Machine learning for early Alzheimer’s diagnosis

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Anant Madabhushi and Case Western colleagues have used machine learning to diagnose Alzheimer’s disease via imaging data in a small study.  The goal is early intervention, which could potentially extend independence.

149 patients were analyzed using a Cascaded Multi-view Canonical Correlation (CaMCCo) algorithm, which integrates MRI scans, features of the hippocampus, glucose metabolism rates in the brain, proteomics, genomics, and MCI.

Parameters that distinguish between healthy and unhealthy subjects were selected first. The algorithm then selected, from the unhealthy variables, those that best distinguish who has mild cognitive impairment and who has Alzheimer’s disease.

This is an admirable attempt to diagnose a disease which currently has no cure.  ApplySci hopes that we will soon be able to combine early detection with a truly effective treatment.  Millions around the world are waiting.


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