Title: Pathpoint Detect - Utilising the ‘Concept Bottleneck’ theory to build interpretable clinical image decision support tools centred on commonly utilised medical concepts and features.
Funder: Department of Health and Social Care

Abstract: Background Skin conditions cost the NHS over £700 million and more than 3 million primary care hours each year. However, formal dermatology training is not mandated in specialist general practice training and frequently undergraduate dermatology training amounts to only 6 days resulting in a heavy burden for specialist secondary care services. Aims and Objectives To develop a novel prototype ‘transparent clinical decision support tool to aid clinical triage for skin conditions. Develop a prototype ‘counterfactual engine' that will allow clinicians to create synthetic data and ‘what-if’ scenarios that can be utilised for clinical training. Completion of this feasibility study will infer future development of a transparent clinical decision support tool for all medical photography applications. Work plan, Methods and Timelines Work plans will be managed with agile methodologies and Kanban sprints. The key technical deliverables over the 12 month project plan are: Data Preparation (1 month) - clean and prepare data for model training. Concept Bottleneck Model (2 months) - provide suggestions of detected clinical features (aka concepts) in an image, together with a degree of certainty. Clinician - Model Interaction (2 months) - a prototype interface where model input can be displayed to clinical users to offer combined decision making. Visual ‘counterfactual explanations’ (5 months) - using a deep convolutional neural network decoder that will generate new skin lesion images directly from the interpretable dermoscopic features. Final analysis (2 months), acceptability testing (1 month) and intellect property analysis (2 months) will also be conducted. Anticipated Impact and Dissemination All results will be published in open-access journals in machine learning and healthcare literature and reported in accordance with the TRIPOD statement for model development. This feasibility study would inform planning of future clinical efficacy studies. Overall, the technology could deliver up to £85 million in savings across the NHS.

Lay abstract: Background to the project: Open Medical has delivered innovative workflow solutions to NHS clinicians since our first digital trauma planner was launched in 2012. We focus on care pathways with a visual element, such as wounds and lesions. During this time our service has gathered >45,000 images, with consent, referred by GPs, nurses or patients themselves into a consultant dermatologist s workflow. Each image forms just one part of a comprehensive digital referral form which also includes personal information, medical, social and drug history, and -crucially- the consultant s diagnostic and treatment decision. Aims of the project: Pathpoint Detect represents a step change in our system s triage (prioritisation) capability. We seek funding to develop a novel machine learning algorithm: a self learning clinical decision support tool to bring further operational support to every NHS dermatology unit. A fully integrated workflow and algorithmic system would allow healthcare professionals to provide the best available care from any location, at any time, in a governed, cost-effective and sustainable manner. Work plan: Pathpoint Detect will be developed to suggest image-based diagnostic features, together with a scale of certainty, providing the clinician end-user with a measure of reliability of the suggestion. A continuous 'self-validating' cycle, with ongoing clinical input based on our existing integrated clinical workflow service, will ensure that the platform continues to learn, adapt and improve in a real-world setting. The construction of the algorithm around concepts (visible features) that have been specified by clinicians as part of their diagnostic process ensures applicability to current clinical practice. Transparency allows users to trust certainty, while the user s decision adds to the system s understanding. This will be assessed with user (clinician) and patient acceptability testing as part of the feasibility study. Patient and public involvement: Decision-making for the inclusion of under-served groups forms a significant part of the project. The incidence of skin cancer increases with age and is highest among those aged 85-89 years old. It is well known in the field of dermatology that there is poor medical education regarding presentations of various skin in people of colour. A project steering committee and focus group will help us to address these important issues. Dissemination and anticipated outcomes: Should the feasibility study prove successful, Pathpoint Detect would have a rapid route to market. In the first instance, this tool would add an additional capability to our existing platforms that are already used in over 40 hospitals within the NHS and are processing more than 50,000 patient pathways per month. Our large NHS client base may allow us to rapidly conduct validation studies in a partnership arrangement with an NHS organisation. Our extensive market access may allow swift uptake of the product upon validation.

Research activity codes: 7.3 MANAGEMENT AND DECISION MAKING
Health categories: SKIN