A machine learning model can more accurately estimate the age of acute ischemic brain lesions than current methods, with researchers predicting the software could mean up to 50% more stroke patients receive appropriate treatment.
The efficacy and appropriateness of stroke treatment depended on the progression stage or biological age of the lesion and whether it was deemed to be reversible, researchers wrote in the journal NPJ Digital Medicine.
‘Biological age is closely related to chronometric lesion age – i.e. time from symptom onset – although these ages disassociate due to variability in tissue vulnerability and arterial collateral supply,’ they said.
Acute ischemic lesions scanned with non-contrast computerised tomography (NCCT), become progressively hypoattenuated over time, the research team explained, a feature which helped to estimate biological lesion age.
At present, clinicians measured the relative intensity (RI) of a lesion on NCCT using a method termed Net Water Uptake (NWU).
However, the researchers noted this approach could be confounded by alternative sources of hypointensity, was also insensitive to additional ischemic features and dependant on lesion segmentation.
For this trial, researchers from Imperial College London and University of Edinburgh, UK, and the Technical University of Munich, Germany, developed a convolutional neural network – radiomics (CNN-R) model to optimise lesion age estimation from NCCT.
They noted that machine learning models had several advantages over current methods for stroke assessment such as the ability to screen high-dimensional imaging features for associations with ischemia progression, including those imperceptible to experts, as well as account for lesion anatomy variability and signal heterogeneity.
They trained the CNN-R model on chronometric lesion age, while validating against chronometric and biological lesion age in external datasets of almost 2,000 stroke patients.
Analysis showed the deep-learning model was approximately twice as accurate as NWU for estimating chronometric and biological ages of lesions.
‘The practical importance of our results lies in the CNN-R lesion age biomarker providing more accurate estimates, compared to current methods, of stroke onset time (unknown in ~20% of cases), and lesion reversibility, both currently used for decisions regarding revascularisation treatments,’ the researchers wrote.
As well as validating the method in a large, independent cohort, the researchers said they had demonstrated the technique could be embedded within a central pipeline of automated lesion segmentation and clinically-based expert selection.
Future research should assess whether the higher accuracy of a CNN-R approach to lesion age estimation carries over to predicting lesion reversibility and functional outcomes, they added.
Lead author Dr Adam Marcus, of Imperial College London’s Department of Brain Sciences, estimated up to 50% more stroke patients could be treated appropriately because of this machine learning method.
‘We aim to deploy our software in the NHS, possibly by integrating with existing artificial intelligence-analytic software that is already in use in hospital Trusts,’ he said.
Study senior author Dr Paul Bentley, of Imperial College London’s Department of Brain Sciences and consultant neurologist at Imperial College Healthcare NHS Trust, said the information would help clinicians make emergency decisions about stroke treatment.
‘Not only is our software twice as accurate at time-reading as current best practice, but it can be fully automated once a stroke becomes visible on a scan,’ he said.
The study follows research released last month showing artificial intelligence-enabled electrocardiography (ECG) can accurately predict an individual patient’s risk of future cardiovascular events as well as their short and long-term risk of dying.
Lead author of this study Dr Arunashis Sau, an academic clinical lecturer at Imperial College London’s National Heart and Lung Institute and cardiology registrar at Imperial College Healthcare NHS Trust, said compared with cardiologists the AI model could detect more subtle detail in the ECGs.
