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London Medical Imaging & Artificial Intelligence Centre for Value-Based Healthcare
Professor Reza Razavi
Centre Director
Unrestricted © Siemens Healthcare GmbH, 2018
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No. of patients
No. of procedures
Reimbursement Fee
The traditional healthcare business model is changing
Patient population 4
Patient population 3
Patient population 2
Patient population 1
∑
Inpatient clinics
Outpatient clinics
Other care professions
Lab and pathology
Anesthesiology
Radiology
Intensive care
Emergency care
Neurology
Surgery
Cardiology
Oncology
Fee-for-service
= Health outcomes and costs per patient
Optimisation
criteria
Fee-for-outcome
Oth
er ho
spitals
Prim
ary care
Oth
er rehab
, elder care etc.
Clinical demand definition of patient population
Patient group experts
Information provider, Decision enabler
Functional experts and teams
Other providers
Healthcare costs
HC DI
Page 3 | Unrestricted © Siemens Healthcare GmbH, 2017
1) Source: www.ai-healthandlifesciences.com
50% potential reduction in medical treatment costs1)
Artificial intelligence (AI) for personalized decisions:
Big data support Personalised diagnosis & therapy Creating Benefits for
• Patients: Cost efficient precision medicine • NHS: Cost savings for the NHS • UK: Transformative economic growth By Enabling • Time efficient automated reporting
• Optimal treatment stratification
• Major redesign of clinical pathways to improve outcomes and efficiency
• New business opportunities for UK companies and job creation
50% potential reduction in medical treatment costs1)
Artificial intelligence (AI) for personalized decisions:
Big data support
Our Centre Vision: Driving the Transformation towards
Value-Based Healthcare
Centre Overview
• Focus on 12 clinical pathways for maximum impact
• 10 SMEs core partners supported on site
• Siemens European Stratified Medicine R&D headquarters
• Co-location of academic, clinical and industry/SME staff: “sandpit” for
rapid prototype iteration
• World-leading expertise in imaging, AI, clinical research
• Substantial computational infrastructure, in partnership with IBM & NVIDIA
• “Bringing the algorithms to the data”
• Engagement with NHS commissioners and health economics
AInostics Thames Mammography
Centre Members
St Thomas’ Hospital Hub
Radiologists/clinicians & health economics Researchers in advanced imaging & AI
Multinational Industry (e.g. Siemens) Small & Medium Enterprises
Radiologists/clinicians & health economics Researchers in advanced imaging & AI
Multinational Industry (e.g. Siemens) Small & Medium Enterprises
Wellcome/EPSRC Centre for Medical
Engineering
Industrial strategy: growing SMEs
SMEs
Clinicians
Health economics
AI/data science experts
Regulatory, QMS & data governance
Computing infrastructure
Siemens, GSK, NVIDIA, IBM
Wellcome/EPSRC Centre
HDR UK LHCRE
BRCs Medical imaging
Business seminars
NHS commissioner engagement
Software development
IP & commercial
strategy
NIHR Med Tech Co-operative
KiTEC The Foundry
Venture Capital
NHS data
Acute Chest Pain
Patient Presents at
A&E with Stable
Chest Pain Clinical Assessment
Non-invasive CT
Coronary
Angiography
Patient Presents at
A&E with Stable
Chest Pain
Admission pending
further investigation
Clinical Assessment
Admission pending
further investigation
Discharge back to
Primary care
Negative
for CAD
CAD
explaining
symptoms
Key Question: Does introducing a CT scan
for low to intermediate risk ACP patients
result in earlier diagnosis and subsequent
appropriate treatment or discharge?
Interesting Facts/Benefits:
• At St Thomas’ 12 patients per day are
admitted for observation with acute chest
pain, at a cost of £1000 per patient.
• Cardiac CT costs £200. 80-90% of
patients could potentially be discharged
earlier with this facility available.
•ACP patients are often discharged to GPs
with no definite diagnosis and are,
subsequently, often referred back to
cardiology.
• NICE acknowledges the use of cardiac CT
• Rapid turnaround of scan and report
within 4 hr wait
Deep learning for automatic reporting of X-rays
Annotation of all images required for training & testing Automated tagging through deep nets for natural language modelling
Initial application: fully-automated reading of chest x-ray images One million exams from Guy & St Thomas’ Archives (2005-15)
Opacity
Pleural Effusion
Enlarged Heart
Enlarged heart
Fully-automated tagging of a raw image done in milliseconds
Semantic segmentation learned from weak labels only
Medical Device
Enlarged heart
Clinical Finding Sensitivity Specificity Accuracy
Medical Device 0.925 0.934 0.929
Opacity 0.883 0.906 0.894
Pleural Effusion 0.951 0.950 0.951
Enlarged Heart 0.917 0.882 0.900
Atelectasis/Collapse 0.852 0.846 0.849
Pneumothorax 0.868 0.909 0.889
Nodule 0.746 0.865 0.806
COPD 0.892 0.865 0.878
Edema 0.912 0.936 0.924
Infection 0.823 0.859 0.841
Abnormal chest radiographs were detected with a precision of 0.94; sensitivity of 0.95 and specificity of 0.71
Pesce E, et al. Radiology 2018 (in print)
Run-time performance: 15 fps
Machine Learning for Organ Recognition in Fetal screening
Baumgartner CF et al IEEE Trans Med Imaging. 2017 Nov;36(11):2204-2215
Deep Learning for Prognostic Modelling in Cancer
For the treatment of oesophageal cancer, neoadjuvant chemotherapy
(NC) is given as a first step before surgery Responders to NC have improved survival after surgery, but prognosis
is worse for non responders
Survival rates in 107 patients
Recent radiomics studies have suggested that texture features extracted from 18F-FDG PET scans can predict NC response
Deep neural networks offer an alternative radiomics strategy: they learn predictive imaging features from raw scans
Responder
Non Responder
Best classifier using >100 texture features achieves 66.8% accuracy A convolutional network fusing adjacent slices reaches 73.4%
Ypsilantis et al. 2015 PLOS One
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