Chiang Pi-Ling, Lin Shih-Yen, Chen Meng-Hsiang, Chen Yueh-Sheng, Wang Cheng-Kang, Wu Min-Chen, Huang Yii-Ting, Lee Meng-Yang, Chen Yong-Sheng, Lin Wei-Che
Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan.
Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan.
J Clin Med. 2022 Aug 31;11(17):5159. doi: 10.3390/jcm11175159.
(1) Background: The Alberta Stroke Program Early CT Score (ASPECTS) is a standardized scoring tool used to evaluate the severity of acute ischemic stroke (AIS) on non-contrast CT (NCCT). Our aim in this study was to automate ASPECTS. (2) Methods: We utilized a total of 258 patient images with suspected AIS symptoms. Expert ASPECTS readings on NCCT were used as ground truths. A deep learning-based automatic detection (DLAD) algorithm was developed for automated ASPECTS scoring based on 168 training patient images using a convolutional neural network (CNN) architecture. An additional 90 testing patient images were used to evaluate the performance of the DLAD algorithm, which was then compared with ASPECTS readings on NCCT as performed by physicians. (3) Results: The sensitivity, specificity, and accuracy of DLAD for the prediction of ASPECTS were 65%, 82%, and 80%, respectively. These results demonstrate that the DLAD algorithm was not inferior to radiologist-read ASPECTS on NCCT. With the assistance of DLAD, the individual sensitivity of the ER physician, neurologist, and radiologist improved. (4) Conclusion: The proposed DLAD algorithm exhibits a reasonable ability for ASPECTS scoring on NCCT images in patients presenting with AIS symptoms. The DLAD algorithm could be a valuable tool to improve and accelerate the decision-making process of front-line physicians.
(1) 背景：艾伯塔卒中项目早期CT评分（ASPECTS）是一种用于在非增强CT（NCCT）上评估急性缺血性卒中（AIS）严重程度的标准化评分工具。本研究的目的是实现ASPECTS评分自动化。(2) 方法：我们总共使用了258例有疑似AIS症状的患者图像。NCCT上的专家ASPECTS读数用作真值。基于168例训练患者图像，使用卷积神经网络（CNN）架构开发了一种基于深度学习的自动检测（DLAD）算法，用于ASPECTS自动评分。另外90例测试患者图像用于评估DLAD算法的性能，然后将其与医生在NCCT上进行的ASPECTS读数进行比较。(3) 结果：DLAD预测ASPECTS的敏感性、特异性和准确性分别为65%、82%和80%。这些结果表明，DLAD算法在NCCT上并不逊色于放射科医生读取的ASPECTS。在DLAD的辅助下，急诊医生、神经科医生和放射科医生的个体敏感性有所提高。(4) 结论：所提出的DLAD算法在对有AIS症状患者的NCCT图像进行ASPECTS评分方面表现出合理的能力。DLAD算法可能是改善和加速一线医生决策过程的有价值工具。
