Lee, L. Y., et al. (2024).
EClinicalMedicine, 102725.
Background
Predicting dementia early has major implications for clinical management and patient outcomes. Yet, we still lack sensitive tools for stratifying patients early, resulting in patients being undiagnosed or wrongly diagnosed. Despite rapid expansion in machine learning models for dementia prediction, limited model interpretability and generalizability impede 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 low-cost (cognitive tests, structural MRI) patient data. To enhance scalability and generalizability to the clinic, we: 1) train the PPM with clinically-relevant predictors (cognitive tests, grey matter atrophy) that are common across research and clinical cohorts, 2) test PPM predictions with independent multicenter real-world data from memory clinics across countries (UK, Singapore).
Interpretation
Our results provide evidence for a robust and explainable clinical AI-guided marker for early dementia prediction that is validated against longitudinal, multicenter patient data across countries, and has strong potential for adoption in clinical practice.
Here is a summary and some thoughts:
Cambridge scientists have developed an AI tool capable of predicting with high accuracy whether individuals with early signs of dementia will remain stable or develop Alzheimer’s disease. This tool utilizes non-invasive, low-cost patient data such as cognitive tests and MRI scans to make its predictions, showing greater sensitivity than current diagnostic methods. The algorithm was able to correctly identify 82% of individuals who would develop Alzheimer’s and 81% of those who wouldn’t, surpassing standard clinical markers. This advancement could reduce the reliance on invasive and costly diagnostic tests and allow for early interventions, potentially improving treatment outcomes.
The machine learning model stratifies patients into three groups: those whose symptoms remain stable, those who progress slowly to Alzheimer’s, and those who progress rapidly. This stratification could help clinicians tailor treatments and closely monitor high-risk individuals. Validated with real-world data from memory clinics in the UK and Singapore, the tool demonstrates its applicability in clinical settings. The researchers aim to extend this model to other forms of dementia and incorporate additional data types, with the ultimate goal of providing precise diagnostic and treatment pathways, thereby accelerating the discovery of new treatments for dementia.
