Title: Development of AI Techniques to Predict Eye Cancer Using Big Longitudinal Data
Funder: Department of Health and Social Care

Abstract: Choroidal naevi are common pigmented fundus lesions (8% of the population) with a very low risk of conversion into choroidal melanoma. The increasing use of wide-angle fundus examinations by High Street Opticians and the fear of litigation if a melanoma is missed, has led to a rising number of referrals of pigmented fundus lesions to Ophthalmology and Ocular Oncology Units. This places a significant amount of pressure on Ophthalmology Departments in the country regarding new patient and follow-up appointments. In order to address this clinical need, this project brings together an interdisciplinary team with distinct, complementary expertise in AI, medical image computing, and clinical medicine. The overarching aim is to develop and evaluate a novel, fully-automatic AI-powered diagnostic tool to support the accurate diagnosis and monitoring of choroidal naevi and to predict the risk of ocular melanoma. This research will harness (1) a unique database of ocular tumours, the largest of its kind worldwide, with a vast enriched dataset of images and clinical data of over 3000 patients, and (2) novel AI prognostic models that have been developed in Liverpool funded by EPSRC and NIHR. Our specific objectives are: (1) to complete curation of a dataset essential for the development of AI tools, (2) to adopt a software development approach to develop and rigorously validate new AI tools by following TRIPOD and PROBAST principles and (3) to deliver the project by proactive managements, public engagement and risk mitigation and develop a robust commercialisation strategy to market. We expect by completion of the project to have developed AI tools ready for future prospective clinical study and commercialisation. The project objective is well aligned with the NIHR AI funding strategy to advance translation of AI enabled healthcare technologies into new diagnostic tools to increase patient benefit and contribute economically to UK PLC.

Lay abstract: We will use artificial intelligence (AI) to assess and monitor lesions in the back of the eye. Choroidal naevi are lesions visible only on detailed examination by an eye professional or retinal cameras. These benign lesions are comparable to moles of the skin, and usually do not cause any problems. Choroidal naevi can be found in 5% in the population over 50 years of age, but the majority are never detected. It may be assumed that several million people are affected by choroidal naevi in the UK, comparable to the number of patients with diabetes. Choroidal naevi can very rarely transform into a type of eye cancer called choroidal melanoma, potentially leading to a loss in vision, loss of the eye, and death in about 30% of patients. Only 1 in 8,000 naevi will transform into a melanoma. Because of this risk, lifelong examinations are recommended to monitor for progression. Due to the greatly increased use of retinal cameras by High Street Opticians, the number of detected naevi has risen sharply in recent years. This has led to significantly more referrals to secondary care ophthalmic services and specialist oncology services, involving expense, travel, and sometimes extreme anxiety on the patients side. Choroidal naevi can be classified by several features based on their appearance and size. Of the well-established six risk factors defining the risk for progression to a melanoma, four can be identified with modern retinal imaging methods available at modern High Street Optician practices. Over the past years, in Liverpool we have developed AI tools to identify patients requiring treatment for common retinal diseases, age-related macular degeneration and diabetic retinopathy, based on retinal images without the need for a medical examination. We have developed a novel AI tool that can provide accurate predictions on a large dataset of 4903 patients from a clinical study on age-related eye disease. Our research team is multidisciplinary, with firmly-embedded patient group representation. Our aim is to develop our existing AI tool to assess the risk factors for progression of a choroidal naevus into a melanoma and to monitor lesions for growth or change over time based on existing clinical datasets from our service. This new tool would be beneficial for assessment and monitoring in the community for low-risk lesions, and for follow-up of conditions with risk factors in secondary care. This will help to streamline the management of patients, reducing cost for the NHS, and unnecessary examinations, travel, and anxiety for patients. We will follow the principle of medical software development and utilise advice from experts in medical device development and commercialisation. The new tool could be used as either a standalone tool or add-on to existing software programmes.

Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES;7.3 MANAGEMENT AND DECISION MAKING
Health categories: CANCER AND NEOPLASMS