A new AI model could help radiologists identify brain abnormalities in MRI scans for neurological conditions such as stroke, multiple sclerosis and brain tumours, in a new study from researchers at UCL and King's College London.
The study, published in Radiology AI, shows the potential of AI to help to clear waiting times in neurology departments due to a shortage of radiologists, as well as increasing demand for MRI scans that are vital for diagnosing and monitoring a range of brain conditions including tumours, strokes and aneurysms.
The AI model could help to ease the pressure on radiology departments by triaging cases according to their likely severity and decreasing the time it takes to return results. To test its capabilities, the model was first asked to distinguish between 'normal' and 'abnormal' scans, which it did accurately (getting it right approximately 19 out of 20 times) when compared to assessments made by expert radiologists.
It was then tested on specific conditions (using new MRI scans which weren't included in the training data) such as stroke, multiple sclerosis and brain tumours, and was able to recognise these conditions accurately (getting it right approximately nine out of 10 times).
Most AI models are currently built with large datasets that have been manually labelled by expert radiologists, which are expensive and time-consuming to produce.
