How e-science may transform healthcare delivery. Professor Sir Michael Brady FRS FREng Hilary Term 2005

Transcription

1 How e-science may transform healthcare delivery Professor Sir Michael Brady FRS FREng Hilary Term 2005

2 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 On-demand epidemiology Coping with the mobile population Accommodate massive ranges of spatiotemporal scales Drug discovery and image-based clinical trials

3 UK Breast Screening Today Paper Film 1.5M - Screened in ,000 - Recalled for Assessment 8,545 Cancers detected Lives per year Saved Began in 1988 Women Screened Every 3 Years 1 View/Breast Scotland, Wales, Northern Ireland England (8 Regions) 92 Breast Screening Centres 230 Radiologists Double Reading Statistics from NHS Cancer Screening web site Each centre sees 5K-20K images/yr

4 UK Breast Screening Challenges Digital Digital 2,000,000 - Screened every Year 120,000 - Recalled for Assessment 10,000 - Cancers 1,250 -Lives Saved Women Screened Every 3 Years 2 Views/Breast + Demographic Increase Scotland, Wales, Northern Ireland England (8 Regions) 92 Breast Screening Programmes Radiologists double Reading 50% - Workload Increase Up to 50K/yr per centre

5 end-user project goals 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 Clinicians want to use the Grid & they profoundly wish to remain ignorant about how it works

6 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

7 Administration Client Authorised personnel at hospital X can create and edit a worklist The images in the worklist can be owned by Hospital Y

8 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

9 GridView This is a view of the Grid as it would appear to the Grid maintenance centre

10 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

11 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

12 Architecture 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

13 Phase 2 deployed in July 2004 Application Development Methodology CHU KCL UCL UED??? Data Load Training Appl Data Load Training Appl Data Load Training Appl Data Load Training Appl Training Application Core & Core Training API API Core & Core Training API API Core & Core Training API API Core & Core Training API API Core API Training API Training Services Core Services Core Services Core Services Core Services OGSA DAI OGSA DAI OGSA DAI OGSA DAI OGSA DAI OGSA DAI Data Federation DB2 Content Manager DB2 Content Manager DB2 Content Manager DB2 Content Manager Database Local Files

14 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

15 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

16 DICOM: Digital Imaging and Communications in Medicine DICOM entity relationship diagram Schema to store DICOM data for ediamond

17 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

18 Computer based diagnosis Training 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)

19 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

20 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, cancers The Grid potentially provides the statistical power at acceptable bandwidth and with guarantees on secure image/data transmission

21 Image data mining: FindOneLikeIt Response 1 Response 2 Response 3 Query image ediamond federated database Search features include: boundary, shape, texture inside & outside,

22 Image data mining: FindOneLikeIt Grid-enabled find-onelike-it from the Mirada EDAS Search features include: boundary, shape, texture inside & outside,

23 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

24 VirtualMammo I(SMF exp=115mas) I(SMF exp=66mas) SMF(x,y) = amount of non-fat tissue at (x,y) Generate SMF Original Image + calibration data See Caryn Hughes of Mirada Solutions for a demonstration

25 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

26 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 Ease of use Intelligent software generates trend analysis and identifies anomalous patterns Clinician alerted and brought into the loop as and when required Clinician Server

27 e-health e-health enables monitoring and selfmanagement of chronic conditions such as: diabetes asthma hypertension e-health relies upon smart signal processing and mobile telecoms

28 The Oxford Diabetes Type 1 clinical trial Both intervention and control group patients are given a blood glucose meter and a GPRS phone Blood glucose readings and patient diary (insulin dose, meal and exercise data) are transmitted by the GPRS phone to the remote server

29 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

30 Original Data From 200 Subjects 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?

31 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 nonrigidly a computationally intensive step

32 IMPERIAL COLLEGE Get reference images KING S S COLLEGE King s LONDON College London (Guy s Create Campus) atlas Patient scan + instructions Personalised Atlas Oxford University

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36 Major biomedical challenges spatiotemporal scales Epidemiology x10^3 people, years Medical science Radiology 1-10mm, Cancer 0.1s-1hour Personalised medicine CVD Pathology -1µ, in vitro Drug discovery Arthritis Proteomics Image-based Degenerative brain Genomics clinical trials disease No single group has all the expertise required Biochemistry 1nm, 1µs No single institution has all the expertise required Mathematical models differ at each Pace of development level, no and fixed are group hard of to people relate will across have the expertise Dynamic Virtual Organisations levels are the only effective way to tackle such problems