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

Abstract

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.

Artificial Intelligence for Automated DWI/FLAIR Mismatch Assessment on Magnetic Resonance Imaging in Stroke: A Systematic Review

“…Cohort sizes were reasonably large in all five included studies and comparable to the cohort sizes in studies included in another systematic review with a focus on the potential of automated segmentation of stroke lesions in MRI images [ 27 ].…”

“…Cohort sizes were reasonably large in all five included studies a cohort sizes in studies included in another systematic review with a of automated segmentation of stroke lesions in MRI images [27]. ranged from 0.70 to 0.82 and from 0.74 to 0.84, respectively.…”

Taxonomy of Acute Stroke: Imaging, Processing, and Treatment

“…In addition, within the first 3 h, this sensitivity is lowered to 7% for CT and 46% for MR [12]. Numerous neuroimage processing and analysis methods have been developed for stroke management based on various criteria and techniques, as reviewed in several papers covering CT [13][14][15], MR and CT [16,17], and artificial intelligence (AI) and deep learning (DL) techniques [18][19][20][21][22][23]. They facilitate the interpretation of stroke scans and assist in decision-making in stroke management.…”

Deep Learning Enhanced Internet of Medical Things to Analyze Brain Computed Tomography Images of Stroke Patients

“…We use ISLES 2018 (Ischemic Stroke Lesion Segmentation) dataset [27] for segmentation. The ISLES 2018 dataset, a key component of our research, is a robust and publicly available collection of multi-center, multi-vendor, and multi-disease stage clinical data.…”