Spotting these clues may allow for lifestyle changes that could possibly delay the disease's destruction of the brain. "Improving the diagnostic accuracy of Alzheimer's disease is an important clinical goal. If we are able to increase the diagnostic accuracy of the models in ways that can leverage existing data such as MRI scans, then that can be hugely beneficial," explained corresponding author Vijaya B. Kolachalama, PhD, assistant professor of medicine at Boston University School of Medicine (BUSM). Using an advanced AI (artificial intelligence) framework based on game theory (known as generative adversarial network or GAN), Kolachalama and his team processed brain images (some low and high quality) to generate a model that was able to classify Alzheimer's disease with improved accuracy. Quality of an MRI scan is dependent on the scanner instrument that is used.
The deep learning algorithm, developed by researchers at the Boston University School of Medicine, uses a combination of brain magnetic resonance imaging (MRI) testing to measure cognitive impairment, along with data on age and gender, which helps to accurately predict the risk of Alzheimer's Disease. Alzheimer's disease is the primary cause of dementia worldwide. One in 10 people age 65 and older has Alzheimer's dementia and it is the primary cause of dementia worldwide. The study has been published in the journal Brain. The researchers used MRI scans of the brain, demographics, and clinical information of individuals with Alzheimer's disease as well as ones with normal cognition.
Recently, machine learning techniques especially predictive modeling and pattern recognition in biomedical sciences from drug delivery system to medical imaging has become one of the important methods which are assisting researchers to have deeper understanding of entire issue and to solve complex medical problems. Deep learning is a powerful machine learning algorithm in classification while extracting low to high-level features. In this paper, we used convolutional neural network to classify Alzheimer's brain from normal healthy brain. The importance of classifying this kind of medical data is to potentially develop a predict model or system in order to recognize the type disease from normal subjects or to estimate the stage of the disease. Classification of clinical data such as Alzheimer's disease has been always challenging and most problematic part has been always selecting the most discriminative features. Using Convolutional Neural Network (CNN) and the famous architecture LeNet-5, we successfully classified structural MRI data of Alzheimer's subjects from normal controls where the accuracy of test data on trained data reached 98.84%. This experiment suggests us the shift and scale invariant features extracted by CNN followed by deep learning classification is most powerful method to distinguish clinical data from healthy data in fMRI. This approach also enables us to expand our methodology to predict more complicated systems.
Since its invention in the 1970s, magnetic resonance imaging (MRI) has opened up a window onto the world beneath our skin. By exploiting the way the nuclei of hydrogen atoms in water and fat molecules resonate in a strong magnetic field, MRI can generate high-contrast three-dimensional images of soft body tissues, joints, and bones. MRI allows clinicians to see evidence of injury and disease within the body, ranging from torn muscle to damaged cartilage, ligaments, and tendons, as well as tumors or other disease lesions within major organs, and blood-flow blockages in the brain, all without the ionizing radiation of the X-rays used in computed tomography (CT) scans. There is, however, a considerable usability problem with the MRI scanner as we currently know it: the technology takes far too long to acquire images, forcing patients to lie still in the confined maw of a massive magnet for up to an hour. With the observable world reduced to a halo of grayish plastic just inches from one's nose, it is a particularly tough experience for those suffering from claustrophobia.