VPH – Opportunities for Biomedical and IT Industries. Hofstraat H. eHealth week 2010 (Barcelona: CCIB Convention Centre; 2010)
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1. Virtual Physiological Human Biomedical and IT industries
providing tools for clinical decision taking Hans Hofstraat,
Philips Research March 17, 2010 With contributions from: Sybo
Dijkstra, Olivier Ecabert, Joerg Sabczynski
2. Trends in Healthcare Were getting older and sicker Demand
for care is growing We dont take good care of ourselves We expect
better choices Philips Research, WoHiT, Barcelona, March 17, 2010
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3. The Meaning of Our Care Cycle Approach The Philips
Healthcare difference People focused Care cycle driven We start
with the needs of patients and We focus on their their care
providers because understanding specific medical their experiences
ensures we create Oncology needs throughout solutions that best
meet their needs. Cardiology the care cycle Womens Health Oncology
Cardiology Womens Health Oncology Cardiology Womens Health And we
apply our technology to help improve healthcare quality and reduce
cost because meaningful innovations create value for patients and
care providers. wherever that care occurs. Meaningful innovation
Care anywhere Philips Research, WoHiT, Barcelona, March 17, 2010
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4. Medicine is Transforming from Art to Science Creating a Need
for Clinical Decision Support Knowledge explosion Need for
solutions Data explosion that enable Drive for better
evidence-based outcomes decision taking: Evidence based medicine
Clinical Decision Support Personalized medicine Philips Research,
WoHiT, Barcelona, March 17, 2010 4
5. Clinical Decision Support Clinical Decision Support
solutions interpret the universe of patient data, acquired from
various sources, intelligently filtered and distilled into
actionable, care specific information. In order to simplify
clinician workflow, improve financial outcomes, and help improve
and save lives. Decision support - Anytime. Anywhere. Philips
Research, WoHiT, Barcelona, March 17, 2010 5
6. Future of Clinical Decision Support Providing clinical
guidance based on multiple data sources Data Clinical Decision
Support Clinical Guidance Early Warning and Monitoring Alarms Image
Recognition Imaging Quantification & Interpretation Feature
Extraction Targeted Modeling Diagnostic Diagnostics Reasoning
Assistance Computer-Interpretable Guidelines Therapy Planning &
Pathology Monitoring Clinical data Outcome Prediction Philips
Research, WoHiT, Barcelona, March 17, 2010 6
7. Clinical Decision Support for cardiac interventions Therapy
planning & monitoring for minimally invasive therapy Data
Clinical Decision Support Clinical Guidance Early Warning and
Monitoring Alarms Image Recognition Imaging Quantification &
Interpretation Feature Extraction Targeted Modeling Diagnostic
Diagnostics Reasoning Assistance Computer-Interpretable Guidelines
Therapy Planning & Pathology Monitoring Clinical data Outcome
Prediction Philips Research, WoHiT, Barcelona, March 17, 2010
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8. Minimally Invasive Interventions in Cardiovascular Disease
#1 cause of death and 17-22% of global health spending Insight:
Less invasive interventions are at the base of a key paradigm shift
in healthcare Reduction of patient trauma and improvement in
quality of life Reduction in length of stay in hospital and in cost
of healthcare Examples: Valve repair/replace, ASD/VSD repair, CABG,
EP.. Cath Lab Interventional EP Navigator 3D Trans- Innovations for
Tools esophageal Echo Interventions Philips Research, WoHiT,
Barcelona, March 17, 2010
9. Background Our scanners produce a huge amount of patient
images with a wealth of information. We need a technology that
helps to inspect the data efficiently, derive quantitative
information, and use the images for therapy. Philips Research,
WoHiT, Barcelona, March 17, 2010 9
10. Road to the Future Philips Research, WoHiT, Barcelona,
March 17, 2010 10
11. Road to the Future Philips Research, WoHiT, Barcelona,
March 17, 2010 11
12. Personalized Cardiac Models - Principle Training +
Anatomical knowledge Sample images Generic model Philips Research,
WoHiT, Barcelona, March 17, 2010 12
13. Personalized Cardiac Models - Principle Training +
Anatomical knowledge Sample images Generic model Personalization +
Generic model New image Adapted model Philips Research, WoHiT,
Barcelona, March 17, 2010 13
14. Diagnosis: Automatic Determination of Heart Function Volume
of four heart chambers over a heart beat from CT images Typical
slowly (53 bpm) beating heart (bottom left) Irregularly (> 80
bpm) beating heart with small ejection fraction (bottom right)
Philips Research, WoHiT, Barcelona, March 17, 2010 14
15. Image-guided Interventions: EP Navigator Pre-interventional
Intervention CT or MR images Guidance Personalized heart model
Visualize left atrium to support accurate navigation of the
catheter Philips Research, WoHiT, Barcelona, March 17, 2010 Picture
courtesy of Catharina Hospital, Eindhoven
16. Road to the Future Philips Research, WoHiT, Barcelona,
March 17, 2010 16
17. Road to the Future Geometry Microstructure Microcirculation
Fluid Deformation Electrophysiology Philips Research, WoHiT,
Barcelona, March 17, 2010 17
18. euHeart Biophysical Cardiac Models Simulation of the
patient- Clinical focus areas: specific heart function -
Resynchronization Therapy - Radiofrequency Ablation - Heart Failure
Blood Electrical - Coronary Artery Diseases Flow Signals - Valves
and Aorta Project coordination: Philips Research Scientific
coordination: The University of Oxford Clinical coordination: Kings
College London Partners: Micro- Cardiac 6 companies, 6
universities, 5 clinics structure mechanics Budget: ~19M (~14M EU
funding) Philips Research, WoHiT, Barcelona, March 17, 2010 18
19. Clinical Decision Support for Oncology Choosing the therapy
with the best outcome tailored to the patient Data Clinical
Decision Support Clinical Guidance Early Warning and Monitoring
Alarms Image Recognition Imaging Quantification &
Interpretation Feature Extraction Targeted Modeling Diagnostic
Diagnostics Reasoning Assistance Computer-Interpretable Guidelines
Therapy Planning & Pathology Monitoring Clinical data Outcome
Prediction Philips Research, WoHiT, Barcelona, March 17, 2010
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20. Personalized cancer treatments Clinical need Cancer is a
hyper-complex disease Cancer is an individual disease Cancer
treatment decisions today are based on a statistical approach
Cancer treatment in personalized medicine must take into account
the individual cancer biology Philips Research, WoHiT, Barcelona,
March 17, 2010
21. Clinical Decision Support in Oncology Models to select the
Best Therapy for an Individual Patient Models are mathematical
representations of reality Models translate available data into
meaningful information Models for tumor response must be
multi-level models Models allow for treatment decision support and
Biopsy material, fluids multi-modal therapy optimization Gene,
protein expressions etc. Gene protein network Imaging data Models
in treatment planning systems Radiobiological, pharmacodynamic
parameter estimation Image processing Surgery Candidate Multi-level
cancer simulator for tumor and Clinical therapy normal tissue data
Radiotherapy response simulation Chemotherapy New candidate therapy
Prediction Evaluation of Interventional radiology prediction Final
decision Select optimal and treatment Sufficient? schedule
application to patient Philips Research, WoHiT, Barcelona, March
17, 2010
22. Multi-level Modeling in ContraCancrum Molecular Level
simulations Biochemical modelling EGFR mutations Molecular
statistical models of response to therapy Cellular and higher
biocomplexity level simulator Discrete event cytokinetic model of
cancer Biomechanical simulations Medical Image analysis modules The
integration of all ContraCancrum modules is implicitly done in
clinical multi-level scenarios Philips Research 24
23. Biochemical level Which drug for which patient? Over
expression of Epidermal Growth Factor Receptor (EGFR) is associated
with cancer Tyrosine kinase as target for inhibitory drugs Binding
affinity calculations can be used to determine mutational effects
phenethyl- Cl F amine aniline 2 3 N 2 3 1 N N 4 NH 1 N 4 NH
pyrrolo- 7 5 pyrimidine 8 5 quinazoline 6 7 6 O O propyl-
morpholino N N ethyl- piperazine N Philips Research O 25
24. Biomechanical level Simulating tumor growth Simulating
effect on normal tissue Interaction between cellular simulation and
biomechanics Philips Research 26
25. PET/CT Image Processing in ContraCancrum Registration of
multi-modality images Registration of time-series base-line
follow-up 1 follow-up 2 Segmentation of tumor Segmentation of tumor
subregions Segmentation of normal tissue Philips Research 27
26. In Silico Oncology - Simulating Therapy Modelling cancer G
S G M G Simulating Simulating at the cellular 1 2 0 Therapy A
Therapy B level N A Modelling at the molecular level Simulating
tissue biomechanics Tumour image analysis and visualization time
Multi-level Modelling In Silico Optimal therapy planning
Multi-level data Multi-level Modelling Philips Research 28
27. Oncology Clinical Decision Support image data Modelling
cancer G S G M G Simulating at the cellular 1 2 0 Therapy A level N
A Modelling at the molecular patient path personalized image level
analysis treatment protocol clinical data clinical guidelines
Simulating tissue biomechanics lab values clinical evidence
pathology workflow cancer therapy checklist Tumour image patient
data analysis model and visualization tim Multi-level Modelling In
Silico Optimal thera Philips Research 29
28. Future of Clinical Decision Support Providing clinical
guidance based on multiple data sources Data Clinical Decision
Support Clinical Guidance Early Warning and Monitoring Alarms Image
Recognition Imaging Quantification & Interpretation Feature
Extraction Targeted Modeling Diagnostic Diagnostics Reasoning
Assistance Computer-Interpretable Guidelines Therapy Planning &
Pathology Monitoring Clinical data Outcome Prediction VPH Philips
Research, WoHiT, Barcelona, March 17, 2010 30
29. Potential impact of VPH on Care Cycles Treatment In-silico
selection Guidance of treatment treatment optimization / testing
Out-patient follow-up Support in decision making Home Health
Improved Management Facilitated disease clinical data understanding
integration Early warning, Early avoidance of detection
exacerbations Risk stratification Philips Research, WoHiT,
Barcelona, March 17, 2010
30. Anticipated Impact of VPH on Stakeholders Patients /
Society Personalization of care: better outcomes, and
quality-of-life Containment of healthcare costs Clinicians
Integration of the fragmented and inhomogeneous data acquired
throughout the Care Cycle Higher confidence in decisions through
evidence-based and personalized medicine Industry Tools for
personalization of treatment Paradigm shift from purely descriptive
data interpretation towards prediction (and monitoring) of disease
progression and treatment outcome Philips Research, WoHiT,
Barcelona, March 17, 2010 33
31. Acknowledgement Universities and research institutes
Industrial partners INRIA, Sophia Antipolis, FR Berlin Heart, DE
INSERM, Rennes, FR HemoLab, NL University of Karlsruhe, DE Philips
Healthcare, NL & SP UPF, Barcelona, SP Philips Research, DE
University of Sheffield, UK PolyDimension, DE University of Oxford,
UK Volcano, BE Amsterdam Medical Center, NL Hospitals and clinics
KCL, London UK DKFZ, Heidelberg, DE INSERM, Rennes, FR HSCM,
Madrid, SP Amsterdam Medical Center, NL Philips Research, WoHiT,
Barcelona, March 17, 2010 34
32. Acknowledgement FORTH, Crete, Greece University of Athens,
Greece Universitt des Saarlandes, Germany University College
London, UK Univesity of Bedfordshire, UK Charles University Prague,
Czech Republic University of Bern, Switzerland Philips Research
Europe Hamburg, Germany Philips Research, WoHiT, Barcelona, March
17, 2010 35