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Validation of automated hippocampus volume assessment using deep learning convolutional neural networks in patients with Alzheimer's disease

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. R.; Harrun, N. H.

2023-04-17 radiology and imaging
10.1101/2023.04.11.23288432 medRxiv
Show abstract

BackgroundDementia 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 Alzheimers 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. MethodOngoing research is being conducted to develop new treatments and improve our understanding of the disease by using VBM to compare sensitivity and specificity with the HippoDeep toolbox. ResultWe were able to validate ADs hippocampal volume compared to age-matched healthy controls (HC) based on HippoDeep Model by comparing it with VBM as the reference standard. ConclusionThere are significant differences between hippocampal volume in AD and HC that have been detected using VBM and HippoDeep analysis.

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