AI in Healthcare Workshop

Canopy Partners Imaging Summit

September 2017

Raleigh, North Carolina

Ed Butler

Hanbo Chen, PhD

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Meet our Seattle team

Founding team’s story• Founding team: 10-year relationships• 15+ PhD’s with US Industry Experience• Academic and Industry Success

• GE• Siemens• NIH• Over 100 journal publications

Multidisciplinary• Medical Image Analysis• Deep Learning• Genomics/Bioinformatics• Regulatory Science• Communications• Healthcare experience

Global Perspective• Seattle, WA• Princeton, NJ• Beijing, China• Shenzhen, China

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Goals for this session

1. Practical applications of AI in Radiology

2. Workflow implications of AI 3. Getting started4. Computer vision & machine learning

Robot assistant at a hotel in Tokyo, July 2017

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Goal– relevant, useful AI, not this…..

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Hollywood and Generalized Machine Intelligence

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The practical AI we use every day

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Some practical AI uses in Radiology

• Urgent condition detection, egIntracranial hemorrhagePneumothorax Pulmonary embolism Intubation tube placement error

• Detailed notes communicated via worklist• Automated reporting• Quality Assurance

• Much more…..

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Workflow!

• Little workflow • Clinician task level: speed, accuracy

• Big workflow• Connecting the care team around the

patient

• Population level- Value Based Care• Building a “learning health system” • Attributing clinical outcomes from

value chain contributors

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Getting started in AI: Key decisions

Innovation Adoption Curve Ø Business priorities?Ø Competitive advantage strategy?

Strategic vs transactional approachesØ Influence AI deployment choicesØDevelop internal AI sophistication?ØLeverage data assets strategically?

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Yann LeCunLeNet to identify post

codes written on U.S. mails

A brief history of computer vision

1959 1966 1989 2012

Alex Krizhevsky, et al.AlexNet won ImageNet

challenging.

VS

MIT Summer Vision Project:1st year of computer vision

Hubel & Wiesel

1st FDA approval for clinical cloud-

based deep learning (Arterys)

Model-driven heuristic

algorithms.

2016

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Computer vision and machine learning

IntracranialHemorrhage

Conv Conv Conv

AI module invoked

FC FC FCClassification

Brain? YesICH? Yes

Results returned

Deep convolutional neural network inspired by brain structure

V1 V2 V3 decision

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Key concepts in computer vision

1. Classification: • Is it x? (y or n)

2. Segmentation: • Identify regions of interest

3. Regression: • Quantitative measurements

4. Image Captioning: • From digital images to words Is it ICH? Yes!

Bleeding area size: 5 cm2

This patient has bleeding in left parietal cortex.

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Deep learning example: ICH detection

• 3302 brain CT images were collected (multiple image centers and different scanners).

• Achieved 98.1% accuracy, 98.3% sensitivity, 97.6% specificity in detecting ICH cases.

• Performance is close to the blind annotation by other 2 radiologists (96.6% accuracy).

• Achieved 95.8% accuracy in classifying 8 identical bleeding regions (e.g subdural, subarachnoid).

• Automatically highlight potential bleeding areas (weak supervised learning).

Brain CT Scan Automatically detected ICH condition heat map

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Session review

üPractical applications• Urgent condition detection• Automated findings/reporting

üWorkflow implications• Little workflow, Big workflow, Aggregate workflow (pop health)

üGetting started in AI• Technology adoption curve positioning• AI Supplier models: strategic v consumer

üComputer vision key concepts• Classification, Segmentation• Regression, Captioning• Machine learning approaches• Example: AI Module for ICH Detection

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Thank You!

Questions?

Ed Butler, VP Corporate DevelopmentEdbutler@curacloudcorp.com

Hanbo Chen, PhD Research ScientistHanboc@curacloudcorp.com

https://curacloud.net