Graduate Institute of Library, Information and Archival Studies, National Chengchi University, Taipei, Taiwan.
Department of Radiology, Mackay Memorial Hospital, Taipei, Taiwan.
Comput Biol Med. 2022 Aug;147:105779. doi: 10.1016/j.compbiomed.2022.105779. Epub 2022 Jun 22.
Stroke is one of the leading causes of disability and mortality. Carotid atherosclerosis is a crucial factor in the occurrence of ischemic stroke. To achieve timely recognition, a computer-aided diagnosis (CAD) system was proposed to evaluate the ischemic stroke patterns in carotid color Doppler (CCD).
A total of 513 stroke and 458 normal CCD images were collected from 102 stroke and 75 normal patients, respectively. For each image, quantitative histogram, shape, and texture features were extracted to interpret the diagnostic information. In the experiment, a logistic regression classifier with backward elimination and leave-one-out cross validation was used to combine features as a prediction model.
The performance of the CAD system using histogram, shape, and texture features achieved accuracies of 87%, 60%, and 87%, respectively. With respect to the combined features, the CAD achieved an accuracy of 89%, a sensitivity of 89%, a specificity of 88%, a positive predictive value of 89%, a negative predictive value of 88%, and Kappa = 0.77, with an area under the receiver operating characteristic curve of 0.94.
Based on the extracted quantitative features in the CCD images, the proposed CAD system provides valuable suggestions for assisting physicians in improving ischemic stroke diagnoses during carotid ultrasound examination.
中风是导致残疾和死亡的主要原因之一。颈动脉粥样硬化是缺血性中风发生的关键因素。为了实现及时识别，提出了一种计算机辅助诊断（CAD）系统，以评估颈动脉彩色多普勒（CCD）中的缺血性中风模式。
从 102 例中风患者和 75 例正常患者中分别收集了 513 例中风和 458 例正常 CCD 图像。对于每张图像，提取了定量直方图、形状和纹理特征，以解释诊断信息。在实验中，使用具有向后消除和留一交叉验证的逻辑回归分类器将特征组合为预测模型。
使用直方图、形状和纹理特征的 CAD 系统的性能分别达到了 87%、60%和 87%的准确率。对于组合特征，CAD 达到了 89%的准确率、89%的敏感度、88%的特异性、89%的阳性预测值、88%的阴性预测值和 Kappa=0.77，以及 0.94 的接收器操作特征曲线下面积。
基于 CCD 图像中提取的定量特征，所提出的 CAD 系统为协助医生在颈动脉超声检查中提高缺血性中风诊断提供了有价值的建议。
