Cathedral Eye Clinic (CEC) and Aston University unite to develop digital aid for eye disease diagnosis.

The Company

Belfast-based CEC is an eye healthcare facility with a team of ophthalmologists that specialise in laser and lens replacement surgery, as well as the the management and treatment of cataracts, retinal conditions, glaucoma and oculoplastics. They also currently run a specialised dry eye clinic, which functions to both diagnose and treat Ocular Surface Conditions (OSC). The company has invested in the latest equipment for the management of eye conditions, offering patients a full range of tailored treatments including the ZEISS VisuMax SMILE laser system and Intense Pulsed Light Treatment (IPL) for dry eye.

The company owns proprietary eye care products, including Eye Piece and Eye Nutrients focused on the prevention and relief of OSC symptoms. 

The Challenge

Professor Moore, Clinical Director at CEC, had noted significant variation in clinical response to the multiple treatment modalities utilised within the Clinic to manage dry eye symptomology and considered whether artificial intelligence tools could potentially be used to better define and guide treatment strategies for this complex condition impacting all aspects of the ocular surface. 

While CEC has strong expertise in the management and treatment of a range of eye conditions, they lack the knowledge in the underlying science and statistical methods required to deliver a digital delivery support system that would enable clinical decisions to be made with reduced senior clinician involvement.

To explore the potential for the incorporation of AI into the dry eye clinic systems, CEC teamed up with experts from Aston University, with the aim to investigate both the methodology and implementation strategies for the utilisation of Artificial Intelligence to diagnose and manage dry eye pathology. 

The Solution

Aston University will provide a combination of state-of-the-art ocular surface clinical assessment methodology and Artificial Intelligence (AI) expertise to help CEC explore the clinical fields of both optometry and ophthalmology.

The partnership will develop a digital decision support system that will apply AI technology to patients’ clinical data. This will aid formulating diagnoses to eye diseases and provide information to clinicians to make better care plan recommendations and improve the quality of care that their patients receive.Overall, the project will improve the quality of care for patients through enhanced diagnoses for eye diseases while also creating a number of new jobs.

Key aspects that are being explored include the impact of ocular surface issues upon the refractive outcomes post laser or lens-based therapies and determining which variables predict success of combined therapies. The complexity of potential interacting variables can pose significant predictive diagnostic difficulty: for example,  if a mild tear film abnormality is combined with mild posterior blepharitis and combined with a hyperopic laser treatment, will this provide a suboptimal result, compared to a myopic patient with the same ocular surface issues? And, if so, what, if any, preoperative clinical management techniques can improve the clinical outcome?

These are the types of clinical queries that recurrently face clinicians and the project is exploring these clinical questions through careful collection of patient data and long-term proactive assessment using machine learning tools.

“We are really excited at the prospect of working with colleagues from Aston University to develop a better understanding of how patients with dry eye can be identified in a more effective manner. This will impact many patients and clinicians around the world and the findings could even be translated into other areas of detecting ocular pathology.” Professor Johnathan Moore, Clinical Director, CEC.

With this project, CEC expects to create a number of new jobs by 2025. The completed research will be used to enhance solutions the industry provides for the betterment of patients and the wider populace. As an outcome of the project, Aston plans to present its findings at conferences such as ‘Optometry Tomorrow’, ‘British Contact Lens Association Clinical Conference’ and the European Academy of Optometry and Optics. 

Team Aston

The Aston University team will be led by Dr Shehzad Naroo, whose research interests include instrumentation for the diagnosis and detection of eye disease. Dr Naroo also has specific knowledge surrounding studies on the diagnostics and treatment of dry eye disease helpful to the project.

He will be assisted by Dr Mark Dunne, a senior lecturer in Optometry at Aston University with an area of study in devising algorithms that use AI to predict the success of a certain therapy. Professor Sunil Shah, who specialises in laser eye surgery and the development of the LASEK surgical technique, will also work on the project. 

Professor Shah has a particular interest in Dry Eye and Artificial Intelligence in optometry. Dr Raquel Gil-Cazorla, a lecturer in Optometry who researches dry eye, contact lenses, laser refractive surgery and cataract and lens surgery, will also assist in the delivery of the project objectives. 

Dr Raquel Gil-Cazorla, a lecturer in Optometry who researches dry eye, contact lenses, laser refractive surgery and cataract and lens surgery will also assist in the delivery of the project objectives. 

‘A published review on artificial intelligence and its application in vision and eye care, written by Louis Catania and Ernst Nicolitz, has suggested that the biggest question for vision and eye care professionals should be “What can I do now to prepare and incorporate this evolving science into my practice and my patient care?“. The associate on this project will address that question by applying machine learning to the creation of a clinical decision support system that uses real evidence from a working clinic to deliver the best possible care to its patients. This task may seem a leap but current machine learning software allows its intuitive realisation with remarkable speed. This is perfect for clinicians who may not have advanced skills in statistical analysis and computer programming’. Mark Dunne, Senior Lecturer in Optometry, Aston University

Next steps

The KTP is expected to complete in June 2023 – look out for more updates by following us on Twitter and checking our webpage

For more information about Knowledge Transfer Partnerships or an informal chat, email or call 0121 204 4242.