Title: Deep learning for effective triaging of skin disease in the NHS
Funder: Department of Health and Social Care

Abstract: Resource within primary and secondary NHS care (dermatology and plastic surgery) cannot meet the growing demand from increasing numbers of referrals for lesions suspicion of skin cancer, estimated at 100,000 p.a. for NHS Scotland. Although skin cancer prevalence has increased by 5% per annum for the last quarter century, most referrals requesting a specialist opinion are benign lesions. Image-based AI assisting human experts has huge potential to support effective diagnostic triage by GPs, dramatically reducing referrals to secondary care whilst delivering safe care nearer to home and rapidly alleviating patient anxiety. To develop an AI system operating robustly and reliably within NHS referral structures, it is essential to train the system using images representative of those produced in primary care or by patients. Our pilot project developed software able to distinguish benign skin lesions from common skin cancers using preselected and pre-processed image datasets, with clear and limited categories, but these are not truly representative of the distribution of NHS clinical data. We propose to develop a tool of high diagnostic accuracy within a real-world NHS setting. Our primary aim, preventing unnecessary referral of benign skin lesions, requires a robust system with near-zero false negative rate. This 12-month project will prepare clinical image datasets and use them to develop and validate a prototype designed for integration into existing clinical workflow. This will incorporate deep learning ensembles optimised for high sensitivity with mechanisms to reject low confidence images and provide visual explanations. We will disseminate technology development and pave the way for subsequent development and integration within the whole NHS, allowing more patients immediate reassurance, and reducing attendance at GPs where one fifth of all appointments are for skin disease. When achieved, this is predicted to reduce dermatology and plastic surgery workloads by 20%, saving &pound;10M per annum for NHS Scotland.

Lay abstract: The problem to be addressed: Skin conditions are extremely common with 1 in 2 of the population affected each year. The health burden on primary care is large with 1 in 5 GP consultations being for skin disease. In addition to GP costs, around &pound;100M per annum in NHS Scotland is spent on secondary care services treating skin disease. Increasing demand for dermatological care outstrips current capacity, exacerbated by vacancies across Primary and Secondary Care. Skin cancer is the commonest human cancer with melanoma the commonest cancer killer in young adults in Scotland. Diagnosis and management of suspected skin cancer represents over 50% of a specialist dermatology service workload with primary care practitioners, often untrained in skin cancer recognition, lacking the necessary experience to distinguish innocent from cancerous lesions. Skin cancer in the UK is increasing in frequency by 5% per year and there is a pressing need to concentrate resources in this area of disease. Why this research is important: Skin disease naturally lends itself to automated image analysis. Lesions can be photographed easily and saved in digital form; NHS Tayside has 20 years of experience of large-scale digital capture of images in primary care, with assessment by secondary care dermatologists providing fast remote diagnosis, resulting in fewer appointments and improved patient satisfaction. Patients are delighted to be reassured about benign lesions, GPs enjoy immediate education from such a system, and fewer patients need to attend hospital appointments. What we aim to achieve: Using our long experience in this area, we now wish to deploy the rich potential of artificial intelligence (AI) systems to assist skin specialists. In a pilot project, funded by Detect Cancer Early, we have developed an AI (deep learning) system able to distinguish many common benign lesions from common skin cancers with state-of-the-art accuracy. However, our preliminary work has used selected and pre-processed image datasets from international research challenges that are not representative of routine NHS data. How we will achieve this: We will develop our technology to achieve diagnostic accuracy with image data from NHS clinics. This next phase will start the integration of a learning AI system into everyday NHS clinical care to refine clinical capability in a real-world setting. Our interdisciplinary group is uniquely positioned to exploit and develop this technology for NHS benefit. Success in this area will be gradual, starting with simple goals such as clinical decision support for the most common benign lesions, but with the potential to become a diagnostic tool for individuals taking their own photos of skin, uploading to a central deep learning tool, and receiving immediate, reliable diagnosis for one of the many thousands of types of skin disease.

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