Application of Machine Learning Techniques for Characterization of Ischemic Stroke with MRI Images: A Review

• Published in: Diagnostics (Basel) 2022-10, Vol.12 (10), p.2535
• Main Authors: Subudhi, Asit, Dash, Pratyusa, Mohapatra, Manoranjan, Tan, Ru-San, Acharya, U. Rajendra, Sabut, Sukanta
• Format: Article
• Language: English
• Subjects: artificial intelligence; Automation; Blood vessels; brain stroke; Classification; computer-aided-diagnosis; Decision making; decision support system; Diagnosis; Disability; Health aspects; Machine learning; Magnetic resonance imaging; medical imaging; Review; Search strategies; Stroke; Stroke (Disease); Systematic review; Technology application
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics12102535

Magnetic resonance imaging (MRI) is a standard tool for the diagnosis of stroke, but its manual interpretation by experts is arduous and time-consuming. Thus, there is a need for computer-aided-diagnosis (CAD) models for the automatic segmentation and classification of stroke on brain MRI. The heterogeneity of stroke pathogenesis, morphology, image acquisition modalities, sequences, and intralesional tissue signal intensity, as well as lesion-to-normal tissue contrast, pose significant challenges to the development of such systems. Machine learning (ML) is increasingly being used in predictive neuroimaging diagnosis and prognostication. This paper reviews image processing and machine learning techniques that have been applied to detect ischemic stroke on brain MRI, including details on image acquisition, pre-processing, techniques to segment, extraction of features, and classification into stroke types. The main objective of this work is to find the state-of-art machine learning techniques used to predict the ischemic stroke and their application in clinical set-up. The article selection is performed according to PRISMA guideline. The state-of-the-art on automated MRI stroke diagnosis, with a focus on machine learning, is discussed, along with its advantages and limitations. We found that the various machine learning models discussed in this article are able to detect the infarcts with an acceptable accuracy of 70–90%. However, no one has highlighted the time complexity to predict the stroke in the model developed, which is an important factor. The work concludes with proposals for future recommendations for building efficient and robust deep learning (DL) models for quantitative brain MRI analysis. In recent work, with the application of DL approaches, using large datasets to train the models has improved the detection accuracy and reduced computational complexity. We suggest that the design of a decision support system based on artificial intelligence (AI) and clinical data presenting symptoms is essential to support clinicians to accelerate diagnosis and timeous therapy in the emergency management of stroke.