Applicable artificial intelligence for brain disease: A survey

Chenxi Huang, Jian Wang, Shui Hua Wang, Yu Dong Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

Brain diseases threaten hundreds of thousands of people over the world. Medical imaging techniques such as MRI and CT are employed for various brain disease studies. As artificial intelligence succeeded in image analysis, scientists employed artificial intelligence, especially deep learning technologies, to assist brain disease studies. The AI applications for brain disease studies can be divided into two categories. The first category is preprocessing, including denoising, registration, skull-stripping, intensity normalization, and data augmentation. The second category is the clinical application that contains lesion segmentation, disease detection, grade classification, and outcome prediction. In this survey, we reviewed over one hundred representative papers on how to apply AI to brain disease studies. We first introduced AI-based preprocessing for brain disease studies. Second, we reviewed the influential works of AI-based brain disease studies. At last, we also discussed three development trends in the future. We hope this survey will inspire both expert-level researchers and entry-level beginners.

Original languageEnglish
Pages (from-to)223-239
Number of pages17
JournalNeurocomputing
Volume504
DOIs
Publication statusPublished - 14 Sept 2022
Externally publishedYes

Keywords

  • Artificial intelligence
  • Brain disease
  • Convolutional neural network
  • Deep learning
  • Stroke

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