A team from Stevens Institute of Technology has developed an artificial intelligence tool that can diagnose Alzheimer’s disease with more than 95 percent accuracy, eliminating the need for expensive scans or in-person testing. In addition, the algorithm is also able to explain its conclusions, enabling human experts to check the accuracy of its diagnosis. Alzheimer’s disease can impact a person’s use of language, the researchers noted. For example, people with Alzheimer’s tend to replace nouns with pronouns, and they can express themselves in a very roundabout, awkward way. The team designed an explainable AI tool that uses attention mechanisms and a convolutional neural network to accurately identify well-known signs of Alzheimer’s, as well as subtle linguistic patterns that were previously overlooked.
Researchers trained the algorithm using texts composed by both healthy subjects and known Alzheimer’s sufferers describing a drawing of children stealing cookies from a jar. The team converted each individual sentence into a unique numerical sequence, or vector, representing a specific point in a 512-dimensional space. This kind of approach allows even complex sentences to be assigned a concrete numerical value, making it easier to analyze structural and thematic relationships between sentences. Using those vectors along with handcrafted features, the AI gradually learned to spot differences between sentences composed by healthy or unhealthy individuals, and was able to determine with significant accuracy how likely any given text was to have been produced by a person with Alzheimer’s.
In the future, AI tools may be able to diagnose Alzheimer’s using any text, from emails to social media posts. However, to develop such an algorithm, researchers would need to train it on many different kinds of texts produced by known Alzheimer’s sufferers instead of just picture descriptions.
Combinostics, a neurology technology company creates a Dementia Differential Analysis report based on the findings on MRI of head. Existing technologies compare against cognitively normal reference data only – the artificial intelligence-enabled application quantifies and evaluates patient MRI data against the distributions of key dementia-specific imaging biomarkers and reference data from approximately 2,000 patients with a confirmed neurodegenerative disease, including front-otemporal dementia, Alzheimer’s disease and vascular dementia, the company explained. This means that in the near future, based on digital biomarkers, a most likely diagnosis can be generated from a radiology or another form of diagnostic study.