Case Study Club Session 2
Case Study 2: ASPIRE™ Using Machine Learning to Detect Undiagnosed Fractures in Patients with Osteoporosis
The University of Manchester developed the ASPIRE™ software, utilising machine learning to enhance the detection of vertebral fragility fractures (VFFs) on CT imaging. During its implementation at NHS sites from 2018 to 2019, ASPIRE™™ identified VFFs in 2,018 out of 9,797 patients, significantly more than the 74 patients referred by radiologists. This resulted in nearly 2,000 additional referrals for further management, contributing to improved prevention and treatment of osteoporosis.
Why Case Study Club?
Understanding research impact case studies can be challenging, particularly when expectations differ across disciplines, institutions, and funding schemes. The RIA Case Study Club provides a structured, collegial space for understanding research impact case studies through peer discussion, shared examples, and guided reflection, helping participants build confidence in how impact is framed and evidenced in practice.
Writing or supporting an impact case study often happens without clear insight into how case studies are read and assessed in practice. RIA’s brand new Case Study Club was created to address this gap. Drawing on real impact case studies (e.g REF 2021), this monthly community forum gives you the opportunity to step into a reviewer mindset. We will discuss research impact narratives, discuss reach and significance, explore how case studies are interpreted, and understand what strengthens or weakens an impact narrative.