Abstract
BackgroundAn increase in lifespan in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer’s disease being the most prevalent. Advances in medical imaging and computational power enable new methods for the early detection of neurocognitive disorders with the goal of preventing or reducing cognitive decline. Computer-aided image analysis and early detection of changes in cognition is a promising approach for patients with mild cognitive impairment, sometimes a prodromal stage of Alzheimer’s disease dementia.MethodsWe conducted a systematic review following PRISMA guidelines of studies where machine learning was applied to neuroimaging data in order to predict whether patients with mild cognitive impairment might develop Alzheimer’s disease dementia or remain stable. After removing duplicates, we screened 452 studies and selected 116 for qualitative analysis.ResultsMost studies used magnetic resonance image (MRI) and positron emission tomography (PET) data but also magnetoencephalography. The datasets were mainly extracted from the Alzheimer’s disease neuroimaging initiative (ADNI) database with some exceptions. Regarding the algorithms used, the most common was support vector machine with a mean accuracy of 75.4%, but convolutional neural networks achieved a higher mean accuracy of 78.5%. Studies combining MRI and PET achieved overall better classification accuracy than studies that only used one neuroimaging technique. In general, the more complex models such as those based on deep learning, combined with multimodal and multidimensional data (neuroimaging, clinical, cognitive, genetic, and behavioral) achieved the best performance.ConclusionsAlthough the performance of the different methods still has room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare professionals.
Highlights
An increase in lifespan in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer’s disease being the most prevalent
The increase in lifespan experienced in Western societies has largely been driven by medical and technological advances [1]; this improvement has resulted in an increasing number of people diagnosed with neurocognitive disorders
mild cognitive impairment (MCI) can, in some cases, be a prodromal stage of dementia, especially for Alzheimer’s disease (AD) [6]. It is worth mentioning at this point that AD should be considered as a continuum, where patients with MCI that will eventually progress to AD dementia already have AD, but the cognitive symptoms have not yet fully manifested
Summary
An increase in lifespan in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer’s disease being the most prevalent. Advances in medical imaging and computational power enable new methods for the early detection of neurocognitive disorders with the goal of preventing or reducing cognitive decline. Pharmacological and non-pharmacological treatments have proven to be effective in reducing cognitive and behavioral symptoms in the early stages of the disease [5] In light of these treatments, recent studies have focused on detecting patients with cognitive impairment that have not reached dementia in order to delay or prevent its development. MCI can, in some cases, be a prodromal stage of dementia, especially for AD [6] It is worth mentioning at this point that AD should be considered as a continuum, where patients with MCI that will eventually progress to AD dementia already have AD, but the cognitive symptoms have not yet fully manifested. It is important to differentiate between those MCI patients that will progress to AD dementia and those who will remain stable
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