Theme 12. Translational Engineering at the Point of Care
Instrumentation and Measurements for Ophthalmology
Mario Ettore Giardini, University of Strathclyde, UK
Luigi Rovati, University of Modena and Reggio Emilia, Italy Emanuele Trucco, university of Dundee, UK
Iain AT Livingstone, NHS Forth Valley, UK and The Scottish Government UK Jamie Thomson, ISOP Scotland LtD, UK
Matteo Menolotto, Tyndall national Institute, Ireland
The eye is the only part of the body where it is possible to gain a non-invasive visual access to structures of the central nervous system and the vasculature. Furthermore, sight is a critical function for daily living. For this reason, eye diagnostics is a core element of clinical practice, and instrumentation, measurements, and protocols in modern ophthalmology are reaching an extraordinary level of sophistication and performance.
Advances are shifting ophthalmic diagnostics from secondary and tertiary care to the community and the field. Telemedicine approaches reach to the remotest areas allowing key diagnostics procedures without the burden of physical access to specialist facilities, and streamlining workflows. Advanced data processing be it through traditional or machine- learning-based algorithmic approaches, complements the core measurement functionalities by adding a layer of information retrieval towards automated forms of screening and/or diagnostics.
This special session aims to present advances in the broad range of research in the field of instrumentation, image processing, and system approaches in the field of ophthalmic diagnostics, and to give insight on the interaction between engineering and related clinical implications. Topics covered include ophthalmic instrumentation for research, secondary care, and community use, ophthalmic image analysis, and telemedicine.
Also of Interest…
The following sessions may be applicable to the theme Translational Engineering at the Point of Care
although they are primarily listed under a different theme.
|Session title||Primary theme|
|Signal Processing and Machine Learning Paradigms for Enabling the Digital Health Ecosystem||9|
|Biomedical Engineering Innovation Toolset – Exploration, Evaluation, and impact Generation: Using the HEALTH Purpose Launchpad||11|