Deep into the Brain: Artificial Intelligence in Stroke Imaging
By Eun-Jae Lee, Yong-Hwan Kim, Namkug Kim, D. Kang. 2017
223 citations recorded by the source corpus.
Artificial intelligence (AI), a computer system aiming to mimic human intelligence, is gaining increasing interest and is being incorporated into many fields, including medicine. Stroke medicine is one such area of application of AI, for improving the accuracy of diagnosis and the quality of patient care. For stroke management, adequate analysis of stroke imaging is crucial. Recently, AI techniques have been applied to decipher the data from stroke imaging and have demonstrated some promising results. In the very near future, such AI techniques may play a pivotal role in determining the therapeutic methods and predicting the prognosis for stroke patients in an individualized manner. In this review, we offer a glimpse at the use of AI in stroke imaging, specifically focusing on its technical principles, clinical application, and future perspectives.
Subfields: Epidemiology, Pulmonary and Respiratory Medicine, Rehabilitation
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Citations in the corpus
- A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI (2019, background): “More technically, assume we have deep learning classifier F (x) ∈ [0, 1] where Fi(x) ∈ [0, 1] is the probability that input x is classified into class i out of D classes.”
- An overview of deep learning in medical imaging focusing on MRI (2018, background): “For example the comprehensive review [101],24 covering both medicine and biology and spanning from imaging applications in healthcare to protein-protein interaction and uncertainty quantification; key concepts of deep learning for clinical radiologists [102–111], including radiomics and imaging geno”
- The Application of Medical Artificial Intelligence Technology in Rural Areas of Developing Countries (2018, background): “and suggested that AI technology may play a critical role in the management of stroke patients through an individualized plan.(2)”
- Artificial Intelligence Transforms the Future of Health Care. (2019, background): “Overall, deep learning is compelling in image recognition 15 as well as in modeling disease onset 16 using temporal relations among events.”