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Updated: Oct 8, 2025
Computerized Adaptive Testing System of Functional Assessment of Stroke
Published on: January 7, 2019
A Review on Computer Aided Diagnosis of Acute Brain Stroke
Mahesh Anil Inamdar1, Udupi Raghavendra2, Anjan Gudigar2
1Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India.
Insights
Computer-aided diagnosis using machine learning and deep learning shows promise for salvaging brain tissue after stroke. This review highlights current techniques and future research directions for stroke detection and segmentation.
Area of Science:
- Neurology
- Medical Imaging
- Artificial Intelligence
Background:
- Stroke is a leading global cause of death, affecting millions annually.
- Prompt detection and intervention are crucial to prevent permanent brain damage, particularly in the ischemic penumbra.
- Advancements in computer-aided diagnosis (CAD) offer new avenues for stroke management.
Purpose of the Study:
- To review the current status and challenges of CAD, machine learning (ML), and deep learning (DL) techniques for stroke detection and segmentation.
- To analyze the application of CT and MRI modalities in stroke diagnosis.
- To identify future research directions in AI-driven stroke management.
Main Methods:
- Systematic review of 177 research papers published between 2010 and 2021.
- Adherence to Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines.
- Focus on computer-aided diagnosis (CAD), machine learning (ML), and deep learning (DL) for stroke detection and segmentation using CT and MRI.
Main Results:
- Computer-aided diagnosis, ML, and DL techniques are increasingly utilized for stroke detection and lesion segmentation.
- CT and MRI are the primary imaging modalities employed in these AI-driven approaches.
- Significant progress has been made, but challenges remain in optimizing these techniques.
Conclusions:
- AI-based methods show significant potential for improving stroke detection and segmentation accuracy.
- Further research is needed to refine current techniques and explore new prospective areas.
- Identifying the preferred imaging modality and addressing domain requirements are key for future advancements.
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.
