iCAIRD Industrial Centre for Artificial Intelligence Research in - - PowerPoint PPT Presentation

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

• f 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

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• 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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