An Indian-origin researcher in the US has developed an artificial intelligence (AI) algorithm that promises to accurately diagnose Alzheimer's without the need for expensive scans or in-person testing.
The software not only can diagnose Alzheimer's with more than 95 percent accuracy but 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 KP Subbalakshmi from the Stevens Institute of Technology in the US.
Training the Algorithm
"We're opening an exciting new field of research, and making it far easier to explain to patients why the AI came to the conclusion that it did while diagnosing patients," she added. By designing an explainable AI engine that uses attention mechanisms and convolutional neural network, the team was able to develop software that could accurately identify well-known telltale signs of Alzheimer's.
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, Subbalakshmi and her team converted each individual sentence into a unique numerical sequence, or vector, representing a specific point in a 512-dimensional space.
Transparent and Modular
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, the AI system gradually learned to spot similarities and differences between sentences spoken by healthy or unhealthy subjects.
"This is absolutely state-of-the-art. Our AI software is the most accurate diagnostic tool currently available while also being explainable," Subbalakshmi said. 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. "We designed our system to be both modular and transparent," Subbalakshmi said
"If other researchers identify new markers of Alzheimer's, we can simply plug those into our architecture to generate even better results," she added. The study was presented at the 19th International Workshop on Data Mining in Bioinformatics at BioKDD.