Artifical intelligence approach may help detect Alzheimer's disease from routine brain imaging tests
The tool may help clinicians identify patients who would benefit from treatment

Date:
March 3, 2023
Source:
Massachusetts General Hospital
Summary:
Researchers have developed and validated a deep learning-based
method to detect Alzheimer's disease based on routinely collected
clinical brain images.


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FULL STORY ========================================================================== Although investigators have made strides in detecting signs of
Alzheimer's disease using high-quality brain imaging tests collected
as part of research studies, a team at Massachusetts General Hospital
(MGH) recently developed an accurate method for detection that relies
on routinely collected clinical brain images. The advance could lead to
more accurate diagnoses.


==========================================================================
For the study, which is published in PLOS ONE, Matthew Leming,
PhD, a research fellow at MGH's Center for Systems Biology and an
investigator at the Massachusetts Alzheimer's Disease Research Center,
and his colleagues used deep learning -- a type of machine learning
and artificial intelligence that uses large amounts of data and complex algorithms to train models.

In this case, the scientists developed a model for Alzheimer's disease detection based on data from brain magnetic resonance images (MRIs)
collected from patients with and without Alzheimer's disease who were
seen at MGH before 2019.

Next, the group tested the model across five datasets -- MGH post-2019,
Brigham and Women's Hospital pre- and post-2019, and outside systems pre-
and post-2019 -- to see if it could accurately detect Alzheimer's disease
based on real-world clinical data, regardless of hospital and time.

Overall, the research involved 11,103 images from 2,348 patients at risk
for Alzheimer's disease and 26,892 images from 8,456 patients without Alzheimer's disease. Across all five datasets, the model detected
Alzheimer's disease risk with 90.2% accuracy.

Among the main innovations of the work were its ability to
detect Alzheimer's disease regardless of other variables, such as
age. "Alzheimer's disease typically occurs in older adults, and so
deep learning models often have difficulty in detecting the rarer
early-onset cases," says Leming. "We addressed this by making the deep
learning model 'blind' to features of the brain that it finds to be
overly associated with the patient's listed age." Leming notes that
another common challenge in disease detection, especially in real-world settings, is dealing with data that are very different from the training
set. For instance, a deep learning model trained on MRIs from a scanner manufactured by General Electric may fail to recognize MRIs collected
on a scanner manufactured by Siemens.

The model used an uncertainty metric to determine whether patient data
were too different from what it had been trained on for it to be able
to make a successful prediction.

"This is one of the only studies that used routinely collected brain
MRIs to attempt to detect dementia. While a large number of deep
learning studies for Alzheimer's detection from brain MRIs have been
conducted, this study made substantial steps towards actually performing
this in real-world clinical settings as opposed to perfect laboratory settings," said Leming. "Our results -- with cross-site, cross-time, and cross-population generalizability -- make a strong case for clinical use
of this diagnostic technology." Additional co-authors include Sudeshna
Das, PhD and, Hyungsoon Im, PhD.

This work was supported by the National Institutes of Health and by the Technology Innovation Program funded by the Ministry of Trade, Industry
and Energy, Republic of Korea, managed through a subcontract to MGH.

* RELATED_TOPICS
o Health_&_Medicine
# Alzheimer's_Research # Healthy_Aging #
Diseases_and_Conditions # Today's_Healthcare
o Mind_&_Brain
# Alzheimer's # Disorders_and_Syndromes # Dementia #
Caregiving
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o Alzheimer's_disease o Dementia_with_Lewy_bodies o
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========================================================================== Story Source: Materials provided by Massachusetts_General_Hospital. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Matthew Leming, Sudeshna Das, Hyungsoon Im. Adversarial confound
regression and uncertainty measurements to classify heterogeneous
clinical MRI in Mass General Brigham. PLOS ONE, 2023; 18 (3):
e0277572 DOI: 10.1371/journal.pone.0277572 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2023/03/230303105255.htm

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