Scientists at Imperial College London and the University of Edinburgh in the UK have developed new AI software that can accurately detect and measure the severity of small vessel disease (SVD) from a CT scan.

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.

The researchers believe that their new 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,” said Clinical Lecturer at Imperial College London, Dr Paul Bentley.

“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,” continued Dr Bentley.

Head of Neuroimaging Sciences at the University of Edinburgh, Professor Joanna Wardlaw, 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.”

According to Dr Bentley, their 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.

Dr Bentley 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 software was recently the focus of a study published in Radiology. The study used historical data of 1,082 CT scans of stroke patients across 70 hospitals in the UK between 2000 and 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 with 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.

The team are now using similar methods to measure the amount of brain shrinkage and other types of conditions commonly diagnosed on brain CT.

The study was funded by a National Institute for Health Research i4i Invention for Innovation award, and a National Institute for Health Research Imperial Biomedical Research Centre grant (NIHR BRC).

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