1. A Siemens Healthineers Portfoliójában
Mesterséges Intelligencia
A Siemens Healthineers Portfoliójában
In this talk we will highlight the current status and the potential impact of AI for the diagnostic imaging and digital services businesses.
Megyesi Béla

2. Challenges requiring AI powered solutions
Increasing
demand for
medical imaging
Growth of
CT/MR scanning
10-12%
but radiologist
Workforce only 3%
per year for the
last ten years1
230,000
patients
are waiting
over a month
for their imaging
test results1
1 The Royal College of Radiologists

3. Challenges requiring AI powered solutions
Mass data
overload
In 2010 it took 3.5 years for medical data
to double. In 2020 it is projected to be 0.2 years
- just 73 days2
The average radiologist
is interpreting
an image every
3–4 seconds,
8 hours a day1
1 “The effects of changes in utilization and technological advancements of cross-sectional imaging on radiologist workload.”, 9th January 2018,
2 University of Iowa, Carver College of Medicine report, 2014

4. Digitalizing Healthcare enabled by Artificial Intelligence
AI drives outcomes that matter to patients by
prioritizing complex/acute cases
avoiding unnecessary interventions
Improving patient
experience
AI drives efficiency and productivity by enabling
increased workforce productivity through assistance in automation
clinical operations optimization
Transforming care
delivery AI
Expanding precision
medicine
AI drives quality of care by increasing
diagnostic precision through quantitative imaging
personalization with intelligent image acquisition

5. Artificial Intelligence Technological impact at multiple levels
Artificial Intelligence Technology
Outcome analysis, quality of care, meaningful use
Population health management
Predict, plan, prescribe
Integrated decision support / Digital twin
Measurements, quantifications
Detection, diagnosis, guidance
Workflow automation
Reconstruction, advanced physics
Patient cohort
Patient centric
Reading / Processing / Guidance
Device / Instrument / Technology
Data
Access
Complexity
Integration

6. Overview of systems and software solutions
AI powered solutions
Overview of systems and software solutions

7. Ultrasound syngo.CT Laboratory Advanced Therapies MR
AI powered systems with partial and conditional automation already in clinical routine
Proactive follow-up
syngo.MI - segmentation
Coronary Analysis Function
eSie Measure
Myocardial Perfusion
syngo.via RT Image Suite (AutoContouring)
eSie Pisa
Segmentation
syngo Auto Left Heart (Auto LH)
Cardiac Planning – Aortic Valve Vascular
syngo.Breast Care - XP
Bone Reading
syngo.CT
eSie Flow
Ultrasound
eSie LVA
Lung CAD
More than 40
Siemens Healthineers AI-powered applications
PACS-Ready Coronaries & Calcium scoring (upcoming VB40)
eSie Valves
Colon.PEV Liver Analysis
syngo Auto Ejection Fraction (Auto EF)
PE CAD
syngo Auto OB Measurements
Automated Patient Positioning and FAST 3D Camera
Atellica – Drawer Vision System
Laboratory
syngo CTO Guidance
syngo Aortic Valve Guidance
240,000
Patient touchpoints per hours
Spine Dot Engine (Auto Align)
CLEARstent Live
syngo Embolization Guidance
SHS has already more than 40 applications with partial and conditional automation in clincial routine
Guardian Program – real-time, remote monitoring of analyzers, AI prediction
syngo.MR – Vascular Analysis
Advanced
Therapies
CLEARstent
syngo.MR - Cardiac 4D Ventricular Function
syngo.via General Engine (Anatomical Range Presets, AutoViews) MR
IVUSmap
Knee Dot Engine (Auto Align)
inlline VF
syngo EP Guidance
LiverLab
syngo TrueFusion
syngo.via MM-reading (Anatomical Registration)
Cardiac Dot Engine (Auto Align)
EVAR Guidance Engine
Brain Dot Engine (Auto Align)
Biomatrix Select & Go

8. with accurate patient positioning
AI helps to reduce unwarranted variations
with accurate patient positioning

9. About 2.6 cm mean deviation
AI helps to reduce unwarranted variations with accurate patient positioning
About 2.6 cm mean deviation
More
accurate
In 95% of all patients, isocentering could be improved
No inter-user variability
Ist für gestandene Radiologen langweilig.
Sie kündigen.
Source: Li J, Udayasankar UK, Toth TL et al. Automatic patient centering for MDCT: effect on radiation dose. AJR 2007; 188: 547 – 552 and Kaasalainen T, Palmu K, Lampinen A et al. Effect of vertical positioning on organ dose, image noise and contrast in pediatric chest CT-phantom study. Pediatric radiology 2013; 43: 673 – 684

10. Accurate patient positioning is key to safe, consistent, error-free CT imaging, reducing rescans and time loss
Users are as individual as patients,
and the quality of results can differ enormously.
“Special attention must be paid to a correct
patient centering in order to optimize organ
doses and image quality of the respective CT examination.“ 1
With its game-changing FAST Integrated Workflow, SOMATOM Force2, SOMATOM Drive2, and SOMATOM Edge Plus2 help technologists acquire the right body region at the right dose – in a reproducible way.
AI-powered workflow automation and standardization.
3D and infrared
picture data input
into multiple AI
algorithms AI
Direction,
shape, positioning
landmarks, dose
profiles are all
calculated AI
1 Natalia Saltybaeva, Hatem Alkadhi; Vertical Off-Centering Affects Organ Dose in Chest CT: Evidence from Monte Carlo Simulations in Anthropomorphic Phantoms
2 The system is pending 510(k) clearance, and is not yet available in the United States.

11. Deep learning algorithms help to care for patients more individuallyAI
Input
Output
Color Image Data
3D Depth Image Data
Infrared Image Data
Based on deep learning algorithms the following are possible:
Landmark detection
Range detection based on protocol input
Range adaption to user changes over time
Isocenter positioning
Patient direction analysis
Right dose modulation
with FAST Isocentering
Right scan direction
with FAST Direction
FAST Integrated Workflow incl.
unique FAST 3D Camera
Correct and complete body region
with FAST Range
The system is pending 510(k) clearance, and is not yet available in the United States.
A.I. system

12. standardized image results
AI helps radiologists by providing
standardized image results

13. No inter-observer variability
AI helps increased precision with time savings & providing reliable results independent from user skills
Less than 1 minute exam-time variation1
Save up to $14K per tech and year in workforce overtime salary2
No inter-observer variability
Potential for
time savings
Guidance for clinicians to understand the organs
Giving more time for clinicians to contour as time saver that leads more precise contouring - > treatment
Avoid any error prone -> No inter user variability
1 Zhongshang Hospital Fudan University, Fudan, CN, Abdomen Dot Engine Workflow Study; 2 Calculation based on: 38h/week; 48 working weeks/year; average annual salary $70K equals ~$40/h

14. Dot engines. Quality results for each examAI
Abdomen Dot Engine AI AI
Large Joint Dot Engine
Cardiac Dot Engine
TimCT Angio Dot Engine AI
Angio Dot Engine
Brain Dot Engine
Idea of this slide:
90% of Exams are covered by the one or other way, with automization features.
(not all of them are AI or Deep Learning based!!!)
RT Dot Engine AI
Breast Dot Engine AI
Whole Body Dot Engine AI
Spine Dot Engine
Over 90%1 of MRI exam requests covered by the Dot engines
1 Siemens Usability Evaluation of 9 million Siemens MR exams, 2013.

15. AI enables robust results in challenging anatomies,
Machine learning algorithms help to fuel AI technology in spine imagine AI
Input
Output
AI enables robust results
in challenging anatomies,
e.g. patients
with severe scoliosis. AI
Automated detection of intervertebral disc planes and display of vertebrae labelling - using anatomical landmarks
Planning images - Localizer
A.I. system

16. image analytics that drives efficient diagnostics
AI helps radiologists by providing
image analytics that drives efficient diagnostics

17. AI based image analytics drive efficient diagnostics – for faster results with less user interaction
MM Reading: Anatomy Visualizer
syngo.via1 now includes extended automatic and semi-automatic segmentation tools AI
Simplifies
imaging:
add, remove or
isolate anatomical
structures
Saves time by fast and easy segmentation of lung, heart and aorta with a single click
Allows semi-automatic free hand contouring of structures in MPR slices
Helps focus on relevant anatomy and achieves highly consistent results with less user interactions
1 syngo.via can be used as a standalone device or together with a variety of syngo.via-based software options, which are medical devices in their own right. syngo.via and the syngo.via based software options are not commercially available in all countries. Due to regulatory reasons its future availability cannot be guaranteed. Please contact your local Siemens organization for further details.

18. Artificial Intelligence technology could assist mammography reporting
May reduce
risk for
litigation of
erroneous
diagnosis
Universal imaging solution
Leverage algorithms to create automatic and consistent results for lesion location, which can be challenging AI AI
Augmented reporting
Pre-populate BI-RADS aligned structured report
Enable immediate and reproducible
reporting with
Automatic measurements of distance to skin line, nipple and chest wall
Reproducible indication of quadrant and o‘clock position
This feature is based on research, and is not commercially available. Due to regulatory reasons its future availability cannot be guaranteed.

19. What if we could create a digital twin of the patient’s heart?
Multiscale, Personalized Physiological Model of the patient’s heart
Similar dimensions, electrical signal activation, muscle contraction, ejection fraction, pressure dynamics
Mechanistic and statistical modeling
Model is under our control
Potential to test and prescribe best therapy for the patient
Let us introduce the concept of digital twin which relates not only to the data statistics but also to physiology. How things are working.
Multiscale, Personalized Physiological Model of the patient’s heart, built from patient’s data. Similar dimensions, electrical signal activation, muscle contraction, ejection fraction, pressure dynamics as the patient’s heart.
The difference is that such model is under our control: we can virtually test therapies, observe the outcome, and select the best therapy for the patient.
The system is currently under development and is not for sale. Its future availability cannot be guaranteed.

20. VirtualHeart: Digital twin to support EP interventions
Quantify tissue substrate
Model anatomy from images
Model myocardium fibers
Model anatomy
Fuse multi-source information
Compute physiology
Virtual test for therapy optimization
 Ejection Fraction, Stroke Volume
 Scar burden, healing tissue
 Fiber strain
Estimate cardiac electrophysiology
Patient-specific cardiac electromechanics
 QRS duration, morphology
 Stiffness, Stress
The system is currently under development and is not for sale. Its future availability cannot be guaranteed.
Neumann et al., “A self-taught artificial agent for multi-physics computational model personalization”, MedIA 2016.

21. Observed changes in QRS duration accurately predicted by the model
Continuous real-time guidance of left ventricle lead implant through digital twin
Side-by-side evaluation
Intraop
Update the model with interventional measurements (ECG, sensed delays), as the intervention proceeded
Preop
Create Digital Twin to assess best 3 LV lead positions in terms of QRS shortening
Our recent test took place in Bordeaux, France, with a focus on Cardiac Resynchronization Therapy.
This is a therapy with more than 30% non-responders. There is a high demand to determine a priori if the patient will respond or not, to optimize the lead placement and personalize stimulation delays.
One day before the planned CRT intervention, we computed the DT of the patient’s heart, which indicated the potential of a positive CRT response. The DT also suggested the best 3 LV placements of the lead.
During the intervention the DT has been updated with interventional measurements (ECG, sensed delays), and it has been able to predict accurately the outcome of the procedure and the changes in the QRS duration.
Evaluation performed at IHU Bordeaux, Dr. Ritter and Dr. Lafitte; Data courtesy of IHU Bordeaux
Observed changes in QRS duration accurately predicted by the model
The system is currently under development and is not for sale. Its future availability cannot be guaranteed.

22. Köszönöm a figyelmet! Megyesi Béla