Automated rating of Fazekas scale in fluid-attenuated inversion recovery MRI for ischemic stroke or transient ischemic attack using machine learning

White matter hyperintensities (WMH) are commonly assessed using the Fazekas scale, a subjective visual grading system. Despite the emergence of deep learning models for automatic WMH grading, their application in stroke patients remains limited. This study aimed to develop and validate an automatic segmentation and grading model for WMH in stroke patients, utilizing spatial-probabilistic methods. We developed a two-step deep learning pipeline to predict Fazekas scale scores from T2-weighted FLAIR images. First, WMH segmentation was performed using a residual neural network based on the U-Net architecture. Then, Fazekas scale grading was carried out using a 3D convolutional neural network trained on the segmented WMH probability volumes. A total of 471 stroke patients from three different sources were included in the analysis. The performance metrics included area under the precision-recall curve (AUPRC), Dice similarity coefficient, and absolute error for WMH volume prediction. In addition, agreement analysis and quadratic weighted kappa were calculated to assess the accuracy of the Fazekas scale predictions. The WMH segmentation model achieved an AUPRC of 0.81 (95% CI, 0.55-0.95) and a Dice similarity coefficient of 0.73 (95% CI, 0.49-0.87) in the internal test set. The mean absolute error between the true and predicted WMH volumes was 3.1 ml (95% CI, 0.0 ml-15.9 ml), with no significant variation across Fazekas scale categories. The agreement analysis demonstrated strong concordance, with an R-squared value of 0.91, a concordance correlation coefficient of 0.96, and a systematic difference of 0.33 ml in the internal test set, and 0.94, 0.97, and 0.40 ml, respectively, in the external validation set. In predicting Fazekas scores, the 3D convolutional neural network achieved quadratic weighted kappa values of 0.951 for regression tasks and 0.956 for classification tasks in the internal test set, and 0.898 and 0.956, respectively, in the external validation set. The proposed deep learning pipeline demonstrated robust performance in automatic WMH segmentation and Fazekas scale grading from FLAIR images in stroke patients. This approach offers a reliable and efficient tool for evaluating WMH burden, which may assist in predicting future vascular events.