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New Project Aims to Revolutionize Stroke Prediction

TL;DR

  • A new project funded by the Medical Research Council aims to predict strokes using AI models based on past brain scans and medical tests.
  • Experts from the University of Plymouth and other institutions collaborate to build a database of patient results to train AI for stroke prediction.
  • The project hopes to improve patient outcomes and save healthcare costs by identifying individuals at high risk of stroke.

A groundbreaking initiative funded by the Medical Research Council is set to transform stroke prevention strategies in the UK. The ABSTRACT project, spearheaded by experts from the University of Plymouth, University Hospitals Plymouth NHS Trust, and the University of Exeter, aims to enhance the predictive capabilities regarding an individual’s susceptibility to strokes. 

With strokes constituting a leading cause of death and disability, affecting approximately 100,000 individuals annually in the UK, this innovative endeavor holds significant promise.

Enhancing stroke prediction through artificial intelligence

With a staggering one in seven strokes deemed preventable, the ABSTRACT project seeks to leverage advanced technologies, particularly artificial intelligence (AI), to identify individuals at heightened risk of experiencing a stroke. 

By scrutinizing past brain scans and medical test results of stroke survivors, researchers aim to discern patterns indicative of elevated stroke risk. Subsequently, they plan to develop sophisticated AI models capable of forecasting an individual’s likelihood of suffering a stroke over the next decade.

Collaborative efforts for improved patient outcomes

The ABSTRACT project harnesses a multidisciplinary approach, bringing together health data science, radiology, neurology, and statistics experts. Collaborating closely with commercial providers of medical investigations, Express Diagnostics, and Ultracardiac, the project aims to construct a comprehensive database comprising MRI and CT brain scans, electrocardiograms, and echocardiograms. 

This database, bolstered by support from the national Sentinel Stroke National Audit Programme (SSNAP) and NIHR Applied Research Collaboration South West Peninsula (PenARC), will be the cornerstone for training AI models to predict stroke risk.

Transforming healthcare with cutting-edge technology

Dr. Stephen Mullin, Associate Professor in Neurology at the University of Plymouth and Principal Investigator of the ABSTRACT project underscores the transformative potential of this initiative. 

The project aims to revolutionize stroke prevention strategies by integrating state-of-the-art AI techniques, including explainability tools for discerning predictive factors. By identifying individuals at heightened risk of stroke, interventions can be implemented proactively, potentially saving lives and alleviating the burden on healthcare systems.

Disclaimer. The information provided is not trading advice. Cryptopolitan.com holds no liability for any investments made based on the information provided on this page. We strongly recommend independent research and/or consultation with a qualified professional before making any investment decisions.

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James Kinoti

A crypto enthusiast, James finds pleasure in sharing knowledge on fintech, cryptocurrency as well as blockchain and frontier technologies. The latest innovations in the crypto industry, crypto gaming, AI, blockchain technology, and other technologies are his preoccupation. His mission: be on track with transformative applications in various industries.

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