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How e-Science may transform healthcare delivery
All Hands Meeting, September 2004
Professor Sir Michael Brady FRS FREngDepartment of Engineering Science
Oxford UniversityFounding Director, Mirada Solutions Ltd
“medicine is a humanly impossible task”
• “It is now humanly impossible for unaided healthcare professionals to deliver patient care with the efficacy, consistency and safety that the full range of current knowledge could support” http://www.openclinical.org/
• Exponential growth in knowledge and techniques, eg over past 20 years:– New imaging modalities CT, MRI, PET, fMRI, MEG– Entirely new drug therapies, particularly for cancer
and brain disease– Molecular medicine
Data flood & information trickle
• Surge in what information might be mobilised in patient management
• Rise of PACS and NHS spine
• Workstations everywhere … and used
• Doctors are drowning in data
• They need information to support patient management decisions
Staying abreast
• Doctors are already working to their limits, and beyond, never mind staying abreast
• Continuing education is vital yet requirements are spotty compared to the USA
Concentration of expertise
• As knowledge becomes deeper and broader, expertise is becoming more widely distributed
• ‘Twas ever thus – large teaching centres in UK, USA, France, …– postcode medicine
• Trend has accelerated
• Example: mammography in the USA
Population expectations
• Less and less prepared to accept, without challenge, expert pronouncements
• Press reports fuel expectation of average treatment that cannot be satisfied by NHS, particularly for a greying population
• More people expect more, high quality treatment, want to participate in management of their condition, want access to specialised knowledge, and they want it now!
Patient management
• Increasingly a team process– Multidimensional meeting– Dialogue of the “hard of hearing”
• The team is increasingly distributed widely
• The team is increasingly a VO
• The timescales are shortening
• While medicine is becoming ever more complex
Patient management = a highly and increasingly complex example of business-on-demand
What might the Grid offer?
• Distributed power, bandwidth & security etc• Federated database: eDiamond• Statistical power• New approaches to CPD• Distributed intelligence: semantic web• Support for continuous monitoring• On-demand epidemiology• Personalisation• Coping with the mobile population• Accommodate massive ranges of spatiotemporal scales• Drug discovery and image-based clinical trials• …
What might the Grid offer?
• Distributed power, bandwidth & security etc• Federated database: eDiamond• Statistical power• New approaches to CPD• Distributed intelligence: semantic web• Support for continuous monitoring• Personalisation• Coping with the mobile population• Accommodate massive ranges of spatiotemporal scales• On-demand epidemiology • Drug discovery and image-based clinical trials• …
UK Breast Screening –
Today
Began in 1988
Women 50-64ScreenedEvery 3 Years1 View/Breast
Scotland,Wales,Northern IrelandEngland(8 Regions)
92 BreastScreeningCentres
Each centre sees5K-20K images/yr
1.5M - Screened in 2001-0265,000 - Recalled for Assessment8,545 – Cancers detected300 - Lives per year Saved
230 – Radiologists “Double Reading”
Film
Paper
Statistics from NHS Cancer Screening web site
UK Breast Screening – Challenges
230 - Radiologists “double Reading”50% - Workload Increase
2,000,000 - Screened every Year120,000 - Recalled for Assessment10,000 - Cancers1,250 - Lives Saved
Women 50-70ScreenedEvery 3 Years2 Views/Breast+ DemographicIncrease
Scotland,Wales,Northern IrelandEngland(8 Regions)
92 BreastScreeningProgrammes
Up to 50K/yr percentre
Digital
Digital
• Architecture–IBM Hursley, Oxford eScience Centre
• Acquisition workstation–Mirada Solutions
• Teaching tool for radiologists, radiographers –St George’s Hospital
• Tele-diagnosis –Edinburgh Breast Screening Unit, W. of Scotland
• Algorithm development: data mining –Oxford medical vision, Oxford Radcliffe Breast Care Unit, Mirada
• Epidemiology –Guy’s Hospital, London
• Quality control –Oxford Medical Vision Laboratory, Mirada Solutions
end-user project goals
Clinicians want to use the Grid & they profoundly wish to remain ignorant about how it works
Functional overview
Key functions are:
• Image acquisition (EDAS)
• query – by several classes of user
• Image retrieval – individually or as a collection
• Diagnostic reports
• Image processing & data mining
Administration Client
Authorised personnel at hospital X can create and edit a worklist
The images in the worklist can be owned by Hospital Y
Viewer
Once there is a worklist created at Hospital X, DICOM images owned by Hospital Y can be viewed by an authorised person at Hospital Z, eg for second reading
GridView
This is a view of the Grid as it would appear to the Grid maintenance centre
Data Acquisition Workstation
• control digitisation of films (or input of directly digital images)
• facilitate addition of annotations
• attach annotations to digitised images
• create/maintain local database
• serve pro tem as visualisation platform
Reporting and Annotation
• support entry of patient data
• reports on location and type of lesion
• …
Reporting varies from centre to centre
Several people contribute at different times to the report & have differing levels of authority
Several people can access & use the report
• Virtual image store– Each breast care unit maintains its own image store
(relational database of patient data & image metadata)• Open standards
– OGSA (Open Grid Services Architecture) GT3• OGSA-DAI
– Data Access & Integration (UK project)– Enables data resources such as databases to be
incorporated in OGSA– OGSA-DAI services represent non-image & image data– “Staging” of data & creation of a worklist
• IBM’s DB2 & Content Manager
Architecture
Phase 2 – deployed in July 2004
Application Development Methodology
CoreServices
OGSADAI
DataLoad
Core API
DB2 ContentManager
CHU
CoreServices
OGSADAI
Core API
DB2 ContentManager
UCL
DataLoad
CoreServices
OGSADAI
Core API
DB2 ContentManager
UED
DataLoad
CoreServices
OGSADAI
Core API
DB2 ContentManager
KCL
DataLoad
???
Core API
Training Application
Training API
LocalFiles
Database
TrainingServices
OGSADAI
OGSADAI
Core & Training API Core & Training API Core & Training API Core & Training API
TrainingAppl
TrainingAppl
TrainingAppl
TrainingAppl
Data Federation
Data Base: key issues
• Large data sets– Images– screening forms as DICOM Structured Report Documents
• Multimodality• Data heterogeneity• Multi-view, temporal and bilateral sets• Infrastructure support• Security
Grid query service architecture
• Interactions with eDiamond database via web services
– query using XML documents
– Ascertain the access rights of a user– Passed to OGSA-DAI– Returns an XML document (easy to translate)
• Access attempts are logged correctly
– Several users can be mapped to a single database account while maintaining traceability
DICOM: Digital Imaging and Communications in Medicine
DICOM entity relationship diagram Schema to store DICOM data for eDiamond
Security modelling
• Considerations– Ethical & legal– NHS network constraints– Current & projected IT initiatives– Deployment of workflow methods
• Asset attributes– Confidentiality– Availability
– Integrity
• human error
• annotation error
• software failures
• IP rights infringed
• data corruption
• theft
• natural disaster
• hacker
• DOS attack (eg virus)
• Unauthorised snoop
• impersonisation
What might the Grid offer?
• Distributed power, bandwidth & security etc• Federated database: eDiamond• Statistical power• New approaches to CPD• Distributed intelligence: semantic web• Support for continuous monitoring• Personalisation• Coping with the mobile population• Accommodate massive ranges of spatiotemporal scales• On-demand epidemiology • Drug discovery and image-based clinical trials• …
Computer based diagnosisTraining sets
• learn characteristic signs from positive examples (eg cancers)
• normality from a screening population, from which abnormality = tails of the normal population density function (“novelty”)
Regions defined by dense attenuation and significant changes in local phase
Have associated descriptor of the shape of the region (left, here spiculated)
Have associated texture descriptors learned from training set (textons from filter response hidden MRF (right)
Training sets
• Subtle distinctions need to be learned
• Images are complex, with poor SNR
• Need many feature dimensions to guarantee acceptable sensitivity/specificity
• “curse of dimensionality”
• Conclude: need vastly more exemplars for learning than can ever be provided in even the largest centres
Can the Grid provide the required power?
• Screening results in about 6 cancers per 1000 cases
• A typical centre sees 10,000-15,000 screening cases annually, that is, 60-90 cancers
• The Grid potentially provides the statistical power at acceptable bandwidth and with guarantees on secure image/data transmission
Image data mining: FindOneLikeIt
Query image
Response 1 Response 2 Response 3
eDiamond federated database
Search features include: boundary, shape, texture inside & outside, …
Image data mining: FindOneLikeIt
Search features include: boundary, shape, texture inside & outside, …
Grid-enabled find-one-like-it from the Mirada EDAS
What might the Grid offer?
• Distributed power, bandwidth & security etc• Federated database: eDiamond• Statistical power• New approaches to CPD• Distributed intelligence: semantic web• Support for continuous monitoring• Personalisation• Coping with the mobile population• Accommodate massive ranges of spatiotemporal scales• On-demand epidemiology • Drug discovery and image-based clinical trials• …
VirtualMammo Grid
• VirtualMammo is a CPD tool for radiographers– Distributed in USA by ASRT– Recently made freely available for NHS staff
by Mirada
• VirtualMammo models image formation– User can experiment with changing tube
voltage, exposure time, ...– Distributed with a “canned” set of cases
VirtualMammo
SMF(x,y) = amount of non-fat tissue at (x,y)
I(SMF|exp=115mAs) I(SMF|exp=66mAs)
Original Image + calibration data
GenerateSMF
See Caryn Hughes of Mirada Solutions for a demonstration
Why VirtualMammo Grid?
• Much of medical training resembles apprenticeship
• Examples are drawn from teacher’s personal experience to illustrate points raised in class– a canned set of cases is not enough
• But most teachers only see a small percentage of the cases of interest ..
• Need to be able to generate SMF “on demand” perhaps after data mining for suitably annotated images … ie Grid enable it
What might the Grid offer?
• Distributed power, bandwidth & security etc• Federated database: eDiamond• Statistical power• New approaches to CPD• Distributed intelligence: semantic web• Support for continuous monitoring• Personalisation• Coping with the mobile population• Accommodate massive ranges of spatiotemporal scales• On-demand epidemiology • Drug discovery and image-based clinical trials• …
The application domain: breast cancer management
DIAGNOSIS
Diagnosis is reached after image analysis AND reasoning about image contents and patient data
Joint initiative between two major projects in the UK: MIAS (Medical Image analysis) and AKT (Advanced Knowledge Technologies)
<rdf:Description rdf:ID="ROI-0001-0001"> <rdf:type> <rdfs:Class rdf:about="#Mammo-Abnormality"/> </rdf:type> <RDFNsId2:has-depth rdf:resource='depth-subareolar' rdf:type='Depth-Descriptor'/> ... ...</RDFNsId2:Mammo-Abnormality>
RDF
<rdf:Description rdf:ID="ROI-0001-0001"> <rdf:type> <rdfs:Class rdf:about="#Mammo-Abnormality"/> </rdf:type> <RDFNsId2:has-depth rdf:resource='depth-subareolar' rdf:type='Depth-Descriptor'/> ... ...</RDFNsId2:Mammo-Abnormality>
DAML
<Mammo-Abnormality rdf:ID="ROI-0001-0001"> <has-depth rdf:resource='depth-subareolar'/> <has-morph-feature rdf:resource='shape-mammo-irregular'/> <has-morph-feature rdf:resource='margin-mammo-spiculated'/> <is-finding rdf:resource='mass'/></RDFNsId2:Mammo-Abnormality>
OWL
Ontology for breast cancerIndex of key concepts and their relationships with unambiguous machine-readable semantics
Based on BI-RADS lexicon for mammography
Ontology-mediated annotation
Advanced Knowledge Technologies
Image Descriptor
Region-of-Interest
Mammogram
Shape
Area
Margin
has-descriptor
contains
has-descriptor
graphic-region
image-id
has-descriptor
Enrichment of Region of Interestsby the computer or by the user
Advanced Knowledge Technologies
Browser interface
FIND SIMILAR …
• Shape: oval, tubular, lobulated, …
• Margin: circumscribed, obscured, …
• Re-express the annotations using description-logic instances • Use them to construct the lattice via Formal Concept Analysis • Nest line diagrams with regard to different features• Associate individual patient record with nodes on the line diagram
Navigating records using a lattice browser
What might the Grid offer?
• Distributed power, bandwidth & security etc• Federated database: eDiamond• Statistical power• New approaches to CPD• Distributed intelligence: semantic web• Support for continuous monitoring• Personalisation• Coping with the mobile population• Accommodate massive ranges of spatiotemporal scales• On-demand epidemiology • Drug discovery and image-based clinical trials• …
Continuous monitoring 20-25% of the population in the Western world suffers from a chronic condition
(diabetes, asthma or high blood pressure), yet tele-medicine has had relatively little impact on improving the management of these conditions
e-San and the University of Oxford have developed a new solution based on 2.5G mobile phone technology (which allows real-time interactive data transfer) for the self-monitoring and self-management of asthma and diabetes
Server
Clinician
Ease of use
Intelligent software generates trend analysis and identifies
anomalous patterns
Clinician alerted and brought into the loop as
and when required
e-Health
• e-Health enables monitoring and self- management of chronic conditions such as:
– diabetes
– asthma
– hypertension
• e-Health relies upon smart signal processing and mobile telecoms
Both intervention and control group patients are given a blood glucose meter and a GPRS phone
The Oxford Diabetes Type 1 clinical trial
Blood glucose readings and patient diary (insulin dose, meal and exercise data) are transmitted by the GPRS phone to the remote server
What might the Grid offer?
• Distributed power, bandwidth & security etc• Federated database: eDiamond• Statistical power• New approaches to CPD• Distributed intelligence: semantic web• Support for continuous monitoring• Personalisation• Coping with the mobile population• Accommodate massive ranges of spatiotemporal scales• On-demand epidemiology • Drug discovery and image-based clinical trials• …
Personalised diagnosis
• Diagnosis is often made relative to an exemplar that consitutes “normality” and “normal expected variation”
• In the case of brain disease, the examplar typically consists of an atlas that is the “average” brain computed from a set of brain images
• Normality varies from patient to patient
Used to create an atlas – the “average” brain, so that differences between this brain and the average can be noted
The database of brains may comprise: young/old; male vs female; normal vs (many) diseases; left vs right handed; …
Can we dynamically create an atlas that is relevant for this patient?
Original Data From 200 Subjects
Personalised diagnosis
• Personalising the atlas– “a 79 year old man who has a history of transient
ischaemic attacks”• The Grid is crucial because:
– The data is federated across many sites to provide the necessary statistical power
– selection of relevant exemplars has to be computed “on demand”
– The atlas then needs to be computed on the fly from the exemplars
– To do this, the data needs to be aligned (geometrically and photometrically) and perhaps non-rigidly – a computationally intensive step
OxfordUniversity
King’s College London
(Guy’s Campus)
IMPERIALIMPERIALCOLLEGECOLLEGE
KING’S COLLEGE KING’S COLLEGE LONDONLONDON
Patient scan+ instructions
Get reference images
Create atlas
Personalised Atlas
Major biomedical challenges spatiotemporal scales
Cancer
CVD
Arthritis
Degenerative brain disease
Epidemiology – x10^3 people, years
Radiology – 1-10mm, 0.1s-1hour
Pathology - 1μ, in vitro
Proteomics
Genomics
Biochemistry – 1nm, 1μs
Mathematical models differ at each level, and are hard to relate across levels
Medical science
Personalised medicine
Drug discovery
Image-based clinical trials
No single group has all the expertise required
No single institution has all the expertise required
Pace of development no fixed group of people will have the expertise
Dynamic Virtual Organisations are the only effective way to tackle such problems
Acknowledgements and where to look for further information
• This talk was distilled from the efforts of many scientists, all of whom I thank:
• The eDiamond team at IBM Hursley (especially Dave Watson, Alan Knox, John Williams); Oxford eScience Centre (especially Andy Simpson, Mark Slaymaker, David Power, Eugenia Politou, Sharon Lloyd); Mirada Solutions Limited (Tom Reading, Dave McCabe, Caryn Hughes, Chris Behrenbruch); UCL & St George’s; Churchill Hospital, Oxford; Edinburgh University & Ardmillan; KCL &Guy’s
• For VirtualMammo: Mirada Solutions Ltd (Caryn Hughes, Kranti Parekh, Hugh Bettesworth, Ralph Highnam)
• For semantic web: the MiAKT consortium headed by Professor Nigel Shadbolt (Southampton)
• For distributed monitoring: Professor Lionel Tarassenko (Oxford) & e-San• For personalised diagnosis: the iXi project, especially Professor Derek Hill
(KCL, ie UCL)• For spatiotemporal analysis: the Integrated Biology group in Oxford led by
Dr. David Gavaghan (Oxford)