Summary: A new AI tool predicts Alzheimer’s disease progression with 82% accuracy using cognitive tests and MRI scans, outperforming current methods. The tool could reduce the need for costly testing and improve early intervention.
Alzheimer’s disease is the leading cause of dementia, affecting more than 55 million people worldwide.
Highlights:
- The AI tool correctly identified Alzheimer’s disease progression 82% of the time.
- It uses non-invasive and inexpensive data for predictions.
- It can stratify patients into groups based on the rate of disease progression.
Source: University of Cambridge
Scientists at Cambridge have developed an artificial intelligence tool that can predict in four out of five cases whether people showing early signs of dementia will remain stable or develop Alzheimer’s disease.
The team says this new approach could reduce the need for invasive and costly diagnostic tests while improving treatment outcomes at an early stage, when interventions such as lifestyle changes or new drugs may have a chance of working optimally.
Dementia represents a major global public health challenge, affecting more than 55 million people worldwide, with an estimated annual cost of $820 billion. The number of cases is expected to nearly triple over the next 50 years.
The leading cause of dementia is Alzheimer’s disease, which accounts for 60-80% of cases. Early detection is essential, as this is when treatments are likely to be most effective. However, early diagnosis and prognosis of dementia may not be accurate without the use of invasive or expensive tests such as positron emission tomography (PET) scans or lumbar punctures, which are not available in all memory clinics.
As a result, up to a third of patients may be misdiagnosed and others diagnosed too late for treatment to be effective.
A team led by scientists from the University of Cambridge’s Department of Psychology has developed a machine learning model that can predict whether and how quickly a person with mild memory and thinking problems will progress to developing Alzheimer’s disease.
In research published in Electronic clinical medicineThey demonstrate that it is more accurate than current clinical diagnostic tools.
To build their model, the researchers used routinely collected, noninvasive, and inexpensive patient data (cognitive tests and structural MRI scans showing gray matter atrophy) from more than 400 people in a research cohort in the United States.
They then tested the model using real-life patient data from 600 other participants in the US cohort and, importantly, longitudinal data from 900 people from memory clinics in the UK and Singapore.
The algorithm was able to distinguish between people with mild, stable cognitive impairment and those who progressed to Alzheimer’s disease over a three-year period. It was able to correctly identify people who developed Alzheimer’s disease 82% of the time, and correctly identify those who did not develop Alzheimer’s disease 81% of the time, based on cognitive testing and MRI alone.
The algorithm was found to be three times more accurate in predicting Alzheimer’s disease progression than current standards of care, i.e. standard clinical markers (such as gray matter atrophy or cognitive scores) or clinical diagnosis. This shows that the model could significantly reduce diagnostic errors.
The model also allowed the researchers to stratify people with Alzheimer’s disease using data from each person’s first visit to the memory clinic into three groups: those whose symptoms would remain stable (about 50% of participants), those who would progress slowly to Alzheimer’s disease (about 35%), and those who would progress more quickly (the remaining 15%).
These predictions were validated by examining six-year follow-up data. These data are important because they could help identify people early enough to benefit from new treatments, while also identifying those who need close monitoring because their condition is likely to deteriorate rapidly.
It is important to note that the 50% of people who have symptoms such as memory loss but remain stable would be better placed on a different clinical pathway, as their symptoms may be due to other causes rather than dementia, such as anxiety or depression.
Lead author Professor Zoe Kourtzi from the University of Cambridge’s Department of Psychology said: “We have created a tool that, although it only uses data from cognitive tests and MRI scans, is much more sensitive than current approaches in predicting whether a person will progress from mild symptoms to Alzheimer’s disease – and if so, whether this progression will be rapid or slow.
“This could significantly improve patient wellbeing, by showing us which people need the closest care, while removing anxiety from patients we expect to remain stable. At a time when healthcare resources are under intense pressure, it will also help eliminate the need for unnecessary invasive and expensive diagnostic tests.”
While the researchers tested the algorithm on data from a research cohort, it was validated using independent data including nearly 900 people who attended memory clinics in the UK and Singapore.
In the UK, patients were recruited as part of the Quantitative MRI in NHS Memory Clinics (QMIN-MC) study led by study co-author Dr Timothy Rittman at Cambridge University Hospitals NHS Trust and Cambridgeshire and Peterborough NHS Foundation Trusts (CPFT).
The researchers say this shows that it should be applicable in a clinical setting and in real-world patients.
Dr Ben Underwood, Honorary Consultant Psychiatrist at CPFT and Assistant Professor in the Department of Psychiatry at the University of Cambridge, said: “Memory problems are common as we get older. In the clinic, I see how the uncertainty about whether these are the early signs of dementia can be a source of great concern for individuals and their families, and frustrating for doctors who would much rather give definitive answers.
“The fact that we can reduce this uncertainty with the information we already have is exciting and will likely become even more important as new treatments emerge.”
Professor Kourtzi said: “AI models are only as good as the data they are trained on. To ensure ours has the potential to be adopted in healthcare, we trained and tested it on routinely collected data, not only from research cohorts, but also from patients in real memory clinics. This shows that it will be generalisable to a real-world setting.”
The team now hopes to extend their model to other forms of dementia, such as vascular dementia and frontotemporal dementia, and using different types of data, such as markers from blood tests.
Professor Kourtzi added: “If we are to address the growing health challenge posed by dementia, we will need better tools to identify and intervene as early as possible.
“Our vision is to evolve our AI tool to help clinicians assign the right person at the right time to the right diagnostic and treatment pathway. Our tool can help match the right patients to clinical trials, accelerating the discovery of new drugs for disease-modifying treatments.”
About this news on AI research and Alzheimer’s disease
Author: Ben Underwood
Source: University of Cambridge
Contact: Ben Underwood – University of Cambridge
Picture: Image credited to Neuroscience News
Original research: Free access.
“Robust and interpretable AI-guided marker for early prediction of dementia in real-world clinical settings” by Ben Underwood et al. Electronic clinical medicine
Abstract
Robust and interpretable AI-guided marker for early prediction of dementia in real-world clinical settings
Background
Early prediction of dementia has major implications for clinical management and patient outcomes. Yet, we still lack sensitive tools to stratify patients early, resulting in some patients being missed or misdiagnosed. Despite the rapid expansion of machine learning models for dementia prediction, limited interpretability and generalizability of the models hamper translation to the clinic.
Methods
We build a robust and interpretable predictive prognostic model (PPM) and validate its clinical utility using real-world, routinely collected, non-invasive, and inexpensive patient data (cognitive testing, structural MRI). To improve scalability and generalizability to the clinic, we: 1) train the PPM with clinically relevant predictors (cognitive testing, gray matter atrophy) that are common across research and clinical cohorts, 2) test the PPM predictions with independent multicenter real-world data from memory clinics in different countries (UK, Singapore).
Results
PPM reliably predicts (accuracy: 81.66%, AUC: 0.84, sensitivity: 82.38%, specificity: 80.94%) whether patients in early stages of the disease (MCI) will remain stable or progress to Alzheimer’s disease (AD). PPM generalizes the research to real-world patient data in memory clinics and its predictions are validated against longitudinal clinical outcomes. PPM allows us to derive an individualized AI-guided multimodal marker (i.e., predictive prognostic index) that predicts progression to AD more accurately than standard clinical markers (gray matter atrophy, cognitive scores; PPM-derived marker: hazard ratio = 3.42, p = 0.01) or clinical diagnosis (PPM-derived marker: hazard ratio = 2.84, p
Interpretation
Our results provide evidence of a robust and explainable AI-guided clinical marker for early prediction of dementia, validated by longitudinal and multicenter patient data across countries, and which has strong potential for adoption in clinical practice.
Funding
Wellcome Trust, Royal Society, Alzheimer’s Research UK, Alzheimer’s Drug Discovery Foundation Diagnostics Accelerator, Alan Turing Institute.
News Source : neurosciencenews.com
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