New AI Tool Promises Faster, More Accurate Alzheimer’s Diagnosis

Researchers have developed a new artificial intelligence algorithm that can accurately diagnose Alzheimer’s by detecting subtle differences in language.

According to researchers at Stevens Institute of Technology in New Jersey, the algorithm promises to accurately diagnose Alzheimer’s without the need for expensive scans or in-person testing.

In fact, they say the software not only can diagnose Alzheimer’s, at negligible cost, with more than 95 percent accuracy, it is also capable of explaining its conclusions, allowing physicians to double check the accuracy of its diagnosis.

“This is a real breakthrough,” said the tool’s creator, K.P. Subbalakshmi, founding director of the Stevens Institute of Artificial Intelligence and a professor of electrical and computer engineering at the Charles V. Schaeffer School of Engineering. “We’re opening an exciting new field of research, and making it far easier to explain to patients why the A.I. came to the conclusion that it did, while diagnosing patients. This addresses the important question of trustability of A.I .systems in the medical field”

It has long been known that Alzheimer’s can affect a person’s use of language, according to the researchers. People with Alzheimer’s typically replace nouns with pronouns, such as 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 saying “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 — Subbalakshmi said she and her students were 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 research team trained the algorithm using texts produced by both healthy subjects and Alzheimer’s sufferers as they described a drawing of children stealing cookies from a jar. Using tools developed by Google, Subbalakshmi and her team 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, Subbalakshmi explained.

By using those vectors, along with handcrafted features that experts identified, the A.I. system gradually learned to spot similarities and differences between sentences spoken by healthy people or those with Alzheimer’s. That led it to determine with “remarkable accuracy” how likely any given text was to have been produced by an Alzheimer’s sufferer, Subbalakshmi said.

The system also can easily incorporate new criteria that may be identified by other research teams in the future, so it will only get more accurate over time, she noted.

“We designed our system to be both modular and transparent,” Subbalakshmi explained. “If other researchers identify new markers of Alzheimer’s, we can simply plug those into our architecture to generate even better results.”

In theory, A.I. systems could one day diagnose Alzheimer’s based on any text, from a personal email to a social media post, she said.

First, though, an algorithm would need to be trained using many different kinds of texts produced by known Alzheimer’s sufferers, rather than just picture descriptions, and that kind of data isn’t yet available, she added.

“The algorithm itself is incredibly powerful,” Subbalakshmi said. “We’re only constrained by the data available to us.”

In coming months, Subbalakshmi hopes to gather new data that will allow her software to be used to diagnose patients based on speech in languages other than English. Her team is also exploring the ways that other neurological conditions, such as aphasia, stroke, traumatic brain injuries, and depression, can affect language use.

“This method is definitely generalizable to other diseases,” said Subbalakshmi. “As we acquire more and better data, we’ll be able to create streamlined, accurate diagnostic tools for many other illnesses too.”

Subbalakshmi and her doctorate students Mingxuan Chen and Ning Wang presented her work at the 19th International Workshop on Data Mining in Bioinformatics at BioKDD 2020.

Source: Stevens Institute of Technology

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