Abstract P494: Automatically Predicting Modified Treatment in Cerebral Ischemia Scores From Patient Digital Subtraction Angiography Using Deep Learning

Abstract

Introduction: We propose a new method for quantifying the effect of endovascular therapy for acute ischemic stroke. Currently, an mTICI (modified treatment in cerebral ischemia) score is assigned manually to document the success of endovascular revascularization therapy. The mTICI score based on Digital Subtraction Angiography (DSA), due to visual assignment, has limitations in settings where standardization is pertinent. Methods: We hypothesize that mTICI scores can be classified successfully by deep learning and thus be used as a standardized imaging biomarker. We aim to develop a regression framework using classification models that can assign continuous score to patients depending on the success of therapy, resulting in a score that is more granular than the mTICI. We use deep learning and 3D Convolutional Neural Networks (CNN) to classify frontal post-intervention DSA 2D time series into the mTICI score categories of 0, 1, 2a, 2b, and 3. An mTICI score of 0 represents no perfusion and a score of 3 represents full perfusion. The DSA series serve as features where the time dimension is the third dimension for the CNN. For our preliminary research we have condensed our groupings into binary {0,1} (0 refers to mTICI of 0, 1, 2a while 1 refers to mTICI of 2b, 3) of frontal DSA to see if Deep Learning models can categorize between the different mTICI classes. Results: We reduced our original data size of 181 patients to 93 patients in binary group 0 and 88 patients in group 1. Using a train/test split of 0.2, we have achieved a test classification accuracy of 73%, and F1-Score of 72.2% on the binary dataset. This is a good statistical indication that neural networks are able to classify between DSA. Conclusion: Neural network models show promise as a method of distinguishing between DSA to be used as an automatic standardized scoring method for acute ischemic stroke procedures. We aim to expand this research to frontal and lateral DSA images to get more vascular information to improve model accuracies. We propose using the softmax score of the classifier as a new score which will be a standardized measurement for endovascular therapy success.

Percussion-Based Pipeline Ponding Detection Using a Convolutional Neural Network

“…CNN, one of the representative algorithms of deep learning, which automatically predicts and classifies the data [28], can overcome the drawbacks of percussive detection methods that requires engineering experience, and can therefore obtain superior results in visual classification tasks [29]. In the classification of audio data, as CNN cannot process sound directly [30], the sound of digital signals is often converted into spectrogram images [31] by a Short-time Fourier transform (STFT) or a wavelet transform.…”