In a new study, researchers developed an A.I. algorithm that promises to accurately diagnose Alzheimer’s without the need for expensive scans or in-person testing.
They developed the algorithm by detecting subtle differences in the way that Alzheimer’s sufferers use language.
The software not only can diagnose Alzheimer’s, at negligible cost, with more than 95% accuracy but is also capable of explaining its conclusions, allowing physicians to double-check the accuracy of its diagnosis.
The research was conducted by a team at Stevens Institute of Technology.
It has long been known that Alzheimer’s can affect a person’s use of language.
People with Alzheimer’s typically replace nouns with pronouns, such as by saying ‘He sat on it’ rather than ‘The boy sat on the chair.’
Patients might also use awkward circumlocutions, saying “My stomach feels bad because I haven’t eaten” instead of simply “I’m hungry.”
By designing an explainable A.I. engine that uses attention mechanisms and convolutional neural network— a form of A.I. that learns over time— the team was able to develop software that could not only accurately identify well-known telltale signs of Alzheimer’s, but also detect subtle linguistic patterns previously overlooked.
The team trained the algorithm using texts produced by both healthy subjects and known Alzheimer’s sufferers as they described a drawing of children stealing cookies from a jar.
Using tools developed by Google, they converted each individual sentence into a unique numerical sequence, or vector, representing a specific point in a 512-dimensional space.
Such an approach allows even complex sentences to be assigned a concrete numerical value, making it easier to analyze structural and thematic relationships between sentences.
By using those vectors along with handcrafted features—those that subject matter experts have identified—the A.I. system gradually learned to spot similarities and differences between sentences spoken by healthy or unhealthy people, and thus to determine with remarkable accuracy how likely any given text was to have been produced by an Alzheimer’s sufferer.
The system can also easily incorporate new criteria that may be identified by other research teams in the future, so it will only get more accurate over time.
In theory, A.I. systems could one day diagnose Alzheimer’s based on any text, from a personal email to a social media post.
One author of the study is K.P. Subbalakshmi, the founding director of Stevens Institute of Artificial Intelligence.
The study was presented at the 19th International Workshop on Data Mining in Bioinformatics at BioKDD.
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