September 20, 2024
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London, England, United Kingdom, United Kingdom
September 20, 2024
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The Moorfields Electrophysiology Department plays a central role in both research and clinical activity. The Department performs a number of different electrodiagnostic tests on patients, including Electroretinograms (ERGs). ERGs record the electrical signals produced by the retina in response to light stimuli and are used routinely to objectively assess and monitor retinal function in a wide range of inherited and acquired retinal disorders. ERGs are notoriously difficult to interpret, thus far eluding routine automated analysis and requiring experienced scientists/clinicians for accurate evaluation. Advances in clinically applicable artificial intelligence (AI) and digital access to medical databases offer the possibly of using AI and in particular Machine Learning (ML) for ERG interpretation.
The Department has already initiated ML analysis of ERG data, involving a multidisciplinary team and collaboration with University College London (UCL). The present part-time role will extend research work in this field, involving extraction and interrogation of ERG legacy data, and determine the potential value and applicability of AI to ERG interpretation and examine the possibility of providing new diagnostic insights.
The successful applicant will work in collaboration with Moorfields and UCL collaborators to develop novel automated analyses of a large and detailed clinical ERG dataset. They will have the task to generate an AI tool capable of automated bulk extraction and ML interrogation of ERG data. They will also develop an AI-based decision support tool using algorithms with embedded safety features, for diagnostic and deep phenotyping purposes, using supervised and unsupervised AI methods. The project is expected to lead to the development of a prototype for future research and real-life clinical applications.
At Moorfields, we provide more than just an excellent career and great colleagues to work with. We also offer:
For further details / informal visits contact: Name: Dr Antonio Calcagni Job title: Consultant Electrophysiologist Email address: a.calcagni@nhs.net Telephone number: 020 7566 2120
Dr Anthony Robson (Head of Elkectrophysiology)
anthony.robson3@nhs.net
020 7566 2120
The Department has already initiated ML analysis of ERG data, involving a multidisciplinary team and collaboration with University College London (UCL). The present part-time role will extend research work in this field, involving extraction and interrogation of ERG legacy data, and determine the potential value and applicability of AI to ERG interpretation and examine the possibility of providing new diagnostic insights.
The successful applicant will work in collaboration with Moorfields and UCL collaborators to develop novel automated analyses of a large and detailed clinical ERG dataset. They will have the task to generate an AI tool capable of automated bulk extraction and ML interrogation of ERG data. They will also develop an AI-based decision support tool using algorithms with embedded safety features, for diagnostic and deep phenotyping purposes, using supervised and unsupervised AI methods. The project is expected to lead to the development of a prototype for future research and real-life clinical applications.
At Moorfields, we provide more than just an excellent career and great colleagues to work with. We also offer:
- Salary including High-Cost Area Supplement
- Opportunity to join the NHS Pension Scheme
- Free 24/7 independent counselling service
- Learning and development opportunities
- Easy and quick transport links
- A range of attractive benefits and discounts
- Access to Blue Light Card and other NHS Discount Schemes
- Free Pilates classes
- Full support and training to develop your skills
- Flexible working friendly organisation
For further details / informal visits contact: Name: Dr Antonio Calcagni Job title: Consultant Electrophysiologist Email address: a.calcagni@nhs.net Telephone number: 020 7566 2120
Dr Anthony Robson (Head of Elkectrophysiology)
anthony.robson3@nhs.net
020 7566 2120
