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Using High Performance Computing to enhance the clinical decision making process in low back pain

Using High Performance Computing to enhance the clinical decision making process in low back pain. Prof. Damien Lacroix INSIGNEO Institute for in silico Medicine Department of Mechanical Engineering University of Sheffield. Low back pain impact. Treatment decision.

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Using High Performance Computing to enhance the clinical decision making process in low back pain

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  1. Using High Performance Computing to enhance the clinical decision making process in low back pain Prof. Damien Lacroix INSIGNEO Institute for in silicoMedicine Department of Mechanical Engineering University of Sheffield

  2. Low back pain impact

  3. Treatment decision

  4. IT Engineering into clinics Lumbar spine surgery leads to satisfactory short-term pain relief, but long/midterm outcomes are less positive in terms of incremental degeneration problems and pain resurgence We hypothesize that the lack of long term success is due to: • Incomplete understanding of the biomechanics of the lumbar spine • Required specificity of the patient condition (geometry, disc properties, loading, psychological profile, etc…) • A non-rationale engineering approach

  5. MySpine consortium

  6. MySpine objectives MySpine aims to create a clinical predictive tool to provide clinicians with patient-specific biomechanical and mechanobiological analysis. This tool will help to determine the best patient specific treatment for low back pain. The project will focus on disc degeneration pathology although the developed prototype system may be able to analyze other spinal pathologies as well.

  7. Multiscale biomechanical analysis Clinical software Patient-specific framework

  8. Subject-specificfiniteelementmodel Subject-specific shape model MRI CT • Morphing FE models onto disc / vertebrae shape model + Subject-specificFE model Generic FE model

  9. Patient-specific mesh

  10. Patient-specific 3D L1-S1 lumbar spine Post-processing through ABAQUS D7.3

  11. Patient-specific 3D L1-S1 lumbar spine Post-processing through ABAQUS

  12. VPH-Share: A secure web portal for the construction of personalised clinical workflows VPH-Share Scientific Coordinator: Prof. Rod Hose, University of Sheffield

  13. MySpine Major Platform Components XNAT Database VPH-Share HPC Server Abaqussimulation: Short terms effects (+Matlab, Python) MySpine Abaqussimulation: Ageing effect (+Matlab, Python) Image-based spine reconstruction

  14. Python Script

  15. Python Script Generic Abaqus input file Personalized inputs

  16. Python Script Patient-specific Abaqus input file

  17. Client Access to Visualisation Cluster Iceberg – Campus Compute Cloud VirtualGL Server (NVIDIA Fermi M2070Q GPU) VirtualGL Client

  18. Time / Cost analysis per patient Based on Amazon c3.4xlarge (16C, 30GB RAM)= $1.366/h • Patient specific model preparation (Gimias): 3h (1 core) • Full lumbar simulation (Abaqus): 8h per loading & per treatment (16 cores) • Bone remodelling and disc adaptation (Abaqus): 7h per year simulated

  19. Time to complete clinical prospective study With 400 cores available (e.g. N8), need only 23 days to run • 200 patients have been treated and followed for 2 years

  20. Discussion With additional resources, further analyses could investigate • additional loadings • variability in surgery techniques • different types of screws to provide an interval of confidence for the clinician.

  21. Conclusions A patient-specific platform to analyse the biomechanical load transfer between conservative treatment, discectomy and fusion was developed. Using VPH-Share cloud facilities and local or cloud HPC system, any clinician can have a patient-specific biomechanical analysis in less than 11 hours. Remote desktop visualization and cloud computing can provide a transformative change to NHS without hardware investment.

  22. Acknowledgements VPH-Share Prof. Rod Hose Dr. SusheelVarma Dr. Steven Wood

  23. Thank You!

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