Technology: Artificial Intelligence in the Fight Against AD
According to the World Health Organization (WHO), 50 million people around the world have dementia, and the condition strikes 10 million more each year. Alzheimer’s disease (AD), the leading form of dementia, accounts for 60% to 70% of all cases.
AD is the sixth leading cause of death in the United States, and it’s estimated that roughly 10% of the general population suffers from it. By 2050, the population of Americans older than age 60 will double. This demographic change will bring a drastic rise in the number of people living with AD, which will increase the burden on the health care system.
Since there’s no medical cure or treatment to reverse the progression of the illness, it’s paramount that health care institutions focus on early detection and subsequent treatment, which has been shown to slow the onset of dementia. Research on the first detectable signs demonstrates that the biomarkers predicting the disease may be spotted as long as 25 years before characteristic symptoms first appear. The challenge in providing early detection is finding cost-effective, noninvasive methods that can be applied to the general health care system.
Traditionally, physicians relied on medical history, tests, exams, and personal information to make a potential diagnosis of AD. Symptoms such as memory impairment, confusion, difficulty with communication, and poor reasoning are clear signs that neurons have already been damaged. However, by the time the obvious symptoms emerge, it’s too late to apply effective treatment because medication cannot reverse the damage to atrophied brain cells.
Early detection is a challenge, but a number of studies are underway, giving rise to promising innovations. Advancements in artificial intelligence (AI) are of particular interest because they offer the potential for timely treatment and early intervention based on a combination of machine-learning algorithms and neuroimaging technology. Through AI, millions of people could benefit from much-needed intervention protocols that can slow the destructive progression of the disease.
One of the main challenges with this kind of data assimilation is the variety of formats that can’t be easily merged, making it difficult to produce meaningful results. Data integrity is not only crucial in establishing credibility and accuracy but also an essential base for analytic systems.
In the AD field of study, some of these challenges have been overcome. Positive results have been obtained in algorithms that have compared specific biomarkers of the disease. A number of research initiatives have also shown positive results utilizing biomarkers and MRI, and genetics and clinical data merged into algorithms, with some displaying astounding accuracy with high-performing results. Specifically, since differences in glucose uptake are predictive of AD, creating a predictive algorithm that can spot glucose anomalies by merging positron emission tomography (PET) scans with machine learning makes early prediction a possibility.
Detection With PET Scans
In AD patients, a decreasing amount of glucose in brain cells is apparent when the cells become diseased and die. As glucose levels drop, PET scans are able to map this slow progression. In particular, brain-imaging scans called 18-F fluorodeoxyglucose PET scans, which are also used to identify some types of cancers, can read these low levels of glucose. With this information, and the application of machine-learning algorithms to PET scans, researchers can measure these molecules.
Applying the Algorithm
Since slowing early-stage AD is a race against the clock, these algorithms will give physicians a chance to treat the disease before irreversible, widespread brain atrophy has caused significant loss of brain volume. More studies are being developed to confirm the possibilities of diagnostics with AI biomarkers of AD, such as the abnormal buildup of proteins. The promising developments will compound the predictive power of AI.
Positive Net Benefits
By keeping patients at less severe stages of the disease for a considerably longer duration and using early therapy treatments such as cholinesterase inhibitors and other innovations, care costs and hospitalization will be greatly minimized.
AI can provide an early diagnostic tool with significant benefits that can improve outcomes for patients and their families as well as drastically lower the costs of health care. Applying the predictive power of algorithms is a crucial method that will enable doctors to provide proactive management and prepare for the inevitable growth of the baby boomer population while opening the door to massive savings for the US health care system.
— Dongmin Kim, PhD, is the chief technology officer/director of the AI R&D Center for JLK Inspection, a Seoul, South Korea–based medical solutions provider specializing in artificial intelligence (AI)-based technology. JLK Inspection’s universal AI platform is created by a combination of big data, experts, and its own unique engines and algorithms, providing onsite/real-time service, seamlessly connected to all systems.