Computerized identification of early ischemic changes in acute stroke in noncontrast CT using deep learning

Treatment for patients with acute ischemic stroke is most commonly determined based on findings on noncontrast computerized tomography (CT). Identifying hypoattenuation of the early ischemic changes on CT images is crucial for diagnosis. However, it is difficult to identify hypoattenuation with certainty. We present an atlas-based computerized method using a convolutional neural network (CNN) to identify hypoattenuation in the lentiform nucleus and the insula, two locations where hypoattenuation appears most frequently. The algorithm for this method consisted of anatomic standardization, setting of regions, creation of input images for classification, training on the CNN and classification of hypoattenuation. The regions of the lentiform nucleus and insula were set according to the Alberta Stroke Programme Early CT score (ASPECTS) method, a visual quantitative CT scoring system. AlexNet was used in the classification of the CNN architecture. We applied this method to the lentiform nucleus and insula using a database of 20 patients with right-sided hypoattenuation, 20 patients with left-sided hypoattenuation, and 20 normal subjects. Our method was evaluated using a leave-one-case-out cross-validation test. This new method had an average accuracy of 88.3%, an average sensitivity of 87.5%, and an average specificity of 90% for identifying hypoattenuation in the two regions. These results indicate that this new method has the potential to accurately identify hypoattenuation in the lentiform nucleus and the insula in patients with acute ischemic stroke.