Center for No-Boundary Thinking (CNBT) at Arkansas State University, The Joint Translational Research Lab of Arkansas State University, St. Bernards Medical Center, Jonesboro, AR, 72467, USA.
Department of Computer Science and Molecular Biosciences Program, Arkansas State University, Jonesboro, AR, 72467, USA.
Sci Rep. 2020 Dec 1;10(1):20900. doi: 10.1038/s41598-020-78060-4.
One of the challenges with urgent evaluation of patients with acute respiratory distress syndrome (ARDS) in the emergency room (ER) is distinguishing between cardiac vs infectious etiologies for their pulmonary findings. We conducted a retrospective study with the collected data of 171 ER patients. ER patient classification for cardiac and infection causes was evaluated with clinical data and chest X-ray image data. We show that a deep-learning model trained with an external image data set can be used to extract image features and improve the classification accuracy of a data set that does not contain enough image data to train a deep-learning model. An analysis of clinical feature importance was performed to identify the most important clinical features for ER patient classification. The current model is publicly available with an interface at the web link: http://nbttranslationalresearch.org/ .
在急诊科对急性呼吸窘迫综合征（ARDS）患者进行紧急评估时，其中一个挑战是区分其肺部发现的心脏病因和感染病因。我们对 171 例急诊科患者进行了回顾性研究。使用临床数据和胸部 X 射线图像数据评估了急诊科患者的心脏和感染原因分类。我们表明，可以使用经过外部图像数据集训练的深度学习模型来提取图像特征，并提高没有足够图像数据来训练深度学习模型的数据集的分类准确性。还进行了临床特征重要性分析，以确定急诊科患者分类的最重要临床特征。当前模型可通过网络链接公开获得：http://nbttranslationalresearch.org/ 。
