A comprehensive review for artificial intelligence on neuroimaging in rehabilitation of ischemic stroke

Abstract

Stroke is the second leading cause of death worldwide, with ischemic stroke accounting for a significant proportion of morbidity and mortality among stroke patients. Ischemic stroke often causes disability and cognitive impairment in patients, which seriously affects the quality of life of patients. Therefore, how to predict the recovery of patients can provide support for clinical intervention in advance and improve the enthusiasm of patients for rehabilitation treatment. With the popularization of imaging technology, the diagnosis and treatment of ischemic stroke patients are often accompanied by a large number of imaging data. Through machine learning and Deep Learning, information from imaging data can be used more effectively. In this review, we discuss recent advances in neuroimaging, machine learning, and Deep Learning in the rehabilitation of ischemic stroke.

Keywords: CT; MRI; artificial intelligence; ischemic stroke; rehabilitation.

Figure 1

Number of published studies on ischemic stroke in the past 15 years.

Figure 2

Pie chart of classes of studies included in this review.

Figure 3

Flow chart of paper selection process.

Figure 4

The CT images of two ischemic stroke patients undergoing thrombectomy (A) a 73-year-old male patient with an NIHSS score of 24 at the onset; and (B) a 78-year-old male patient with an NIHSS score of 14 at the onset.

Figure 5

The MRI images at the onset of ischemic stroke for two patients, both of whom underwent rehabilitation training after their conditions stabilized, are presented. (A) A 57-year-old female patient, post-rehabilitation Berg Balance Scale score of 2, Functional Independence Measure (FIM) score of 6, and Modified Barthel Index of 3. (B) A 72-year-old male patient, post-rehabilitation Berg Balance Scale score of 0, FIM score of 1, and Modified Barthel Index of 0.