Introduction: Head Computed Tomography (CT) with perfusion imaging have become crucial in the selection of patients for treatment for mechanical thrombectomy. In recent years machine learning has rapidly evolved and found applications in a wide variety of health care tasks. We report our initial experiences with training a simple neural network to predict the presence and sidedness of a perfusion deficit in patients with acute ischemic stroke.
Methods: CT perfusion imaging of patients with suspicion for acute ischemic stroke were obtained. The data was split into training and validation sets. A long term, recurrent convolutional (LRCN) network was constructed consisting of a convolutional neural network stacked on top of a long short term (LSTM) layer.
Results: 139 (35.1%) patients had a right sided perfusion deficit, while 199 (50.3%) had a left sided deficit, and 58 (14.6%) had no evidence of a deficit. The best model was able to achieve an accuracy of 87.5% on validation data. Receiver Operating Characteristic (ROC) curves were generated for right sided perfusion deficit, left sided perfusion deficit, and no perfusion deficit and an Area Under the Curve (AUC) was calculated for each class. For a right sided deficit, the AUC was 0.90, for left sided deficit 0.96, and for no deficit the AUC was 0.93.
Conclusions: The field of machine learning, powered by convolutional neural networks for the task of image recognition and processing, has quickly developed in recent years. We have constructed an artificial neural network that can identify and classify the presence and sideness of a perfusion deficit CT perfusion imaging.
Patient Care: Improve time to diagnosis of ischemic stroke
Learning Objectives: By the conclusion of this session, participatns should be able to: 1) Understand the basic structure of an artificial neural network 2) Understand the role such networks can play in stroke triage