The technology, described in the journal Radiology, 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, researchers said.
"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 Paul Bentley from Imperial College London.
"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," said Bentley.
"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," said Joanna Wardlaw, a professor at the University of Edinburgh.
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.
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.
"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," Bentley added.
"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 said.
Bentley said 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, researchers said.
Bentley also suggests that the software can help quantify the likelihood of patients developing dementia or immobility, due to slowly progressive SVD.
(This story has not been edited by Business Standard staff and is auto-generated from a syndicated feed.)