Early Identification of High-Risk TIA or Minor Stroke Using Artificial Neural Network

Abstract

Background and Purpose: The risk of recurrent stroke following a transient ischemic attack (TIA) or minor stroke is high, despite of a significant reduction in the past decade. In this study, we investigated the feasibility of using artificial neural network (ANN) for risk stratification of TIA or minor stroke patients. Methods: Consecutive patients with acute TIA or minor ischemic stroke presenting at a tertiary hospital during a 2-year period were recruited. We collected demographics, clinical and imaging data at baseline. The primary outcome was recurrent ischemic stroke within 1 year. We developed ANN models to predict the primary outcome. We randomly down-sampled patients without a primary outcome to 1:1 match with those with a primary outcome to mitigate data imbalance. We used a 5-fold cross-validation approach to train and test the ANN models to avoid overfitting. We employed 19 independent variables at baseline as the input neurons in the ANN models, using a learning algorithm based on backpropagation to minimize the loss function. We obtained the sensitivity, specificity, accuracy and the c statistic of each ANN model from the 5 rounds of cross-validation and compared that of support vector machine (SVM) and Naïve Bayes classifier in risk stratification of the patients. Results: A total of 451 acute TIA or minor stroke patients were enrolled. Forty (8.9%) patients had a recurrent ischemic stroke within 1 year. Another 40 patients were randomly selected from those with no recurrent stroke, so that data from 80 patients in total were used for 5 rounds of training and testing of ANN models. The median sensitivity, specificity, accuracy and c statistic of the ANN models to predict recurrent stroke at 1 year was 75%, 75%, 75%, and 0.77, respectively. ANN model outperformed SVM and Naïve Bayes classifier in our dataset for predicting relapse after TIA or minor stroke. Conclusion: This pilot study indicated that ANN may yield a novel and effective method in risk stratification of TIA and minor stroke. Further studies are warranted for verification and improvement of the current ANN model.

Artificial neural network based prediction of postthrombolysis intracerebral hemorrhage and death

“…We herein exploited some of the benefits of neural networks, such as the requirement of little (or no) formal statistical training, minor user input, the implicit capacity to detect complex nonlinear relationships between dependent and independent variables, and the ability to delineate all possible interactions between predictor variables 35 . The high predictive power of our models is consistent with known application of ANN to assist with the diagnosis of stroke and prediction of its outcome [35][36][37] . More specifically, the present study demonstrated that the application of ANN helped improve the accuracy of predicting sICH within 72 h of intravenous tPA and the risk of death after the intravenous administration of tPA.…”

Clinical Risk Score for Predicting Recurrence Following a Cerebral Ischemic Event

“…Deep neural networks have been studied for ischemic stroke risk assessment with promising results (92). Artificial neural network have been used to differentiate stroke from stroke mimics (93) and to identify patients at high risk for TIA or minor stroke (94). The use of machine learning in prediction modeling is encouraging but require further studies.…”

Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine

“…Two different teams of scientists have used ML algorithms to predict three-month functional outcomes following ischemic stroke [ 91 , 92 ]. ML has also been utilized to predict 90-day readmission [ 93 ] and one-year recurrence in patients with ischemic stroke [ 94 ]. In patients undergoing endovascular treatment for ischemic stroke, ML algorithms did not improve outcome prediction when compared to logistic regression [ 95 ].…”