practical ai experience from imaging industry

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


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


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


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.


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


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).


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


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


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)


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


Thank you

Siemens HealthineersUltrasoundSiemens Healthcare22010 SE 51st StreetIssaquah, WA USA siemens-healthineers.com

Andy [email protected]

mailto:[email protected]

practical ai experience from imaging industry

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