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Details of Grant 

EPSRC Reference: EP/Y002865/1
Title: NeuReader: Eye Tracking Enabled Explainable-AI for Empowering Resource Scarce Neurological Healthcare in Pakistan
Principal Investigator: Khan, Dr H
Other Investigators:
Shafait, Professor F Bajwa, Dr M
Researcher Co-Investigators:
Project Partners:
Life Care Clinic
Department: College of Engineering and Physical Sci
Organisation: Aston University
Scheme: Standard Research - NR1
Starts: 01 May 2024 Ends: 30 April 2026 Value (£): 190,154
EPSRC Research Topic Classifications:
Artificial Intelligence Computational Linguistics
Image & Vision Computing
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
02 May 2023 ODA ECR International Collaboration Grants Announced
Summary on Grant Application Form
Neurological disorders place a significant burden on the healthcare system of Pakistan where only a handful of trained neurologists are available to serve a population of 231 million people. More than 60% of Pakistan's population resides in rural areas where healthcare is provided via basic health units (BHU) and rural health centres (RHC) which are run by junior doctors or nursing staff. These facilities do not have the manpower or resources to provide any neurological care. The aim of project NeuReader is to develop a system that helps improve the provision of neurological care to patients in Pakistan. Neurological health will be monitored using electroencephalograms (EEG) which measure the brain's electrical activity via electrodes placed on the scalp. NeuReader will leverage Artificial Intelligence and Natural Language Processing to read EEG data, diagnose neurological disorders, and provide a report explaining the problem. Simple diagnostic decisions with no explanation are not very helpful as junior medical staff administering the tests at BHUs or RHCs would also need help to increase patient awareness about their health condition. A better understanding of the diagnosis will also allow patients and their families make informed decisions about travelling to hospitals in urban areas to seek further assistance since that entails substantial travel and lodging expenses. The explainability features of NeuReader will also help neurologists (connected remotely to the system) prioritise patients based on the gravity of their conditions. Building such a system requires overcoming several technical challenges. The performance of AI systems is highly dependent on the amount and quality of data available for training them. Two types of data will be used for training: (1) EEG recordings with doctor's reports summarising them in words (2) Locations of abnormalities spotted within an EEG recording. Doctor reports will be written by neurologists during data collection. Labelling of locations of abnormalities within EEG recordings is time consuming and laborious. An EEG recording may consist of several minutes/ hours of data with abnormalities lasting only a few seconds and spread out across different locations within the recording. To avoid investing hundreds of hours labelling EEG records, eye tracking will be used to record locations of neurologist gaze patterns on a computer screen as they examine EEG data in their routine practice. These eye gaze patterns will then be used to promptly generate labels of events saving hours of highly valuable neurologist time. The recorded labels will be used to train AI algorithms that can automatically spot events of interest which can then be used to generate a text report that can be used by junior doctors and nursing staff at BHUs or RHCs to assist patients suspected of suffering from neurological disorders. A significant time will be dedicated to field studies designed to assess the needs of patients and doctors who will be the end users of this systems. The learn outcomes of these field studies will be incorporated into the final design to maximise on ground impact.
Key Findings
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Potential use in non-academic contexts
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Date Materialised
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Organisation Website: http://www.aston.ac.uk