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Fracture assessment and diagnosis in urgent care typically involves triage in which a nurse, advanced clinical practitioner or doctor will do an initial assessment before requesting imaging. X-rays taken by a diagnostic radiographer are usually the first-line imaging approach for non-complex fractures. Multiple surgical and non-surgical treatment options are available depending on the type of fracture.
NICE's guideline on non-complex fractures recommends that a radiologist, radiographer or other trained reporter should review X-rays and provide a definitive report before the injured person is discharged (hot reporting). Clinical experts explained that in practice this is not always possible and reporting delays can occur ranging from days to weeks.
Missed fractures are reported to be the most common diagnostic error in the emergency department (Hussain et al. 2019). Missed or delayed diagnosis of fractures on radiographs is reported to occur in around 3% to 10% of cases (Kuo et al. 2022).
Artificial intelligence (AI) technologies that can help healthcare professionals detect fractures on X-ray images could improve the accuracy of fracture diagnoses in urgent care. This could help reduce:
the number of fractures that are missed before a radiologist or reporting radiographer reviews the X-rays
the number of people being recalled to hospital after radiology review
the risk of further injury or harm to people during the time between the initial interpretation and treatment decision in urgent care and the definitive radiology report
the number of unnecessary referrals to fracture clinics.
AI technologies may also improve consistency of X-ray interpretation when the ability of healthcare professionals to interpret X-rays may be reduced, for example, when they are tired, distracted or working outside normal hours.
The technologies included in this early value assessment are standalone software that use AI-derived algorithms to analyse X-ray images to detect fractures. They are intended to be used as decision aids for healthcare professionals interpreting the X-ray image. Some companies provide the software directly, whereas others provide it through multivendor platforms. The technologies use X-ray radiographs in digital imaging and communications in medicine (DICOM) format which are stored on the hospital's picture archiving and communications system (PACS). Images are then interpreted using proprietary AI-derived algorithms. The technologies included in this assessment are shown in table 1.
AI technology (manufacturer) | CE marking | Regions covered | Population | Other pathologies detected |
|---|---|---|---|---|
BoneView (Gleamer) | Class IIa | Appendicular skeleton, ribs and thoracic-lumbar spine | 2 years and over | Dislocations, effusions and bone lesions |
qMSK (Qure.ai) | Class IIb | Appendicular skeleton and ribs | Adults | – |
Rayvolve (AZmed) | Class IIa | Appendicular skeleton and ribs | Adults | Dislocations, joint effusions and chest pathologies (pneumothoraces, cardiomegaly, pleural effusions, pulmonary oedema, consolidation, nodules) |
RBfracture (Radiobotics) | Class IIa | Appendicular skeleton and ribs | 2 years and over | Effusion of the knee and elbow, and lipohaemarthrosis of the knee |
TechCare Alert (Milvue) | Class IIa | Appendicular skeleton and ribs | No age limit | Dislocations, elbow joint effusion, pleural effusion, pulmonary opacity, pulmonary nodules and pneumothorax |
The comparator is standard care for fracture assessment in which the urgent care healthcare professional interprets the X-ray radiograph without AI assistance.
The reference standard is based on the consultant radiologist or reporting radiographer interpretation and report.
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