Artificial Intelligence Improves Stroke and Dementia Diagnosis
London: Machine learning has detected one of the commonest causes of dementia and stroke, in the most widely used form of brain scan (CT), more accurately than current methods. New software, created by scientists at Imperial College London and the University of Edinburgh, has been able to identify and measure the severity of small vessel disease, one of the commonest causes of stroke and dementia. The study, published in Radiology, took place at Charing Cross Hospital, part of Imperial College Healthcare NHS Trust.
Researchers say that this technology can help clinicians to administer the best treatment to patients more quickly in emergency settings. This can also help in predicting the likelihood of dementia too. The development may also pave the way for more personalised medicine.
Professor Joanna Wardlaw, Head of Neuroimaging Sciences at the University of Edinburgh, said: "This is a first step in making a scan reading tool that could be useful in mining large routine scan datasets and, after more testing, might aid patient assessment at hospital admission with stroke."
Small vessel disease (SVD) is a very common neurological disease in older people that reduces blood flow to the deep white matter connections of the brain, damaging and eventually killing the brain cells. It causes stroke and dementia as well as mood disturbance. SVD increases with age but is accelerated by hypertension and diabetes.
At the moment, doctors diagnose SVD by looking for changes to white matter in the brain during MRI or CT scans. However, this relies on a doctor gauging from the scan how far the disease has spread. In CT scans it is often difficult to decide where the edges of the SVD are, making it difficult to estimate the severity of the disease, explains Dr Paul Bentley, lead author and Clinical Lecturer at Imperial College London.
Dr Bentley added: "Current methods to diagnose the disease through CT or MRI scans can be effective, but it can be difficult for doctors to diagnose the severity of the disease by the human eye. The importance of our new method is that it allows for precise and automated measurement of the disease. This also has applications for widespread diagnosis and monitoring of dementia, as well as for emergency decision-making in stroke."
He also suggests that the software can help quantify the likelihood of patients developing dementia or immobility, due to slowly progressive SVD. This would alert doctors to potentially reversible causes such as high blood pressure or diabetes.