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 –
and predict a person’s likelihood of developing dementia. The development may
also pave the way for more personalised medicine.

 “This is the first
time that machine learning methods have been able to accurately measure a
marker of small vessel disease in patients presenting with stroke or memory
impairment who undergo CT scanning. Our technique is consistent and achieves
high accuracy relative to an MRI scan – the current gold standard technique for
diagnosis. This could lead to better treatments and care for patients in
everyday practice,” Dr Paul Bentley, lead author and Clinical Lecturer at
Imperial College London, said.

Prof Joanna Wardlaw, Head of Neuroimaging Sciences at the
University of Edinburgh, added: “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 Bentley.

Although MRI can detect and measure SVD more sensitively, it
is not the most common method used due to scanner availability, and suitability
for emergency or older patients.

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 with the human eye. The importance of
the 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.”

Dr Bentley explains that this software could help influence
doctors decision-making in emergency neurological conditions and lead to more
personalised medicine. For example, in stroke, treatments such as ‘clot busting
medications’ can be quickly administered to unblock an artery. However, these
treatments can be hazardous by causing bleeding, which becomes more likely as
the amount of SVD increases. The software could be applied in future to
estimate the likely risk of haemorrhage in patients and doctors can decide on a
personal basis, along with other factors, whether to treat or not with clot
busters.  

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.

The study used historical data of 1082 CT scans of stroke
patients across 70 hospitals in the UK between 2000-2014, including cases from
the Third International Stroke Trial. The software identified and measured a
marker of SVD, and then gave a score indicating how severe the disease was
ranging from mild to severe. The researchers then compared the results to a
panel of expert doctors who estimated SVD severity from the same scans. The
level of agreement of the software with the experts was as good as agreements
between one expert and another.

Additionally, in 60 cases they obtained MRI and CT in the
same subjects, and used the MRI to estimate the exact amount of SVD. This
showed that the software is 85% accurate at predicting how severe SVD is.

Source: https://www.imperial.ac.uk/news/186108/artificial-intelligence-improves-stroke-dementia-diagnosis/

Reference: Bentley
P, et al. Rapid Automated Quantification of Cerebral Leukoaraiosis on CT
Images: A Multicenter Validation Study. Radiology.
Published online 16 May 2018.