1. Machine Learning and Medical Devices
Presented by: Damini Chelladurai

2. Overview
Why Machine Learning, Applications, Examples

3. Why Machine Learning for Medical Devices?
As we have learned, healthcare is based on a LOT of data
Using data for:
Decision-making
Innovation
Efficiency
Why Machine Learning for Medical Devices?

4. Applications: Disease Identification and Diagnosis
IBM Watson Genomics
Partnership between IBM Watson Health and Quest Diagnostics
Data collected from Memorial Sloan Kettering (image from OncoKB)
Publicly accessible
Applications: Disease Identification and Diagnosis

5. Applications: Disease Identification and Diagnosis
Berg
AI-powered biotech
Disease profiling to provide drug targets/ biomarker targets
Applications: Disease Identification and Diagnosis

6. Applications: Disease Identification and Diagnosis
Google DeepMind Health
AI based -powered biotech
Uses Eye scans, mammograms, medical records to identify possible issues
Applications: Disease Identification and Diagnosis

7. Applications: Personalized treatment
Somatix and SmokeBeat app
Requires a smartwatch/smartband
Uses personalized Cognitive Behavior Therapy
Applications: Personalized treatment

8. Examples of Current Technology and Research
Wearable device for seizure detection
Extremely helpful for those who have treatment-resistant epilepsy
Allows to get to safe location
Uses convolutional neural network
App to determine arterial stiffness
Uses the patient’s pulse
Cogito
Voice analysis to determine well being
Interface provides a rating on their mood
Examples of Current Technology and Research

9. Obstacles
Data Governance, Streamlining Electronic Records, “Data Silos”

10. FDA Two types of devices according to FDA:
CADe device- used to draw attention to a certain area of interest for the clinician
CADx device- provides an assessment of the actual disease- identification, treatment, other details
Difference between these two?
Currently not much in CADx domain
Restrictions- approval time vs. software cycle, black box nature of algorithms
Transparency of algorithms
There needs to be transparency about what information is collected and how it will be used
FDA

11. Streamlining Electronic Records
We’ve talked a lo about this in class!
Linking data- provides more data for the algorithm to access
Overcoming these challenges is important
Provides the opportunity to use just medical records and machine learning to provide valuable markers
Creating a more cohesive method for two different systems to communicate information
These markers are important
Streamlining Electronic Records

12. More prevalent in the pharmaceutical field- but still an important issue
Data regulation is also an issue here- mainly because of concerns with concerns about intellectual property
Research and development tends to be a non-collaborative effort between companies or even stakeholders
Allowing external partners and collaborations knowledge can be shared and allow for greater innovations
Internal collaboration is essential- allow for greater insight
“Data Silos”

13. Questions?

14.
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Dihub
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