Scientific American 201905

20 Scientific American, May 2019

SCIENCE SOURCE

ADVANCES

HEALTH TECH

Alzheimer’s AI

Algorithm predicts eventual Alzheimer’s diagnoses from brain scans

An estimated 5.7 million people in the U.S. have Alzheimer’s disease—the most common type of dementia—and that num- ber is expected to more than double by

Early diagnosis is crucial for patients
to benefit from the few therapies available.
But no single assay or scan can deliver a
conclusive diagnosis while a person is alive;
instead doctors have to conduct numerous
clinical and neuropsychological tests. So
there is growing interest in developing
artificial intelligence to identify Alzheimer’s
based on brain imaging.
Researchers at the University of Califor-
nia, San Francisco, have now successfully
trained an AI algorithm to recognize one of
the early signs of Alzheimer’s—a reduction
in the brain’s glucose consumption—in posi-
tron emission tomography (PET) imaging.
The algorithm accurately predicted an
eventual Alzheimer’s diagnosis in nearly all
the test cases, according to the study.
In PET imaging, trace amounts of a
radioactive compound are ingested or
injected into the body, producing three-
dimensional images of metabolism, circu-
lation and other cellular activity. PET is
well suited for an AI diagnostic tool be
cause Alzheimer’s causes subtle changes
in the brain’s metabolism that begin years
before neural tissue starts to degrade,
says study co-author Jae Ho Sohn, a radi-
ologist at U.C.S.F. These changes are “very

hard for radiologists to pick up,” he notes. The algorithm was trained and tested on 2,100 PET brain images from about 1,000 people 55 years and older. The images came from a 12-year study that tracked people who would ultimately be diagnosed with Alzheimer’s, as well as those with mild memory declines and healthy control sub- jects. The algorithm was trained on 90 percent of the data and tested on the remaining 10 percent. It was then retested on a second, independent data set from 40 patients monitored for 10 years. The algorithm was highly sensitive and was able to recognize 81 percent of the patients in the first test group and 100 percent in the second who would be diagnosed with Alzheimer’s six years later, on average. The findings were published in February in Radiology. The algorithm is based on “deep learning,” a machine-learning technique that uses artificial neural networks programmed to learn from examples. “This is one of the first promising, preliminary applications of deep learning to the diagnosis of Alzheimer’s,” says Christian Salvatore, a physicist at Italy’s National Research Council, who was not involved in the study. “The model performs very well when identifying patients with mild or late” diagnoses, he says, but catch- ing it in the earliest stages “remains one of the most critical open issues in this field.” — Rod McCullom

PET scans of normal ( left ) and Alzheimer’s ( right ) brains.

20 Scientific American, May 2019

SCIENCE SOURCE

ADVANCES

HEALTH TECH

Alzheimer’s AI

Algorithm predicts eventual Alzheimer’s diagnoses from brain scans

An estimated 5.7 million people in the U.S. have Alzheimer’s disease—the most common type of dementia—and that num- ber is expected to more than double by

Early diagnosis is crucial for patients
to benefit from the few therapies available.
But no single assay or scan can deliver a
conclusive diagnosis while a person is alive;
instead doctors have to conduct numerous
clinical and neuropsychological tests. So
there is growing interest in developing
artificial intelligence to identify Alzheimer’s
based on brain imaging.
Researchers at the University of Califor-
nia, San Francisco, have now successfully
trained an AI algorithm to recognize one of
the early signs of Alzheimer’s—a reduction
in the brain’s glucose consumption—in posi-
tron emission tomography (PET) imaging.
The algorithm accurately predicted an
eventual Alzheimer’s diagnosis in nearly all
the test cases, according to the study.
In PET imaging, trace amounts of a
radioactive compound are ingested or
injected into the body, producing three-
dimensional images of metabolism, circu-
lation and other cellular activity. PET is
well suited for an AI diagnostic tool be
cause Alzheimer’s causes subtle changes
in the brain’s metabolism that begin years
before neural tissue starts to degrade,
says study co-author Jae Ho Sohn, a radi-
ologist at U.C.S.F. These changes are “very

hard for radiologists to pick up,” he notes. The algorithm was trained and tested on 2,100 PET brain images from about 1,000 people 55 years and older. The images came from a 12-year study that tracked people who would ultimately be diagnosed with Alzheimer’s, as well as those with mild memory declines and healthy control sub- jects. The algorithm was trained on 90 percent of the data and tested on the remaining 10 percent. It was then retested on a second, independent data set from 40 patients monitored for 10 years. The algorithm was highly sensitive and was able to recognize 81 percent of the patients in the first test group and 100 percent in the second who would be diagnosed with Alzheimer’s six years later, on average. The findings were published in February in Radiology. The algorithm is based on “deep learning,” a machine-learning technique that uses artificial neural networks programmed to learn from examples. “This is one of the first promising, preliminary applications of deep learning to the diagnosis of Alzheimer’s,” says Christian Salvatore, a physicist at Italy’s National Research Council, who was not involved in the study. “The model performs very well when identifying patients with mild or late” diagnoses, he says, but catch- ing it in the earliest stages “remains one of the most critical open issues in this field.” — Rod McCullom

PET scans of normal ( left ) and Alzheimer’s ( right ) brains.

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Scientific American 201905

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