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An intelligent imaging technology for automatic characterisation of the refractive power of human eye


Project Description

The project is supported by the University of Liverpool Doctoral Network in Future Digital Health, which is directed at creating and maintaining a community of AI health care professionals that can realise the benefits that AI can bring to Health Care. The vision is that of a world-class centre providing high-quality doctoral training within the domain of AI for Future Digital Health. Each available PhD project has been carefully co-created in collaboration with a health provider and/or a healthcare commercial interest so that the outcomes of the PhD research will be of immediate benefit. The network will be providing doctoral training, culminating in a PhD, in a collaborative environment that features, amongst other things, peer-to-peer and cohort-to-cohort based learning. On completion students will be well-placed to take up rewarding careers within the domain of AI and Digital Health.

Determining and analysing the shape and power of the cornea forms the basis for planning, undertaking and assessing many therapeutic and surgical effects such as the correction of corneal shape or refractive errors using laser vision correction (LVC). Theoretically, the most accurate way to obtain the net corneal power is to measure the posterior surface as well as the anterior surface curvatures at each location in each individual eye. Current methods that are used to measure corneal shape and power rely on interpreting the shape of reflected light. In these approaches assumptions are made that the cornea has a pre-defined shape and none use an actual reference point. This not only introduces significant approximation errors but limits the ability to accurately measure corneal shape needed for example, to improve laser correction of refractive errors of the eye. In addition, for the millions of subjects who have had such laser treatment and who then require inevitable cataract surgery, the change in shape of the cornea following such treatment introduces significant errors in determining the power of the replacement intra ocular lens. Our current inability to accurately measure such changes in corneal shape, lead to what is known as refractive surprises following cataract surgery and a disappointed patient. New technology and methods are therefore needed to accurately measure and locate corneal shape and power. This will also be of significant benefit in planning any corrective procedures to the cornea and also will facilitate the design and fitting of contact lenses which require a more precise knowledge of shape of the underling corneal surface.

This project aims to precisely and automatically quantify the corneal power by imaging and measuring both the posterior and the anterior surface curvatures using optical coherency tomography (OCT) newly developed in the University. Our novel OCT device allows the entire Bscan map to be obtained in a single-shot pattern thus minimises the artefacts associated with patient movement [1-3]. Inspired by our existing programs [4], new AI techniques will be developed for fast and accurate automated analysis of OCT images and in particular detection of both the anterior and posterior surfaces of the cornea [4]. The resultant detailed topography information can subsequently be used to determine and analyse refractive change for planning or assessing the impact of many therapeutic and surgical effects [5, 6]. These provide solid foundation upon which the student will develop a low-cost version of the device with a focus on measurement and image analysis automation.

The successful PhD candidate will benefit from working with a multidisciplinary team covering areas of electrical engineering, computer science, imaging technology, and medicine. All postgraduate students undertake the PGR Development Programme which aims to enhance their skills for a successful research experience and career. They are required to maintain an online record of their progress and record their personal and professional development throughout their research degree.

The successful candidate should have, or expect to have an Honours Degree at 2.1 or above (or equivalent) in Engineering, Computer Science, Physics, or related disciplines. It is highly desirable to have good background knowledge in optics, electrical engineering, machine learning, and computer programming plus a proactive approach to their work. Candidates whose first language is not English should have an IELTS score of 6.5 or equivalent.

For enquiries please contact Professor Yaochun Shen ( ) and Dr Yalin Zheng,

To apply please visit: https://www.liverpool.ac.uk/study/postgraduate-research/how-to-apply/

Funding Notes

This project is funded by the University of Liverpool Doctoral Network in Future Digital Health, successful students will receive a studentship of tuition fees paid at the Home/EU rate for 3.5 years and a stipend of £15,009 per annum for 3.5 years. In addition, students will have access to a research support fund of £1,000 per annum for purchasing equipment, consumables and conference costs co-managed by the academic supervisor. Applications from international students are welcomed, however suitable arrangements will need to be made for the difference between the Home/EU and international rate.

References

(1) Lawman S. et al.. Optics Express. 2016;24(11):12395-405.
(2) Lawman S. et al., Biomedical Optics Express. 2017;8(12):5579–93.
(3) Lawman S, et al., Appl. Sci. 2017;7(4):351.
(4) Williams D. et al., J. Biomed. Opt. 2013;18, 056003-1:7
(5) Kaye S., J Cataract Refract Surg, 2010;36, 665-670;
(6) Kaye S., Optom Vis Sci 2009;86(4):382-94l

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