Abstract
Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., ‘ischemic penumbra’) can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta–Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas.
Highlights
In the recent past, stroke has become the foremost cause of mortality and health–disability worldwide, causing over 6.6 million deaths annually [1], and with up to 50% of survivors being chronically disabled [2]
These can be broadly grouped into two types: (a) region of interest (ROI) detection followed by stroke prediction, and (b) segmentation
In our study we found that there is an urgent need for better imaging analysis technology to improve inference, and a need for advancements in imaging techniques, as detection of incomplete infarction in the acute stroke setting on magnetic resonance imaging (MRI) or computed tomogram (CT) is currently not feasible
Summary
Mahesh Anil Inamdar 1 , Udupi Raghavendra 2, * , Anjan Gudigar 2 , Yashas Chakole 2 , Ajay Hegde 3 , Girish R. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
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