The Raynaud’s App: the story so far

Imagine a world where the power of Artificial Intelligence (A.I.) could be harnessed, helping clinicians identify Raynaud’s patients at risk of developing scleroderma sooner? Well, a new and exciting SRUK-led research project: ‘The Raynaud’s ResearchApp,’ is investigating whether this ‘sci-fi’ dream could become a reality.

Enabling the early detection of scleroderma is a key area of SRUK’s Research Strategy, since identifying those at risk can enable faster clinical intervention before the condition progresses further. This is also the long-term goal of The Raynaud’s App and Rare Disease Platform Research Project.

The idea for the project came about in 2018 when SRUK made a successful application to Microsoft’s ‘AI for Good’ Fund. An investment of £6,500 was received from Microsoft along with invaluable support through a partnership with D4T4, a company whose technical expertise provided the ‘know-how’ to develop this concept. The partnership generated the ‘Rare Disease Platform,’ a data repository which could gather and hold clinical information and patient-reported data (such as frequency and severity of attacks), to further our understanding of the progression of Raynaud’s and any early predictors of scleroderma. But how best to collect this data? The idea of a ‘Raynaud’s App’ was born. 

SRUK engaged  Healthbit, a small company with expertise in health apps, to develop a bespoke ‘Raynaud’s App’. Each person who signs up to the app records their own experiences throughout the week. On a personal level, the data they generate can be used to teach them about potential triggers for their Raynaud’s; or could be used to support discussions with their consultant. The app contains trackers like ‘Raynaud’s Attack,' ‘Raynaud’s Score,’ ‘Blood Pressure,’ ‘Heart Rate’ and ‘Mood.’ Additional information such as the attack trigger, timeframe and severity can be recorded to provide detail. Lastly, users can upload photos, or record journal entries. All this data is converted into charts that show changes in attack frequency or severity over time, specific to the individual user. This allows them to learn more about their Raynaud’s, any triggers which may set off attacks and the effect that this has on their wellbeing.

Version One of the app has been developed and piloted in the clinical study of people with secondary Raynaud’s: those with very severe Raynaud’s along with additional risk factors that mean they are at increased risk of developing scleroderma. Within the study, patient-reported data collected via the app is used to support clinical data such as images and blood sample analysis collected during clinical follow ups. These combined datasets will give researchers and clinicians a window into the various factors which underpin Raynaud’s and the early indicators of scleroderma. Over time as usage, datasets and knowledge expand, it is hoped that machine learning could be applied to these data to highlight those who may be in the early stages of scleroderma allowing them access to care and treatment before the condition has progressed.   

The potential success of this algorithm – powered through those who use the ResearchApp, will enable faster clinical intervention before the condition progresses further; hugely improving their outcomes.

We will continue to provide updates as this research progresses, and we hope to make The Raynaud's App widely available in the not-too-distant future.