PT  - JOURNAL ARTICLE
AU  - Fontanella, Alessandro
AU  - Li, Wenwen
AU  - Mair, Grant
AU  - Antoniou, Antreas
AU  - Platt, Eleanor
AU  - Armitage, Paul
AU  - Trucco, Emanuele
AU  - Wardlaw, Joanna M
AU  - Storkey, Amos
TI  - Development of a deep learning method to identify acute ischaemic stroke lesions on brain CT
AID  - 10.1136/svn-2024-003372
DP  - 2024 Nov 20
TA  - Stroke and Vascular Neurology
PG  - svn-2024-003372
4099  - http://svn.bmj.com/content/early/2024/12/24/svn-2024-003372.short
4100  - http://svn.bmj.com/content/early/2024/12/24/svn-2024-003372.full
AB  - Background CT is commonly used to image patients with ischaemic stroke but radiologist interpretation may be delayed. Machine learning techniques can provide rapid automated CT assessment but are usually developed from annotated images which necessarily limits the size and representation of development data sets. We aimed to develop a deep learning (DL) method using CT brain scans that were labelled but not annotated for the presence of ischaemic lesions.Methods We designed a convolutional neural network-based DL algorithm to detect ischaemic lesions on CT. Our algorithm was trained using routinely acquired CT brain scans collected for a large multicentre international trial. These scans had previously been labelled by experts for acute and chronic appearances. We explored the impact of ischaemic lesion features, background brain appearances and timing of CT (baseline or 24–48 hour follow-up) on DL performance.Results From 5772 CT scans of 2347 patients (median age 82), 54% had visible ischaemic lesions according to experts. Our DL method achieved 72% accuracy in detecting ischaemic lesions. Detection was better for larger (80% accuracy) or multiple (87% accuracy for two, 100% for three or more) lesions and with follow-up scans (76% accuracy vs 67% at baseline). Chronic brain conditions reduced accuracy, particularly non-stroke lesions and old stroke lesions (32% and 31% error rates, respectively).Conclusion DL methods can be designed for ischaemic lesion detection on CT using the vast quantities of routinely collected brain scans without the need for lesion annotation. Ultimately, this should lead to more robust and widely applicable methods.Data are available in a public, open access repository. Data from the third International Stroke Trial (IST-3) is available at the following link: https://datashare.ed.ac.uk/handle/10283/1931.