ASPIRE™: Using Machine Learning to detect undiagnosed fractures in patients with osteoporosis

Impact: Technological impacts, Health and wellbeing


Osteoporosis weakens bones, increases risk of fractures and affects half of all people aged over 50. Vertebral fragility fractures (VFFs) are a common early manifestation, but those seen opportunistically on computed tomography (CT) images are often not reported by radiologists. At the University of Manchester (UoM) we have developed software to identify VFFs in CT images, combining it with oversight from radiologists to create ASPIRE™, a service that improves VFF identification. Implementation at NHS sites (2018-2019) analysed 9,797 patients and identified VFFs in 2,018 of them. Only 74 patients had been referred by hospital radiologists; ASPIRE™ referred 1,945, ensuring better management.
Impact date20182020
Category of impactTechnological impacts, Health and wellbeing
Impact levelEngagement

Research Beacons, Institutes and Platforms

  • Christabel Pankhurst Institute
  • Institute for Data Science and AI
  • Lydia Becker Institute