Development of a smart tool for the comprehensive assessment of the diabetic foot including diagnosis of poor circulation in people with diabetes (SMRTFLO)

Clinical Problem

Foot ulceration is a common and devastating complication of diabetes. Ulceration is associated with high rates of amputation and death [1], and costs the NHS >£1 billion annualy [2]. There are two important targets for improving outcomes:

1. Improving the diagnosis of peripheral arterial disease (PAD). PAD is the most important risk factor for ulceration. The most accurate test for its diagnosis is the assessment of visual Doppler arterial waveforms [3]. Waveform assessment has been shown to be cost-effective and its adoption may improve outcomes [4]. However, it is associated with significant inter-observer variation.  
2. Improving the quality of foot assessments by collecting and collating information regarding other risk factors for ulceration to better identify at risk patients. 

The solution

We aim to develop an application that incorportates a machine learning algorithm (current TRL 4) for the diagnosis of PAD from images of waveforms (92% sensitivity, 82% specificity); patent filed. The application will also allow for structured foot assessment findings to be recorded and reviewed. Thereby, seamlessly integrating this technology into the clinical workflow whilst streamlining the foot assessment.  

Development plan

The design will be informed by key stakeholders using qualitative methodology- national survey, observation of user workflow and focus group. These insights will be used by a UI/UX design expert to ensure that the user interface and journeys are optimised. The application will then be developed iteratively alongside concurrent user testing.

Future work

We will evaluate the application across a diabetic foot service. 

Team

Leading applicant: Pasha Normahani, MBBS BSc (Hons) MSc (Distinction) MRCS [5]

Academic PI: Mr Usman Jaffer