Artificial intelligence-supported early fracture diagnosis: SBRI competition

  • Competition opens:Monday 20 May 2019
  • Registration closes: Wednesday 24 July 2019 12:00pm

Register and apply online using below link, copy into your browser

This is a Small Business Research Initiative (SBRI) competition funded by Opportunity North East and NHS Scotland. Successful applicants will receive 100% funding and have access to advice from NHS Grampian, NHS Greater Glasgow and Clyde (NHSGGC), the University of Aberdeen, the Canon Medical Research Europe and the funders.

The overall programme will be delivered in 2 phases. A decision to proceed with phase 2 will depend on the outcomes from phase 1. Only successful applicants from phase 1 will be able to apply to take part in phase 2.

NHS Scotland and Opportunity North East (ONE) are investing up to £240,000, including VAT, in innovative data analytics technology. The aim is to improve front-line clinical decision making and patient management in unscheduled care facilities.

The solution will improve clinical workflow and safety by optimising clinical decision making and management pathways. It must use artificial intelligence (AI) or machine learning algorithms to interpret data from upper limb (wrist or hand) and lower limb (ankle or foot) radiographs and linked text-based reports. Accurate determination of the presence or absence of a fracture in these areas has the potential to significantly improve patient care.

Phase 1 research and development contracts will be focused on feasibility studies. Phase 2 contracts will be prototype development and testing.

Funding type


Project size

The competition has 2 phases. Up to £100,000, including VAT, is allocated for phase 1 and up to £140,000, including VAT, for phase 2.

Who can apply

To lead a project, you can:

  • be an organisation of any size
  • work alone or with others from business, the research base or the third sector as subcontractors

Your project

Phase 1 projects must start by October 2019 and last up to 3 months.

It is anticipated that the feasibility study R&D contracts will be in the region of up to £20,000, including VAT. This is for each project for up to 3 months. We expect to fund up to 5 projects. The assessors will consider fair value in making their evaluation.

We would welcome bids that bring together a consortium of sector specialists.

In phase 1, you must:

  • demonstrate the technical feasibility of your proposed innovation
  • establish ongoing collaboration between technical and clinical members of the project team
  • formalise any required ethical approvals, data sharing agreements and contracts
  • begin working with clinical and imaging data

Phase 2 projects must last up to 9 months.

Only applicants who complete phase 1 can apply for funding to progress into phase 2. If your application is successful, you must:

  • develop and evaluate a prototype of your solution
  • test the prototype on real-world data and systems within NHS Grampian to establish clinical utility
  • develop a plan for full commercial exploitation


NHS Scotland and Opportunity North East have allocated up to £240,000, including VAT, to fund projects in this competition. There are 2 phases. Up to £100,000, including VAT, is allocated for phase 1 and up to £140,000, including VAT, for phase 2.

Applications must have at least 50% of the contract value attributed directly and exclusively for R&D services. R&D can cover solution exploration and design. It can also include prototyping and field-testing the product or service. R&D does not include:

  • commercial development activities such as quantity production
  • supply to establish commercial viability or to recover R&D costs
  • integration, customisation or incremental adaptations and improvements to existing products or processes

The total funding available for the competition can change. The funders have the right to:

  • adjust the provisional funding allocations between the phases
  • apply a ‘portfolio’ approach

Your proposal

The challenge

The challenge is to develop an AI or machine-based learning programme that can help healthcare organisations accurately identify whether a patient has a fracture. This is initially a classification problem (by assigning a value of yes, no or maybe).

In simple terms, the task to begin with is to develop an automatic system that, with a degree of certainty, can remove from clinicians’ workload those that are definitely yes or no, leaving them to focus on the more complex images. This is an initial step towards integrating AI systems into a mainstream clinical workflow within the NHS and could be a platform for building more intelligent learning systems.

Each year in Scotland, the NHS gives some 5,000 patients x-rays of the peripheral upper limb (wrist or hand) and lower limb (ankle or foot), most often looking for a fracture after trauma. Although isolated injuries in these areas are often categorised as ‘minor’, misdiagnosis and consequent mismanagement can result in significant morbidity and financial cost.

The interpretation of peripheral limb x-rays is the remit of a wide variety of clinical staff in many clinical settings, from large urban emergency departments to nurse-led remote cottage hospitals and minor injury units.

The diagnosis of a fracture in the wrist or ankle is made from 2 standard radiographic views taken at right angles to each other. Radiographic fracture assessment of the hand or foot may include a third oblique view.

Recently published studies have successfully used machine learning to analyse radiographs to detect fractures. They have shown the ability to perform at the same diagnostic standard as an expert.

AI or machine learning could be included in clinical workflows to interpret peripheral limb radiographs for the presence of fractures, which in most cases are not reported for several days. This would help:

  • improve diagnostic accuracy and treatment
  • improve patient pathways and outcomes
  • reduce the growing deficit between radiology reporting workloads and staffing levels

This competition draws on Scotland’s expertise in:

  • clinical and academic digital radiology
  • advanced data storage
  • data governance and access
  • interoperable healthcare databases

Example: in 2019

John Doe has a swollen right wrist after falling on an outstretched hand in the street. He lives in a rural location and attends his local minor injury unit where he is seen by a nurse who requests x-rays.

The films are placed on a digital archiving system but both the nurse and the radiographer are unsure if there is a fracture. Mr Doe is keen to get back to his activities, including driving. The staff decide to let him keep his wrist free but say they will contact him if an abnormality is found on the formal radiology report. After 4 weeks the formal radiology report shows there was a fracture. Mr Doe is recalled and given more x-rays. They show the bones have moved and he will need an operation.

The initial reading of the radiology image is made by a variety of grades of staff with wide experience. While local departments often have safeguard systems to minimise risk, some fractures have a delayed diagnosis. Peripheral limb injuries may have significant morbidity and often financial or lifestyle implications.

Example: in 2020

Following the application of machine learning, John Doe’s x-ray is displayed to the clinician with an augmented image highlighting the presence of a fracture or abnormality. The clinician can use this information alongside other clinical details and, if necessary, seek a specialist review.

If no fracture is found, patient management is simpler as the films do not have to go for formal radiology reporting.

This real-time system of augmentation has:

  • significantly and reliably improved the confidence the patient and the clinician have in the diagnosis of ‘no fracture’
  • reduced the number of specialist consultations for patients with a suspected fracture who do not have a fracture
  • sizeably and safely reduced radiology reporting, letting the department concentrate on more complex image interpretation

Your proposal

Successful applicants must use an available dataset of peripheral limb x-rays and linked text-based reports from the University of Aberdeen’s accredited secure Grampian Data Safe Haven (DaSH). With these they will develop AI algorithms to:

  • interpret the existing text-based report to categorise as fracture or no fracture
  • interpret the radiograph image to identify the presence of fracture
  • develop an AI product with the required level of real world accuracy to enhance to enhance radiology image interpretation in mainstream clinical practice

The competition is looking for proposals that:

  • improve peripheral limb fracture detection by non-radiology experts in out of hours environments within NHS Grampian
  • transform peripheral limb injury clinical pathways to improve patient outcomes and increase productivity by at least 20%
  • use the relevant NHS, academic and commercial expertise, data and infrastructure offered by Grampian
  • have clinical and commercial potential locally, nationally and globally

We are looking for industrial innovators. You must confidently collaborate and use multiple data sources to develop clinically relevant and commercially practicable solutions. There is potential to commercialise outputs directly through NHS Scotland and globally through the sales and marketing channels of Canon Medical.

Any adoption and implementation of a solution from this SBRI competition would be the subject of a separate, possible competitive, procurement exercise. This competition only covers R&D not the purchase of any solution.

The performance of models trained on the dataset that is made available during the programme will be validated against an unseen dataset. There will be a further dataset available to demonstrate generalisability.

Project types

In phase 1 you must work closely with the stakeholders to develop a solution. In phase 2 the outcome of your project will be a prototype of the solution.

Phase 1: technical feasibility studies

This means planned research or critical investigation to gain new knowledge and skills for developing new products, processes or services.

Phase 2: prototype development and evaluation

This can include prototyping, demonstrating, piloting, testing and validation of new or improved products, processes or services in environments representative of real-life operating conditions. The primary objective is to make further technical improvements on products, processes or services that are not substantially set.

20 May 2019 Competition opens 27 June 2019 Briefing event. Sign up by 21 June24 July 2019 12:00pmRegistration closes31 July 2019 12:00pmCompetition closes4 October 2019Feedback and phase 1 contracts awarded.

Published on: 14 May 2019