Feasibility study of deep neural networks to classify intracranial aneurysms using angiographic parametric imaging

Purpose: Angiographic Parametric Imaging (API) based on Digital Subtraction Angiography (DSA) of Intracranial Aneurysms (IA) can provide parameters related to contrast flow. In this study we propose to investigate the use of a Deep Neural Network (DNN) to analyze API parameters to classify IAs as un-treated or treated, quantify the prediction accuracy, and compare its performance with the Naïve Bayes (NB) and K-Nearest Neighbor (KNN) algorithms. Materials and Methods: DSA scans were obtained from patients with un-treated and treated IAs. Three datasets were created based on treatment method: coiled, flow-diverted and combined. These scans were analyzed to provide API parameters for the IA and corresponding main artery. IA parameters were normalized to the main artery parameters. Data was augmented by adding Gaussian noise. The DNN, NB and KNN models were trained on API parameters and tested to classify aneurysms as un-treated or treated. This was performed on each dataset for both normalized and un-normalized data. Results: The DNN had an accuracy and ROC AUC of 72.4% and 0.80 respectively on un-normalized coiled data, 87.9% and 0.95 respectively on normalized coiled data, 73.9% and 0.79 respectively on un-normalized flow-diverted data, 85.3% and 0.80 respectively on normalized flow-diverted data, 62.9% and 0.64 respectively on un-normalized combined data, 64.8% and 0.73 respectively on normalized combined data. Conclusions: This study proves feasibility of using DNNs to classify IAs and make other clinical predictions using normalized API data with treatment methods separated, in addition to being more effective than other classifiers.