：在宾夕法尼亚州立健康MiltonS.Hershey医学中心记录了50例CVC程序的超声可视化。所开发的算法用于检测血管边缘，计算度量，如面积，position,和偏心。
结果：结果显示静脉和动脉的典型解剖变异，在大多数情况下，动脉更呈圆形，在静脉后方。值得注意的是,两例显示不典型动脉位置,强调算法在检测异常时的精度。此外,对动态血管特性进行了分析，静脉平均压缩至原始大小的13.4％，动脉扩张13.2％。
结论：这项研究提供了有价值的见解，可用于提高训练模拟的准确性，从而提高医学教育和程序专业知识。此外，采用自动数据分析技术的临床记录的新方法展示了持续评估患者解剖结构的潜力，这可能对未来的进步有用。
