AI tool improves diagnostic accuracy for dementia by 26%

Boston University researchers have developed an artificial intelligence tool designed to assist physicians in diagnosing the specific causes of cognitive decline. The findings were reported in Nature Medicine. While Alzheimer’s disease is the most recognized cause of dementia, it’s not the only one. The diagnostic challenge is compounded by the fact that multiple causes of dementia can occur simultaneously, making it difficult for physicians to arrive at a definitive diagnosis quickly. This delay often hampers timely intervention.

The researchers, led by Vijaya B. Kolachalama, PhD, an expert in using computational tools to aid in medical diagnoses, created an AI-driven platform capable of identifying up to 10 types of dementia, including vascular and frontotemporal dementia. This advanced tool integrates commonly collected patient data—such as medical history, medication use, demographic information, and scores from neurological and neuropsychological exams—with neuroimaging data like MRI scans. The AI then generates a prediction of the type of dementia a patient has, along with a confidence score, offering valuable insights to guide clinical decisions.

“Our goal is for AI to assist in identifying these disorders early, thereby enabling physicians to manage their patients more effectively and potentially prevent the diseases from worsening,” says Kolachalama, who serves as an associate professor of medicine and computer science at BU, in a statement.

The platform’s development represents a collaboration between BU researchers and external experts. Trained on data from over 50,000 individuals across nine global datasets, the AI tool has been rigorously tested, according to the researchers. In a study comparing neurologists working alone to those assisted by the AI, the tool improved diagnostic accuracy by 26%.

This AI tool is particularly valuable because it can function with limited data, which is crucial for health care providers in resource-constrained settings. Kolachalama said that in low-income regions, where MRI machines are less accessible, having a tool that can operate effectively with available clinical data is essential for expanding the reach of this technology.

Kolachalama also notes the increasing strain on health care systems due to a global shortage of neurology experts and a growing number of patients with neurological conditions. By enhancing diagnostic accuracy and efficiency, this AI tool has the potential to significantly alleviate the burden on physicians with limited time and resources.

Looking ahead, Kolachalama said he and his team are focused on bringing this AI platform into hospitals and clinics for real-world testing. The hope is that this technology will soon become an integral part of the diagnostic process, helping to improve outcomes for patients with dementia worldwide.

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Where a patient lives may be the biggest factor for a dementia diagnosis

A University of Michigan study found significant regional differences in the likelihood of receiving a dementia diagnosis in the United States, which could have profound implications for accessing new treatments for Alzheimer's disease and other forms of dementia. The research found that the percentage of people diagnosed with dementia each year varies widely across regions, with particularly stark differences for those aged 66 to 74 and individuals who are Black or Hispanic.

The study, published in Alzheimer's & Dementia: The Journal of the Alzheimer's Association, suggests that where a person lives may play a more significant role in whether they receive a dementia diagnosis than individual risk factors. According to the findings, someone in one region of the U.S. could be twice as likely to be diagnosed with dementia as someone in another region.

Julie Bynum, M.D., a U-M Health geriatrician and lead author of the study, emphasized the need to address these disparities. "These findings go beyond demographic and population-level differences in risk and indicate that there are health system-level differences that could be targeted and remediated," said Bynum in a statement. She noted that the variation in diagnosis rates could be due to differences in health care practices, patient knowledge, and care-seeking behaviors.

The study analyzed data from 4.8 million Medicare beneficiaries aged 66 and older in 2019, focusing on "diagnostic intensity" across 306 hospital referral regions (HRRs). Researchers found that while nearly 7 million Americans currently have a dementia diagnosis, many more likely have symptoms but remain undiagnosed. Access to advanced dementia treatments, including new medications and diagnostic tests, requires a formal diagnosis.

The study found that the prevalence of diagnosed dementia ranged from 4% to 14% across HRRs, with new diagnoses in 2019 ranging from 1.7% to 5.4%. After adjusting for various factors, including education level, smoking rates, obesity, and diabetes, researchers calculated that people in low-intensity areas were 28% less likely to be diagnosed with dementia, while those in high-intensity areas were 36% more likely.

The concentration of dementia diagnoses was highest in the southern U.S., but this pattern shifted once researchers accounted for other risk factors. Bynum suggested that the variation could stem from differences in clinical practices, such as how frequently primary care physicians screen for dementia or the availability of specialists.

Bynum called for increased efforts to ensure early identification of cognitive issues, especially in younger Medicare populations. She also encouraged individuals to advocate for themselves to receive cognitive screenings, which are covered by Medicare during annual wellness visits.

Bynum highlighted Medicare's recent GUIDE model for dementia care as a potential avenue for improving care coordination and access. This new model incentivizes clinical practices to provide better dementia care and offer 24/7 access to trained providers.

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Researchers Find New Method to Measure Cognitive Impairment, Dementia

This article, " Health-Deficit Accumulation Affects Risk for Mild Cognitive Impairment, Dementia," was originally published in NeurologyLive.

Using a frailty index score could enable clinicians to identify patients at risk for cognitive dysfunction, making it an important marker for prognostic value.

Newly published data suggests health-deficit accumulation, specifically among older Americans, affects the likelihood of progressive cognitive impairment, as well as the likelihood of cognitive improvement independent of the APOE ε4 allele.

Lead author David D. Ward, PhD, postdoctoral fellow, geriatric medicine research, Centre for Health Care of the Elderly, Nova Scotia Healthy Authority, and colleagues calculated a frailty index score using the deficit-accumulation approach in participants aged 50 years and older from the National Alzheimer’s Coordinating Center (NACC).

Among those not cognitively impaired (NCI; n = 9773), each 0.1 increment increase in score were associated with a higher risk of developing mild cognitive impairment (MCI) and a higher risk of developing dementia.

In total, there were 14,490 participants in the study with a mean age of 72.2 years. In the MCI subsample (n = 4717) at baseline, there was a higher degree of frailty that was associated with a lower probability of being reclassified as NCI from MCI, a higher risk of returning to MCI in those who were reclassified as NCI, and a higher risk of progressing to dementia.

"We conclude that frailty is a key risk factor for age-related cognitive dysfunction and dementia, representing both a target for interventions aimed at the prevention of age-related cognitive impairment and possible prognostic marker among those who have MCI,” the authors wrote.

The score is a health-state measure, incorporating information from multiple physiological systems, and closely reflects an individual’s risk for adverse health events and mortality independently of chronological age. A higher frailty index score indicated accumulation of more age-related health deficits while approximating biological age.

The researchers aimed to detail the dynamic nature of cognitive functioning by calculating the likelihood of transitions between cognitive states in both directions over a 12-month period. Decline of cognitive function was considered forward transition, whereas improvement of cognitive functioning was defined as backwards transition.

The investigators also assessed whether frailty index score and APOE ε4 allele carrier status exerted independent or interactive effects on cognitive-state transition probabilities.

They found no statistically significant interactions between these variables for any transition in the NCI subsample. However, in the MCI subsample, the association of the frailty index score and the risk of progressing to dementia was significantly weaker in those carrying an APOE ε4 allele than in non-carriers (interaction hazard risk [HR], 0.88; 95% CI, 0.80–0.97).

There were no meaningful differences in these associations when participants whose race was other than white were removed from the analytical sample. Notably, associations of the frailty index score with transition probabilities did not differ significantly between men and women.

Over 12 months, NCI subsample participants maintained their prior state 43,086 times (90.6%) and transitioned between states 4491 times (9.4%), 3086 (68.7%) of which were transitions between cognitive states, with 1405 (31.3%) transitions to death. Of the cognitive-state transitions in the NCI subsample, 80.9% were forward transitions, and 19.1% were backward transitions. In the MCI subsample, 70.5% were forward compared to 29.5% who experienced backwards transition.

"This work supports an emerging conceptualization of late-onset dementia as a complex outcome of aging that often is intimately related to an individual’s general health, as well as genetic risk factors,” the authors wrote.

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