A study published in the Journal of Medical Imaging unveils the use of machine learning to detect the early stages of Alzheimer’s disease (AD) by functional magnetic resonance imaging.
Alzheimer’s disease is a neurodegenerative condition primarily occurring in late-adulthood and begins with symptoms of cognitive decline.
Researchers from Texas Tech University developed a deep-learning algorithm called a convolutional neural network able to distinguish between the fMRI signals of healthy individuals, patients with mild cognitive impairment (MCI), and patients with Alzheimer’s.
“We present one such synergy of fMRI and deep learning, where we apply a simplified yet accurate method using a modified 3D convolutional neural networks to resting-state fMRI data for feature extraction and classification of Alzheimer’s disease,” the co-authors explained in their findings.
“The convolutional neural network is designed in such a way that it uses the fMRI data with much less preprocessing, preserving both spatial and temporal information.”
By the study’s conclusion, the network was efficient enough to differentiate between fMRI data of healthy participants and those with either mild cognitive impairment or Alzheimer’s.
Researchers concluded the following, as stated in their journal publication: “This convolutional neural network can detect and differentiate between the earlier and later stages of MCI and AD and hence, it may have potential clinical applications in both early detection and better diagnosis of Alzheimer’s disease.”