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Conventional dark-adaptometers are unsuitable as a mass screening tool due to their high cost, lack of easy portability, need of trained staff and a totally dark room to be operated, arbitrary testing procedures, associated time waste in clinic and patient burden to mention a few. Consequently, dark adaptometers are not routinely used as clinical tools for retinal diagnosis and monitoring despite the inherent benefits over other visual electrophysiology equipment such as the ERG system, whose cost and features may often be surplus to optometrists' requirements.
This trial will assess the dark-adaptometry testing performance of a novel light-emitting system by generating full dark-adaptation threshold functions in normal adult healthy volunteers.
The novel system has been proposed to overcome the issues associated with current instrumentation; it is semi-automatic and easy to use without the need of any skilled operator.
It is envisaged that this system could spread the practice of dark-adaptometry testing and its adoption by high-street optometrists. This will allow diagnosing a number of retinal pathologies more quickly and more reliably that, faced with an ageing population, represents a major asset to the Health Community and the NHS.
This trial will involve 20 healthy volunteers, distributed in equal number in 2 groups of 18-40 and 50-70 years old, respectively. Proven the good health and eye condition of the participants, one of their eyes will be randomly-allocated and undergo dark-adaptometry testing 3 times on separate days within 3 weeks.
Testing will clarify whether by using the novel system it is possible to reproduce state-of-the-art threshold measurements as good or better than those produced by commercially-available dark-adaptometers. Threshold measurements in the elderly will be compared with literature data adjusted to exclude aged crystalline lens and pupillary miosis contributions. Data variability and system usability will be also assessed.
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20 participants in 2 patient groups
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Rachel Williams, Prof; Alessandro Giuliano, PhD
Data sourced from clinicaltrials.gov
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