A Review of Deep Learning Approaches for Non-Invasive Cognitive Impairment Detection

Keywords:

Alzheimer’s Disease, Cognitive Impairment Detection, Deep Learning Models.

Contributions:

• the advantages and limitations of different integrated non-invasive modalities.
• review studies that utilized various modalities to propose cognitive impairment detection systems.
• comprehensive outline of existing trends and future directions in deep learning methods for cognitive impairment detection.
• concluding with the overall impact of these methods on early detection of cognitive decline.
• insights into disease progression across large populations.

Production: An overview of the diverse data modalities on MCI research.

Used Framework:

BibTex:

@article{alsuhaibani2025review,
  title={A Review of Machine Learning Approaches for Non-Invasive Cognitive Impairment Detection},
  author={Alsuhaibani, Muath and Fard, Ali Pourramezan and Sun, Jian and Poor, Farida Far and Pressman, Peter S and Mahoor, Mohammad H},
  journal={IEEE Access},
  year={2025},
  publisher={IEEE}
}

Recommended citation: M. Alsuhaibani, A. Pourramezan Fard, J. Sun, F. Far Poor, P. S. Pressman and M. H. Mahoor, "A Review of Machine Learning Approaches for Non-Invasive Cognitive Impairment Detection," in IEEE Access, vol. 13, pp. 56355-56384, 2025, doi: 10.1109/ACCESS.2025.3555176.
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