Early Identification of Alzheimer’s Disease Using Medical Imaging: A Review From a Machine Learning Approach Perspective

Dementia is a medical syndrome resulting in substantial memory loss or deterioration and other cognitive capabilities, beyond the normal aging process. Alzheimer’s disease (AD) is the leading cause of dementia in aged adults, affecting up to 70% of the dementia patients, and posing a serious public health hazard in the twenty-first century. With the growing lifespan, the number of AD patients is also increasing, and estimated that by the year 2050, 135 million people will be affected. With age being the predominant dementia factor, the dominance ranges from 1–2 percent in the age group of 65 to 30 percent at 85. AD is a progressive, irreversible and neuro-degenerative disease with a long pre-clinical period, affecting brain cells leading to memory loss, misperception, learning problems, and improper decisions. Given its significance, presently no treatment options are available, although disease advancement can be retarded through medication. Unfortunately, AD is diagnosed at a very later stage, after irreversible damages to the brain cells have occurred, when there is no scope to prevent further cognitive decline. Individual diagnoses of AD are now based mostly on neuropsychological testing and clinical examination, but only post-mortem brain study may confirm the final diagnosis. The use of non-invasive neuroimaging procedures capable of detecting AD at preliminary stages is crucial for providing treatment retarding disease progression, and has stood as a promising area of research. We conducted a comprehensive assessment of papers employing machine learning to predict AD using neuroimaging data. Most of the studies employed brain images from Alzheimer’s disease neuroimaging initiative (ADNI) dataset, consisting of magnetic resonance image (MRI) and positron emission tomography (PET) images. The most widely used method, the support vector machine (SVM), has a mean accuracy of 75.4 percent, whereas convolutional neural networks(CNN) have a mean accuracy of 78.5 percent. Better classification accuracy has been achieved by combining MRI and PET, rather using single neuroimaging technique. Overall, more complicated models, like deep learning, paired with multimodal and multidimensional data (neuroimaging, cognitive, clinical, behavioural and genetic) produced superlative results. Our work shows that promising results have been achieved, but still there is a room for performance improvement of the proposed methods, providing assistance to healthcare professionals and clinicians