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Cognitive Impairment Prediction by Normal Cognitive Brain MRI Scans Using Deep Learning

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AI 2022: Advances in Artificial Intelligence (AI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13728))

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Abstract

Alzheimer’s disease is a neurodegenerative disease without a cure and is one of the leading causes of death across the world. The early detection of cognitive impairment could prove crucial for reducing the occurrence of Alzheimer’s disease in the future. Significant research into detecting the disease from MRI images has already been performed and has produced encouraging results. However, there has been very limited work on predicting conversion from normal cognition to cognitive impairment. This study is aimed at producing a deep learning model to predict whether a subject will remain cognitively normal or progress to a state of cognitive impairment in the future. We found that the use of a patch-based approach combined with pre-trained ResNet-50 model using 3D MRI scans provide better results as compared to equivalent whole brain voxel-based approach and other state-of-the-art CNN models. Our proposed model achieved an accuracy of 90% and an area under the receiver operating characteristic curve of 0.99, which are better than the existing state-of-the-art results.

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Acknowledgements

Data was provided by OASIS-3: Principal Investigators: T. Benzinger, D. Marcus, J. Morris; NIH P50 AG00561, P30 NS09857781, P01 AG026276, P01 AG003991, R01 AG043434, UL1 TR000448, R01 EB009352. AV-45 doses were provided by Avid Radiopharmaceuticals, a wholly owned subsidiary of Eli Lilly.

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Authors and Affiliations

  1. Department of Computer Science and Software Engineering, The University of Western Australia, Perth, Australia

    Justin Bardwell, Ghulam Mubashar Hassan, Farzaneh Salami & Naveed Akhtar

  2. Department of Industrial Engineering, Alborz Campus, University of Tehran, Tehran, Iran

    Farzaneh Salami

Authors
  1. Justin Bardwell
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  2. Ghulam Mubashar Hassan
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  3. Farzaneh Salami
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  4. Naveed Akhtar
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Corresponding author

Correspondence to Ghulam Mubashar Hassan .

Editor information

Editors and Affiliations

  1. University of New South Wales, Sydney, NSW, Australia

    Haris Aziz

  2. University of Western Australia, Perth, WA, Australia

    Débora Corrêa

  3. University of Western Australia, Perth, WA, Australia

    Tim French

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Bardwell, J., Hassan, G.M., Salami, F., Akhtar, N. (2022). Cognitive Impairment Prediction by Normal Cognitive Brain MRI Scans Using Deep Learning. In: Aziz, H., Corrêa, D., French, T. (eds) AI 2022: Advances in Artificial Intelligence. AI 2022. Lecture Notes in Computer Science(), vol 13728. Springer, Cham. https://doi.org/10.1007/978-3-031-22695-3_40

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  • DOI: https://doi.org/10.1007/978-3-031-22695-3_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-22694-6

  • Online ISBN: 978-3-031-22695-3

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