This M.Sc. project will be based at the UCL Translational Imaging Group, and its goal is the improvement of a cost-effective ophthalmoscope for developing economies.
Given the extensive worldwide population suffering from a potentially blinding ophthalmic pathology, innovation on intraocular observation methods is imperative. In rural societies, patients that suffer from detectable and treatable diseases, such as cataracts or retinopathy of prematurity, would benefit from easily accessible digital ophthalmoscopes. Notably, 80% of blindness is preventable when detected early. Disruptions in the miniaturisation of lenses, electronics, and mechanical components, together with ubiquitous computing through microprocessors, can instigate developments in ophthalmoscopy and reshape a field substantially based on 20th century developments.
Retinal fundus imaging is a critical task in ophthalmoscopy, and, thus, the focus of recent device innovations. Examples of new approaches to fundoscopy mainly make use of smartphone technologies. Initial approaches in 2012 entailed observing the retina using a smartphone’s camera and a handheld indirect ophthalmoscopy lens. A limiting factor of smartphone-based approaches, however, is that they do not account for the expense of the device itself. Thus, the reported costs are misleading and may amount to the cost of a hand-held commercial digital ophthalmoscope. Furthermore, the fact that 90% of blind people live in low-income countries is not considered. Their location should be examined together with the lack of smartphone penetration, e.g., only 37% of population in China, and only 19% in Kenya owns a smartphone. Contrary, truly widespread ophthalmoscopic screening will make use of ubiquitous technology, such as miniaturised inexpensive computers like the Raspberry Pi.
This M.Sc. project is about building exactly such a device: an inexpensive ophthalmoscope that is controlled by tiny computers and embedded electronics. It will consist of state-of-the-art liquid lenses and high-definition cameras, all embedded in a hand-held device. The student will be able to leverage substantial existing progress on such a device, and will be encourage to contribute his own research ideas to shape the project.
The student will learn about state-of-the-art technologies in imaging, and will be able to design and create electronic components. He/She will get the opportunity to deliver computationally efficient code that runs on Raspberry Pi and Arduino, so that all image processing can be performed without the need of an external computer or an expensive smartphone.
This project is suitable for a student with an interest in optical technologies, electronics, and software.