How AI can help predict Alzheimer's disease progression

Paul De Sousa, head of life sciences at Massive Analytic and former researcher at Edinburgh University, writes about a study using artificial precognition AI to analyse results of protein biomarker tests associated with Alzheimer's disease progression. Accounting for over 30 million Disability Adjusted Life Years worldwide, Alzheimer's disease (AD) is a global societal challenge and a threat to healthcare systems around the world. A long history of failures of AD drug trials has highlighted the need for early detection and diagnosis to support patients and clinicians to implement the best life adjustments or medical interventions to alter the course of the disease and personalise the care of those at risk. Biomarkers are measurable indicators of the biological conditions of health, on which disease prognosis and diagnosis is founded. In AD there are a range of diagnostic procedures to detect these biomarkers including testing Cerebrospinal fluid (CSF) and PET scans for markers of amyloid-β and tau that can accurately detect AD pathology, but their cost and invasive nature preclude the broad accessibility required for early detection.