Role of Artificial Intelligence in Brain Stroke Management: A survey
- Resource Type
- Conference
- Authors
- Bhatia, Suhavi Kaur; Goyal, Sumit; Arora, Tripatjot Singh; Chhabra, Rishu
- Source
- 2023 2nd Edition of IEEE Delhi Section Flagship Conference (DELCON) Delhi Section Flagship Conference (DELCON), 2023 2nd Edition of IEEE. :1-5 Feb, 2023
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Surveys
Deep learning
Machine learning algorithms
IEEE Sections
Medical services
Learning (artificial intelligence)
Stroke (medical condition)
brain stroke
deep learning
ischemic stroke
hemorrhagic stroke
machine learning
- Language
Deep Learning (DL) and Machine Learning (ML) are the key subsets of Artificial Intelligence that have evolved into an important tool in healthcare settings. Brain stroke management is one of the applications where these computer based techniques can help the patients with better diagnosis and individualized clinical care. However, stroke diagnosis and prognosis are dependent on a number of clinical and individual factors. To increase diagnostic and prognostic accuracy, the development of efficient ML and DL algorithms and thorough data collection and assimilation is the key. In this paper, we present a survey of deep learning and machine learning techniques for brain stroke management. The techniques have been categorized on the basis of type of cerebral stroke: ischemic stroke and hemorrhagic stroke. The paper concludes with the future research directions in the area of brain stroke management.