Deep learning guided stroke management: a review of clinical applications

Abstract

Stroke is a leading cause of long-term disability, and outcome is directly related to timely intervention. Not all patients benefit from rapid intervention, however. Thus a significant amount of attention has been paid to using neuroimaging to assess potential benefit by identifying areas of ischemia that have not yet experienced cellular death. The perfusion-diffusion mismatch, is used as a simple metric for potential benefit with timely intervention, yet penumbral patterns provide an inaccurate predictor of clinical outcome. Machine learning research in the form of deep learning (artificial intelligence) techniques using deep neural networks (DNNs) excel at working with complex inputs. The key areas where deep learning may be imminently applied to stroke management are image segmentation, automated featurization (radiomics), and multimodal prognostication. The application of convolutional neural networks, the family of DNN architectures designed to work with images, to stroke imaging data is a perfect match between a mature deep learning technique and a data type that is naturally suited to benefit from deep learning's strengths. These powerful tools have opened up exciting opportunities for data-driven stroke management for acute intervention and for guiding prognosis. Deep learning techniques are useful for the speed and power of results they can deliver and will become an increasingly standard tool in the modern stroke specialist's arsenal for delivering personalized medicine to patients with ischemic stroke.

An overview of deep learning in medical imaging focusing on MRI

“…There are several thorough reviews and overviews of the field to consult for more information, across modalities and organs, and with different points of view and level of technical details. For example the comprehensive review [102] 27 , covering both medicine and biology and spanning from imaging applications in healthcare to protein-protein interaction and uncertainty quantification; key concepts of deep learning for clinical radiologists [103,104,105,106,107,108,109,110,111,112], including radiomics and imaging genomics (radiogenomics) [113], and toolkits and libraries for deep learning [114]; deep learning in neuroimaging and neuroradiology [115]; brain segmentation [116]; stroke imaging [117,118]; neuropsychiatric disorders [119]; breast cancer [120,121]; chest imaging [122]; imaging in oncology [123,124,125]; medical ultrasound [126,127]; and more technical surveys of deep learning in medical image analysis [42,128,129,130]. Finally, for those who like to be hands-on, there are many instructive introductory deep learning tutorials available online.…”

Threshold-based Identification of Persistent Infarction after Successful Endovascular Treatment: Re-evaluating the “Core”

“…Therefore, based on the more recent evidence one might argue whether a threshold-based definition of persistent infarction in baseline DWI is a useful imaging criterion to start with. Recently, machine learning-based algorithms have been proposed for the segmentation of persistent infarction from MRI data, which may serve as an alternative to threshold-based algorithms 9,20,28 in the near future. Furthermore, advances in MRI technology, such as e.g.…”

Integrating uncertainty in deep neural networks for MRI based stroke analysis

“…Besides clinical and patient data, medical experts rely on multi-modal Computed Tomography (CT) and Magnetic Resonance (MR) imaging for diagnoses and treatment decisions (Baird & Warach, 1998;Chilla, et al, 2015). To both support physicians in their diagnosis and accelerate treatment decision-making, methods for automatic image analysis are increasingly integrated into acute stroke care (Feng, et al, 2018;Pinto, et al, 2018). Deep Convolutional Neural Networks (CNNs) are state-of-the-art to recognize pathological features such as ischemic stroke lesions on brain images (Bernal, et al, 2019;Havaei, et al, 2017).…”