Heo Yoonjung, Lee Go-Eun, Cho Jungchan, Choi Sang-Il
Division of Trauma Surgery, Department of Surgery, Dankook University College of Medicine, Cheonan-si 31116, Republic of Korea.
Department of Trauma Surgery, Trauma Center, Dankook University Hospital, Cheonan-si 31116, Republic of Korea.
Diagnostics (Basel). 2025 May 23;15(11):1312. doi: 10.3390/diagnostics15111312.
The accurate assessment of aortic diameter (AoD) is essential in managing patients with traumatic hemorrhage, particularly during interventions such as resuscitative endovascular balloon occlusion of the aorta (REBOA). Manual AoD measurements are time-consuming and subject to inter-observer variability. This study aimed to develop and validate a deep learning (DL) model for automated AoD measurement in trauma patients requiring massive transfusion. Abdominal CT scans from 300 adult patients were retrospectively analyzed. A Shallow Attention Network was trained on 444 manually annotated axial CT images to segment the aorta and measure its diameter. An ellipse-based calibration method was employed for enhanced measurement accuracy. The model achieved a mean Dice coefficient of 0.865 and an intersection over union of 0.9988. After calibration, the mean discrepancy between predicted and ground truth diameters was 2.11 mm. The median diaphragmatic AoD was 22.59 mm (interquartile range: 20.18-24.74 mm). The proposed DL model with ellipse-based calibration demonstrated robust performance in automated AoD measurement and may facilitate timely planning of aortic interventions in trauma care.
准确评估主动脉直径（AoD）对于创伤性出血患者的管理至关重要，尤其是在诸如主动脉复苏性血管内球囊阻断术（REBOA）等干预过程中。手动测量AoD耗时且存在观察者间差异。本研究旨在开发并验证一种深度学习（DL）模型，用于对需要大量输血的创伤患者进行自动AoD测量。对300例成年患者的腹部CT扫描进行回顾性分析。在444张手动标注的轴向CT图像上训练一个浅注意力网络，以分割主动脉并测量其直径。采用基于椭圆的校准方法提高测量准确性。该模型的平均Dice系数为0.865，交并比为0.9988。校准后，预测直径与真实直径之间的平均差异为2.11mm。膈下AoD的中位数为22.59mm（四分位间距：20.18 - 24.74mm）。所提出的基于椭圆校准的DL模型在自动AoD测量中表现出强大性能，可能有助于在创伤护理中及时规划主动脉干预措施。
