{"id":83956,"date":"2020-05-04T09:12:02","date_gmt":"2020-05-04T13:12:02","guid":{"rendered":"http:\/\/www.bumc.bu.edu\/busm\/?p=83956"},"modified":"2020-05-11T11:42:25","modified_gmt":"2020-05-11T15:42:25","slug":"ai-algorithm-can-accurately-predict-risk-diagnose-alzheimers-disease","status":"publish","type":"post","link":"https:\/\/www.bumc.bu.edu\/camed\/2020\/05\/04\/ai-algorithm-can-accurately-predict-risk-diagnose-alzheimers-disease\/","title":{"rendered":"AI Algorithm Can Accurately Predict Risk, Diagnose Alzheimer\u2019s Disease"},"content":{"rendered":"

Researchers have developed a computer algorithm based on Artificial Intelligence (AI) that can accurately predict the risk for and diagnose Alzheimer\u2019s disease using a combination of brain magnetic resonance imaging (MRI), testing to measure cognitive impairment, along with data on age and gender.<\/p>\n

The AI strategy, based on a deep learning algorithm, is a type of machine learning framework. Machine learning is an AI application that enables a computer to learn from data and improve from experience. Alzheimer\u2019s disease is the primary cause of dementia worldwide. One in 10 people age 65 and older has Alzheimer\u2019s dementia. It is the sixth-leading cause of death in the United States.<\/p>\n

\"\"\u201cIf computers can accurately detect debilitating conditions such as Alzheimer\u2019s disease using readily available data such as a brain MRI scan, then such technologies have a wide-reaching potential, especially in resource-limited settings,\u201d explained corresponding author Vijaya B. Kolachalama, PhD, assistant professor of medicine. \u201cNot only can we accurately predict the risk of Alzheimer\u2019s disease but this algorithm\u00a0 can generate interpretable and intuitive visualizations of individual Alzheimer\u2019s disease risk en route to accurate diagnosis,\u201d said Dr. Kolachalama.<\/p>\n

The researchers obtained access to raw MRI scans of the brain, demographics and clinical information of individuals with Alzheimer\u2019s disease and the ones with normal cognition from four different national cohorts. Using data from one of these cohorts, they developed a novel deep learning model to predict Alzheimer\u2019s disease risk. They then showed that their model could accurately predict the disease status on the other independent cohorts.<\/p>\n

An international team of expert neurologists were then asked to perform the task of detecting Alzheimer\u2019s disease on the same set of cases. In this head-to-head comparison, the algorithm model performed slightly better than the average neurologist. They also showed that model-identified regions of high disease risk were highly aligned with autopsy reports of the brains on a few individuals who were deceased.<\/p>\n

According to the researchers, this study has broad implications for expanding the use of neuroimaging data such as MRI scans to accurately detect the risk of Alzheimer\u2019s disease at the point of care. \u201cIf we have accurate tools to predict the risk of Alzheimer\u2019s disease (such as the one we developed), that are readily available and which can use routinely available data such as a brain MRI scan, then they have the potential to assist clinical practice, especially in memory clinics.\u201d<\/p>\n

The researchers believe their methodology can be extended to other organs in the body and develop predictive models to diagnose other degenerative diseases.<\/p>\n

These findings appear online <\/a>in the journal Brain<\/em>.<\/p>\n

\n

This project was supported in part by the National Center for Advancing Translational Sciences, National Institutes of Health, through BU-CTSI Grant (1UL1TR001430), a Scientist Development Grant (17SDG33670323) from the American Heart Association, and a Hariri Research Award from the Hariri Institute for Computing and Computational Science & Engineering at Boston University, Framingham Heart Study\u2019s National Heart, Lung and Blood Institute contract (N01-HC-25195; HHSN268201500001I) and NIH grants (R56-AG062109, AG008122, R01-AG016495, and R01-AG033040). Additional support was provided by Boston University’s Affinity Research Collaboratives program and Boston University Alzheimer’s Disease Center (P30-AG013846).<\/p>\n","protected":false},"excerpt":{"rendered":"

Ability to identify disease will lead to better, proactive care of affected individuals.<\/p>\n","protected":false},"author":903,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[91,90],"tags":[],"_links":{"self":[{"href":"https:\/\/www.bumc.bu.edu\/camed\/wp-json\/wp\/v2\/posts\/83956"}],"collection":[{"href":"https:\/\/www.bumc.bu.edu\/camed\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.bumc.bu.edu\/camed\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.bumc.bu.edu\/camed\/wp-json\/wp\/v2\/users\/903"}],"replies":[{"embeddable":true,"href":"https:\/\/www.bumc.bu.edu\/camed\/wp-json\/wp\/v2\/comments?post=83956"}],"version-history":[{"count":9,"href":"https:\/\/www.bumc.bu.edu\/camed\/wp-json\/wp\/v2\/posts\/83956\/revisions"}],"predecessor-version":[{"id":84198,"href":"https:\/\/www.bumc.bu.edu\/camed\/wp-json\/wp\/v2\/posts\/83956\/revisions\/84198"}],"wp:attachment":[{"href":"https:\/\/www.bumc.bu.edu\/camed\/wp-json\/wp\/v2\/media?parent=83956"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.bumc.bu.edu\/camed\/wp-json\/wp\/v2\/categories?post=83956"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.bumc.bu.edu\/camed\/wp-json\/wp\/v2\/tags?post=83956"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}