Abstract：
Intracranial hemorrhage (ICH) is a severe condition that requires rapid diagnosis and treatment. Automated methods for calculating ICH volumes can reduce human error and improve clinical decisioPlease provide professional degrees (e.g., PhD, MD) for the corresponding author.n-making. A novel automated method has been developed that is comparable to the ABC/2 method in terms of speed and accuracy while providing more accurate volumetric data.
We developed a novel automated algorithm for calculating intracranial blood volume from computed tomography (CT) scans. The algorithm consists of a Python script that processes Digital Imaging and Communications in Medicine images and determines the blood volume and ratio. The algorithm was validated against manual calculations performed by neurosurgeons.
Our novel automated algorithm for calculating intracranial blood volume from CT scans demonstrated excellent agreement with the ABC/2 method, with a median overall difference of just 1.46 mL. The algorithm was also validated in patient groups with ICH, epidural hematoma (EDH), and SDH, with agreement coefficients of 0.992, 0.983, and 0.997, respectively.
The study introduces a novel automated algorithm for calculating the volumes of various ICHs (EDH, and SDH) within CT scans. The algorithm showed excellent agreement with manual calculations and outperformed the commonly used ABC/2 method, which tends to overestimate ICH volume. The automated algorithm offers a more accurate, efficient, and time-saving approach to quantifying ICH, EDH, and SDH volumes, making it a valuable tool for clinical evaluation and decision-making.
We developed a novel automated algorithm for calculating intracranial blood volume from computed tomography (CT) scans. The algorithm consists of a Python script that processes Digital Imaging and Communications in Medicine images and determines the blood volume and ratio. The algorithm was validated against manual calculations performed by neurosurgeons.
Our novel automated algorithm for calculating intracranial blood volume from CT scans demonstrated excellent agreement with the ABC/2 method, with a median overall difference of just 1.46 mL. The algorithm was also validated in patient groups with ICH, epidural hematoma (EDH), and SDH, with agreement coefficients of 0.992, 0.983, and 0.997, respectively.
The study introduces a novel automated algorithm for calculating the volumes of various ICHs (EDH, and SDH) within CT scans. The algorithm showed excellent agreement with manual calculations and outperformed the commonly used ABC/2 method, which tends to overestimate ICH volume. The automated algorithm offers a more accurate, efficient, and time-saving approach to quantifying ICH, EDH, and SDH volumes, making it a valuable tool for clinical evaluation and decision-making.
摘要：
背景：颅内出血（ICH）是一种严重的疾病，需要快速诊断和治疗。计算ICH体积的自动化方法可以减少人为错误并改善临床决策。已开发出一种新颖的自动化方法，该方法在速度和准确性方面可与ABC/2方法相媲美，同时提供更准确的体积数据。
方法：我们开发了一种新的自动算法，用于根据CT扫描计算颅内血容量。该算法由Python脚本组成，该脚本处理DICOM图像并确定血液体积和比率。该算法已针对神经外科医生进行的手动计算进行了验证。
结果：我们用于从CT扫描中计算颅内血容量的新颖自动算法与ABC/2方法具有极好的一致性，中位数总体差异仅为1.46mL。该算法在ICH患者组中也得到了验证，EDH,和SDH,一致系数分别为0.992、0.983和0.997。
结论：该研究引入了一种新颖的自动算法，用于计算各种颅内出血的体积（ICH，EDH,和SDH）在CT扫描中。该算法与人工计算非常吻合，优于常用的ABC/2方法，这往往会高估ICH的体积。自动算法提供了更准确的，高效,以及量化ICH的节省时间的方法，EDH,和SDH卷，使其成为临床评估和决策的有价值的工具。
方法：我们开发了一种新的自动算法，用于根据CT扫描计算颅内血容量。该算法由Python脚本组成，该脚本处理DICOM图像并确定血液体积和比率。该算法已针对神经外科医生进行的手动计算进行了验证。
结果：我们用于从CT扫描中计算颅内血容量的新颖自动算法与ABC/2方法具有极好的一致性，中位数总体差异仅为1.46mL。该算法在ICH患者组中也得到了验证，EDH,和SDH,一致系数分别为0.992、0.983和0.997。
结论：该研究引入了一种新颖的自动算法，用于计算各种颅内出血的体积（ICH，EDH,和SDH）在CT扫描中。该算法与人工计算非常吻合，优于常用的ABC/2方法，这往往会高估ICH的体积。自动算法提供了更准确的，高效,以及量化ICH的节省时间的方法，EDH,和SDH卷，使其成为临床评估和决策的有价值的工具。
