Hazel Tang A science writer with data background and an interest in the current affair, culture, and arts; a no-med from an (almost) all-med family. Follow on Twitter.
A common cause of stroke and dementia, small vessel disease (SVD), can now be detected in brain CT scans using AI algorithms.
Scientists from Imperial College London (ICL), in the UK used machine learning (ML) to diagnose the neurological disease and say their technique will enable clinicians to deliver treatments more quickly in emergency settings.
Dr Paul Bentley, Clinical Senior Lecturer at ICL said: “This is the first time ML methods accurately measured a marker of SVD 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.”
Doctors currently diagnose SVD by looking for changes to white matter in the brain during CT or MRI scans, which are the preferred method but less widely available.
When looking at CT scans it is often difficult to decide where the edges of the disease are and so accurately estimate its severity.
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.”
The study, published in Radiology, took place at Charing Cross Hospital and used historical data of 1082 CT scans of stroke patients across 70 hospitals in the UK between 2000-2014.
Researchers at Imperial College found the level of agreement of the software with the experts was as good as agreements between one expert with another.
