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UK atrial fibrillation research incorporates Brainomix

Research will analyse how Huawei’s wearable technology detects atrial fibrillation in post-stroke patients

Brainomix has revealed its participation in new research, sponsored by the University of Liverpool, which concentrates on post-stroke atrial fibrillation (AF).

The sites, which currently have clinical deployments of Brainomix’s ‘e-Stroke platform’, will now incorporate the AI system in order to gather real-time imaging data which can be securely transferred to central investigators.

The Liverpool-Huawei Stroke Study aims to establish the clinical effectiveness, acceptability and cost-effectiveness of Huawei Smartwear to detect AF in patients following an acute ischaemic stroke.

The ultimate aim is to establish detection of AF, thereby initiating earlier treatment and reducing risk of another stroke.

Oxford-based Brainomix is increasingly interested in this type of research activity across the UK. It is a regular provider of stroke AI imaging solutions across the NHS and is keen to support future trials of a similar design.

Dr George Harston, chief medical and innovation officer at Brainomix said: “We are pleased to be involved in the Liverpool-Huawei Stroke Study, where our e-Stroke platform can provide trial investigators with high-quality, prospectively processed descriptive baseline imaging data.”

Chief investigator, professor Gregory Lip, who is Price-Evans chair of cardiovascular medicine and director of the Liverpool Centre for Cardiovascular Science, is optimistic about the partnership: “We are delighted by this collaboration with Brainomix, which enhances our growing research portfolio into stroke and atrial fibrillation research, and would help inform clinical practice and improve our care and management of these patients.”

AF remains a common heart rhythm disorder which increases the risk of stroke five-fold and is estimated to affect 24% of post-stroke patients. Current monitoring and treatment combinations can be initiated but are often challenging and unreliable.

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