Chongqing Key Laboratory of Vehicle Crash/Bio-impact and Traffic Safety, Institute of Surgery Research, Third Affiliated Hospital, Army Medical University, Chongqing, China.
First Affiliated Hospital, Army Medical University, Chongqing, China.
Sci Prog. 2020 Apr-Jun;103(2):36850420908750. doi: 10.1177/0036850420908750.
The fatality rate can be dramatically reduced with the help of emergency medical services. The purpose of this study was to establish a computational algorithm to predict the injury severity, so as to improve the timeliness, appropriateness, and efficacy of medical care provided. The computer simulations of full-frontal crashes with rigid wall were carried out using LS-DYNA and MADYMO under different collision speeds, airbag deployment time, and seatbelt wearing condition, in which a total of 84 times simulation was conducted. Then an artificial neural network is adopted to construct relevance between head and chest injuries and the injury risk factors; 37 accident cases with Event Data Recorder data and information on occupant injury were collected to validate the model accuracy through receiver operating characteristic analysis. The results showed that delta-v, seatbelt wearing condition, and airbag deployment time were important factors in the occupant's head and chest injuries. When delta-v increased, the occupant had significantly higher level of severe injury on the head and chest; there is a significant difference of Head Injury Criterion and Combined Thoracic Index whether the occupant wore seatbelt. When the airbag deployment time was less than 20 ms, the severity of head and chest injuries did not significantly vary with the increase of deployment time. However, when the deployment time exceeded 20 ms, the severity of head and chest injuries significantly increased with increase in deployment time. The validation result of the algorithm showed that area under the curve = 0.747, < 0.05, indicating a medium level of accuracy, nearly to previous model. The computer simulation and artificial neural network have a great potential for developing injury risk estimation algorithms suitable for Advanced Automatic Crash Notification applications, which could assist in medical decision-making and medical care.
在紧急医疗服务的帮助下，死亡率可以显著降低。本研究的目的是建立一种计算算法来预测损伤严重程度，以提高医疗服务的及时性、适当性和有效性。使用 LS-DYNA 和 MADYMO 在不同的碰撞速度、安全气囊展开时间和安全带佩戴条件下对正面碰撞进行了计算机模拟，总共进行了 84 次模拟。然后采用人工神经网络构建头部和胸部损伤与损伤风险因素之间的相关性；收集了 37 起带有事件数据记录器数据和乘员受伤信息的事故案例，通过接收者操作特征分析验证模型的准确性。结果表明，Delta-V、安全带佩戴条件和安全气囊展开时间是乘员头部和胸部受伤的重要因素。当 Delta-V 增加时，乘员头部和胸部的严重受伤程度显著增加；无论乘员是否佩戴安全带，头部损伤准则和胸部综合指数都有显著差异。当安全气囊展开时间小于 20ms 时，头部和胸部损伤的严重程度随展开时间的增加而没有显著变化。然而，当展开时间超过 20ms 时，头部和胸部损伤的严重程度随展开时间的增加而显著增加。算法的验证结果表明，曲线下面积=0.747，<0.05，表明准确性处于中等水平，接近之前的模型。计算机模拟和人工神经网络在开发适合高级自动碰撞通知应用的损伤风险估计算法方面具有很大的潜力，这可以帮助进行医疗决策和医疗护理。
