The Institute of Medical and Biological Engineering Knee Dataset



P.G. Conaghan, J.H. Edwards, H.L. Fermor, J. Fisher, A. Herbert, E. Ingham, L.M. Jennings, A.C. Jones, M. Mengoni, R.K. Wilcox



This data collection was initiated through the EPSRC programme grant “Optimising knee therapies through improved population stratification and precision of the intervention” (EP/ P001076/1) led by Prof Ruth Wilcox at the Institute of Medical and Biological Engineering, University of Leeds.



The vision for that programme is that patients with knee pain receive the right treatment at the right time. In the UK, one third of people aged over 45 have sought treatment for osteoarthritis, and the disease costs the NHS over £5 billion per year. The knee is the most common site for osteoarthritis, with over four million sufferers in England alone.

The aging population with expectations of more active lifestyles, coupled with the increasing demand for treatment of younger and more active patients, are challenging the current therapies for knee joint degeneration. There is a major need for effective earlier stage interventions that delay or prevent the requirement for total knee replacement surgery. There are large variations in patients' knees and the way that they function, and it is important that this variation is taken into account when treatments are developed, so that the right treatment can be matched to the right patient.

Through this ambitious programme of research we develop novel testing methods that combine laboratory-based simulation and computer modelling to predict the mechanical performance of new therapies for the knee and enable their design and usage to be optimised. Importantly these tests take into account the variation in patients' anatomy and knee biomechanics, as well as variations in device design and surgical technique. This will enable different therapies, or different variants of a device, to be matched to different patient groups.



This data collection contains all MR and CT images collected on cadaveric knees, as well as corresponding outcomes of computational and experimental preclinical models developed to test existing and emerging knee therapies (ethics REC 18/EM/0224).