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1© Siemens Healthineers 2020© Siemens Healthineers 2020
Practical AI Experience from Imaging Industry
Andy Milkowski
2© Siemens Healthineers 2020
Practical AI Experience from Imaging Industry
We believe
• AI can raise the standard of care• AI can improve components of the quadruple aim• FDA’s initiatives to create a more streamlined and efficient regulatory
oversight
We present a few opportunities to work with the FDA to develop industry consensus standards that reflect current state of technology
Patient Cohort
• Population health • Outcome analysis• Meaningful use
Patient Centric
• Digital Twin• Predict, plan prescribe• Appropriate use
Reading/ Reporting/ Guidance
• Measure, quantify• Detect, diagnose• Report
Scanner/ Instrument
• Workflow automation• Reconstruction
AI System Hierarchy
3© Siemens Healthineers 2020
Camera-based patient positioning system powered by AI
Faster, more consistent performance. Less radiation, improved quality. For experts or novices.
Current 510k mechanism adequate for assurance of safety and efficacy
Deep learning algorithms Landmark detection Range detection based
on protocol input Range adaption to user
changes over time Isocenter positioning Patient direction analysis
Siemens AI ExperienceComputed Tomography
Teixeira et al, Generating Synthetic X-ray Images of a Person from Surface Geometry, IEEE CVPR 2018
Color, 3D Depth, and Infrared Image Data + Deep Learning
Scan direction
Correct and complete body region
Dose modulation
4© Siemens Healthineers 2020
AI patient positioning, automatic bolus timing, automatic alignment, … 90% of exams powered by AI1
Reduced setup time (<1min2), variation (+29%3,4) and improved follow-up5
Current 510k mechanism adequate for assurance of safety and efficacy
2011 20132012
Siemens AI ExperienceMagnetic Resonance
1 Siemens Usability Evaluation of 75.1 million Siemens MR exams, 2018. 2 Data on file3 Zhongshang Fudan University Hospital, Fudan, CN, Abdomen Dot Engine Workflow Study. 4 Martin, Diego R., “Optimization of Single Injection Liver Arterial Phase Gadolinium Enhanced MRI Using Bolus Track Real-Time Imaging.” Journal of Magnetic Resonance Imaging; 33:110-118 (2011). 5 Renal Carcinoma patient monitoring scanned consecutive years on Aera and Skyra, Erlangen, Germany.
5© Siemens Healthineers 2020
Siemens AI ExperienceUltrasound
Image recognition based automated spectral Doppler, LA volume, LV volume measurements powered by AI
Faster (6min/exam1), more consistent (27% less variability2)For experts and/or novices
Current 510k mechanism adequate for assurance of safety and efficacy
1 Study at one clinical site on the ACUSON SC2000 system and one competitive system. Three sonographers scanned one healthy patient on each system following a standard TTE protocol. 2 Results were achieved in customer’s unique setting. There can be no guarantee that other customers will achieve the same results (Data on file).
6© Siemens Healthineers 2020
Adaptive Algorithms
Locked and Discrete Adaptive Algorithm• Improvements applied through new software version
Adaptive Algorithm• Software independently ‘learns’/changes based on reward mechanism• Changes without regulatory oversight• Workflow potential
Challenges with Adaptative Algorithms• Repeatability and reliability• Validation, Maintainability, traceability challenges • Unintended consequences (direction, bias, etc)
Industry can generate consensus standards and work with FDA on regulatory oversight for adaptive algorithm safety and efficacy issues
7© Siemens Healthineers 2020
Non-expert User
‘Autonomous’ traditionally defined as no human in the loop
Non-expert users arguably similar to autonomousAI Image guided acquisition expert cannot intervene in a timely fashion
Non-expert user• Confirm properly operating device & device is working properly• Medical data property and access rights• Missing actionable information liability• Non-regulated apps not covered
• Foreseeable Misuse … sufficiency of ‘labeling’ mitigation
Non-expert users outside medical setting introduce new safety concerns that can and must be mitigated
Ref 2020.01.24 COCIR meeting AI regulatory aspects 30 January V2
8© Siemens Healthineers 2020
Regulatory Framework
Support FDA’s initiatives to create a more streamlined and efficient regulatory oversight
Introduction of preferred approval pathways• Predetermined Change Control Plan • Broad Algorithm Change Protocols (ACP) • SaMD Pre-Specifications (SPS)• Good Machine Learning Practices
Existing, traditionally-approved devices could benefit• Equivalent to ‘Pre-Cert’ / SaMD devices• Innovations beyond AI/ML SaMD
Existing, successful authorization pathways should take advantage of streamlined and efficient regulatory oversight
Ref Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML) – Based Software as a Medical Device (SaMD)
9© Siemens Healthineers 2020
Key Messages
Industry has broad success in implementing AI algorithms within existing regulatory framework
Most adaptive algorithms should be managed as discrete adaptive today
Non-expert users create additional risks that must and can be managed
Existing regulatory framework should be updated inline with recent proposals
Look forward to work with the FDA to develop consensus standards, update existing regulatory framework, and advance patient healthcare
Gartner Hype Cycle
10© Siemens Healthineers 2020
Thank you
Siemens HealthineersUltrasoundSiemens Healthcare22010 SE 51st StreetIssaquah, WA USA siemens-healthineers.com
Andy [email protected]