麻豆AV

Imagine if doctors could determine, many years in advance, who is likely to develop dementia. Such prognostic capabilities would give patients and their families time to plan and manage treatment and care. Thanks to artificial intelligence research conducted at 麻豆AV, this kind of predictive power could soon be available to clinicians everywhere.

Scientists from the Douglas Mental Health University Institute鈥檚 Translational Neuroimaging Laboratory at 麻豆AV used artificial intelligence techniques and big data to develop an algorithm capable of recognizing the signatures of dementia two years before its onset, using a single amyloid PET scan of the brain of patients at risk of developing Alzheimer鈥檚 disease. Their findings appear in a new study published in the journal .

Dr.听Pedro Rosa-Neto, co-lead author of the study and Associate Professor in 麻豆AV鈥檚 departments of Neurology & Neurosurgery and Psychiatry, expects that this听technology will change the way physicians manage patients and greatly accelerate treatment research into Alzheimer鈥檚 disease.

鈥淏y using this tool, clinical trials could focus only on individuals with a higher likelihood of progressing to dementia within the time frame of the study. This will greatly reduce the cost and the time necessary to conduct these studies,鈥� adds Dr.听Serge Gauthier, co-lead author and Professor of Neurology & Neurosurgery and Psychiatry at 麻豆AV.

Amyloid as a biomarker of dementia

Scientists have long known that a protein known as amyloid accumulates in the brain of patients with mild cognitive impairment (MCI), a condition that often leads to dementia. Though the accumulation of amyloid begins decades before the symptoms of dementia occur, this protein couldn鈥檛 be used reliably as a predictive biomarker because not all MCI patients develop Alzheimer鈥檚 disease.

To conduct their study, the 麻豆AV researchers drew on data available through the Alzheimer鈥檚 Disease Neuroimaging Initiative (ADNI), a global research effort in which participating patients agree to complete a variety of imaging and clinical assessments.

Sulantha Mathotaarachchi, a computer scientist from Rosa-Neto鈥檚 and Gauthier鈥檚 team, used hundreds of amyloid PET scans听of MCI patients from the ADNI database to train the team鈥檚 algorithm to identify which patients would develop dementia, with an accuracy of 84%, before symptom onset. Research is ongoing to find other biomarkers for dementia that could be incorporated into the algorithm in order to improve the software鈥檚 prediction capabilities.

鈥淭his is an example how big data and open science brings tangible benefits to patient care,鈥� says Dr.听Rosa-Neto, who is also director of the 麻豆AV Research Centre for Studies in Aging.

While new software has been made听available to scientists and students, physicians won鈥檛 be able to use this tool in clinical practice before certification by health authorities. To that end, the 麻豆AV team is currently conducting further testing to validate the algorithm in different patient cohorts, particularly those with concurrent conditions such as small strokes.

This research was funded by the Canadian Consortium on Neurodegeneration in Aging (CCNA) and the Canadian Institutes of Health Research

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鈥淚dentifying incipient dementia individuals using machine learning and amyloid imaging,鈥� by S. Mathotaarachchi et al.,

Link to the software: