iCAIRD

iCAIRD

Industrial Centre for Artificial Intelligence Research in Digital Diagnostics

June, 2019

Cambridge, MA

Santa Clara, CA

Peter Hamilton Leader Image Analytics, Philips Digital Pathology Solutions, Hon Professor of Tissue Imaging, QUB, Belfast

Andy Smout Vice President Research, Canon Medical, Edinburgh

Colin McCowan Professor of Health Informatics, University of Glasgow and Glasgow Safe Haven

Alison Murray Professor of Radiology, Director of SINAPSE, University of Aberdeen, NHS Grampian

David Harrison NHS Lothian & Universities of St Andrews, Edinburgh & Glasgow

Key features • Tried & tested partnerships •NHS NSS Board • Safe havens & HDRUK • SINAPSE •National PACS •Direct link to clinicians

Imaging Centre of Excellence

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Democratising AI: Reducing Barriers to Entry

1. The Domain Barrier

2. The Data and Annotation Barrier

3. The Clinical Validation Barrier

4. The Regulatory Barrier

5. The Channel to Market Barrier

Without an existing product line and an established clinical collaborator network it is hard for SMEs to know where to focus

Machine learning solutions require huge amounts of data to generalise well. It is hard for SMEs to get access to that scale of data and harder still to annotate it accurately

Without a product already integrated into the clinical workflow it is difficult for SMEs to validate algorithms in a real-world multi-centre setting and generate the evidence needed to demonstrate their clinical effectiveness

Healthcare AI has stringent requirements on safety and effectiveness. These can daunt SMEs wanting to enter the market

Without an established global sales and marketing organisation it is difficult for SMEs to access a large enough customer base, and without an established reputation it is equally hard to form commercial partnerships with established vendors

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The Safe Haven AI Platform (SHAIP)

Healthcare Enterprise Technology Company

WORKSPACE

Data Controller

Algorithm

Caldicott

Anonymizing

Data Portal

Machine

Learning Portal Safe Haven

Researcher

Data Scientist

Clinician 1: Researcher works with Clinician to

identify a potential new AI algorithm 2: Clinician identifies a suitable

cohort of patients for research

6: Researcher uses anonymizing data

portal to explore data and generate

ground truth without encountering PHI

7: Data scientist uses machine learning

portal to train new algorithm

3: Caldicott guardians approve use of

data from cohort for specified research

5: Data approved for research is pulled from

clinical systems and cached in the workspace

4: Data controller

allocates cohort to

company workspace

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Philips-centric pathology AI Exemplars: transforming pathology, enabling pathologists

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Endometrial AI Pathology App

Why? • 42% of gynaecological specimens are endometrial • uniform with >95% comprising single slide • Exclusion of neoplasia is key pivot • Only 3% of endometrial biopsies show adenocarcinoma • Only 1.5% are atypical • >95% of biopsies are benign Perfect setting to develop AI to screen out non-malignant/ atypical cases and reduce NHS workload Technically challenging Benign patterns show considerable heterogeneity in pattern due to endogenous and exogenous hormonal influence.

Cervical AI Pathology App

Why? • 26% of gynaecological specimens are cervical biopsies

(including punch biopsies, polyps and LLETZ/LOOP excisions)

• The primary reason for a cervical biopsy is for the assessment of cervical intra-epithelial neoplasia (CIN) and exclusion of invasive squamous or adenocarcinoma.

Perfect setting to develop AI to identify invasive cancer, generate automated reports and reduce NHS workload Technically challenging Requires contextual image mapping at multiple resolutions to distinguish CIN from Invasive cancer particularly the identification of microinvasion

If these targets are achieved there would be an 85% time saving in consultant time across these specimen types. This would result in a saving of £185,650 per annum for NHS GGC which is 54% of reporting time in gynaecological pathology. Extrapolated across the UK, this would equate to a saving of £9.3 M per annum

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Sustainability & SME growth

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Market reach

Public confidence

Sales

Regulatory

Accelerator

NSS NHS procurement

Health economics

UK SME ecosystem

SME Application SME

Engagement Team

Searchable data lake access for R&D and product development

Conduit to pathologists for application development and annotation

Access to data scientists for deep learning expertise in pathology

Use of validated tools for fast track deep learning development

Conduit to established industry platform as an option to accelerate pace to market

Entry to Accelerator Programme for training, mentorship and leadership in health-tech

Access to interdisciplinary team of health, technology and industry experts

On-line educational programme for pathologists and data scientists, business and innovators

SME Engagement

Digital pathology

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Implementation Economic case unproven Early adopter risk & competition Interoperability Artificial intelligence Platform and apps or tied to hardware provider Future of pathology High volume High complexity

Window of opportunity

• Clinical implementation

• Operating system for artificial intelligence apps & interoperability

• High quality artificial intelligence

• Clinical trials exemplars

• Open source datalake 9

• Clinical implementation – Visiopharm, Definiens, Indica

• iCAIRD funding – Blackford Analytics, Glencoe, EPCC

• Clinical trials exemplars – kidney cancer, links to industry, cancer centres & tissue, attracting in clinical trials, CSO Innovation Fellows

• Open source datalake & interoperability

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