Comparison of deep learning convolutional neural networks method with conventional volume-based morphometry measurement of hippocampal volume in Alzheimer's disease

Authors

  • Nur Shahidatul Nabila Ibrahim Department of Radiology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Selangor, Malaysia.
  • Subapriya Suppiah 1) Department of Radiology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Selangor, Malaysia. 2) Nuclear Imaging Unit, Hospital Sultan Abdul Aziz Shah, Universiti Putra Malaysia, Selangor, Malaysia. 3) Centre for Diagnostic Nuclear Imaging, Universiti Putra Malaysia, Selangor, Malaysia 4) Malaysia Research Institute on Ageing (MyAgeing), Universiti Putra Malaysia, Selangor, Malaysia.
  • Buhari Ibrahim Department of Radiology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Selangor, Malaysia.
  • Nur Hafizah Mohad Azmi Department of Radiology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Selangor, Malaysia.
  • Vengkatha Priya Seriramulu Department of Radiology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Selangor, Malaysia.
  • Mazlyfarina Mohamad Centre for Diagnostic and Applied Health Sciences, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia.
  • Marsyita Hanafi Faculty of Engineering, Universiti Putra Malaysia, Selangor, Malaysia.
  • Hakimah Mohammad Sallehuddin 1) Malaysia Research Institute on Ageing (MyAgeing), Universiti Putra Malaysia, Selangor, Malaysia. 2) Department of Geriatric, Hospital Sultan Abdul Aziz Shah, Universiti Putra Malaysia, Selangor, Malaysia.
  • Rizah Mazzuin Razali Department of Medicine, Hospital Kuala Lumpur, Ministry of Health of Malaysia, Kuala Lumpur, Malaysia.
  • Noor Harzana Harrun Pandamaran Health Clinic, Ministry of Health of Malaysia, Selangor, Malaysia.

DOI:

https://doi.org/10.31117/neuroscirn.v6i4.248

Keywords:

Hippocampal volume, Alzheimer's disease, Deep learning, Convolutional neural network

Abstract

Dementia is a spectrum of diseases characterised by a progressive and irreversible decline in cognitive function. Appropriate tools and references are essential for evaluating individuals' cognitive levels, especially hippocampal volume, as it is the commonly used biomarker in detecting Alzheimer's disease (AD). It is important to note that while there is no cure for dementia, early intervention and support can greatly improve the lives of those affected. Our ongoing AD research is being conducted to develop new treatments and improve our understanding of the disease by using voxel-based morphometry (VBM) to compare sensitivity and specificity with the HippoDeep toolbox. We validated AD's hippocampal volume compared to age-matched healthy controls (HC) based on the HippoDeep Model by comparing it with VBM as the reference standard. Significant differences between hippocampal volume in AD and HC have been detected using VBM and HippoDeep analysis.

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Published

2023-12-23

How to Cite

Ibrahim, N. S. N., Suppiah, S., Ibrahim, B., Mohad Azmi, N. H., Seriramulu, V. P., Mohamad, M., Hanafi, M., Mohammad Sallehuddin, H., Razali, R. M. and Harrun, N. H. (2023) “Comparison of deep learning convolutional neural networks method with conventional volume-based morphometry measurement of hippocampal volume in Alzheimer’s disease”, Neuroscience Research Notes, 6(4), pp. 248.1–248.10. doi: 10.31117/neuroscirn.v6i4.248.

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Vol. 6 No. 4 (2023): Regular Issue

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Research Notes

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Copyright (c) 2023 Nur Shahidatul Nabila Ibrahim, Subapriya Suppiah, Buhari Ibrahim, Nur Hafizah Mohad Azmi, Vengkatha Priya Seriramulu, Mazlyfarina Mohamad, Marsyita Hanafi, Hakimah Mohammad Sallehuddin, Rizah Mazzuin Razali, Noor Harzana Harrun

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