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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• …

• 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 –

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”

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

• 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

CoreServices

OGSADAI

Core API

DB2 ContentManager

DataLoad

CoreServices

OGSADAI

Core API

DB2 ContentManager

DataLoad

CoreServices

OGSADAI

Core API

DB2 ContentManager

DataLoad

Core API

Training Application

Training API

LocalFiles

Database

TrainingServices

OGSADAI

Core & Training API Core & Training API Core & Training API Core & Training API

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

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

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

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>

<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>

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

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

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

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

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)

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