A team of scientists from the University of Cambridge developed an advanced machine learning model to detect Alzheimer's with the help of artificial intelligence.
According to the research, led by Professor Zoe Kourtzi, the model outperforms current clinical diagnostic tools in predicting progression of the disease.
Professor Kourtzi said, "We've created a tool which, despite using only data from cognitive tests and MRI scans, is much more sensitive than current approaches at predicting whether someone will progress from mild symptoms to Alzheimer's - and if so, whether this progress will be fast or slow".
For the study, published in eClinical Medicine, researchers used non-invasive, and low-cost patient data - cognitive tests and structural MRI scans showing grey matter atrophy, from over 400 individuals who were part of a research cohort in the U.S.
Later, the model was tested using real-world patient data from a further 600 participants from the U.S. cohort, and longitudinal data from 900 people from UK and Singapore memory clinics.
The tool correctly identified those people who were developing Alzheimer's in 82 percent of cases, and those who didn't in 81 percent of cases from cognitive tests and an MRI scan alone, ensuring the accuracy of the algorithm at predicting the disease.
Professor Kourtzi emphasized, "AI models are only as good as the data they are trained on. To make sure ours has the potential to be adopted in a healthcare setting, we trained and tested it on routinely-collected data not just from research cohorts, but from patients in actual memory clinics. This shows it will be generalisable to a real-world setting."
The research team is planning to extend the tool to other forms of dementia, such as vascular dementia and frontotemporal dementia, by using various types of data, such as markers from blood tests.
Professor Kourtzi added, "If we're going to tackle the growing health challenge presented by dementia, we will need better tools for identifying and intervening at the earliest possible stage. Our vision is to scale up 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 new drug discovery for disease modifying treatments."
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