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Ava Women’s health
Lisa Falco, Director of Data Science
Geek Girl’s Carrot, 2018.10.27
2
•  MSc in Engineering Physics, Chalmers, Sweden, 2001
•  PhD in Biomedical image analysis, EPFL, Switzerland, 2006
•  Solianis Monitoring, Zürich, 2006 - 2011
•  Data scientist in the algorithm development team
•  Non-invasive glucose monitoring
•  Scanco Medical, Zürich, 2011 - 2015
•  Product manager
•  micro-Computer Tomography
•  Joined AVA in 2015 as Director of Data Science
MY WAY TO AVA
3
1.  AVA – The vision and solution
2.  The basics - Why fertility tracking works
3.  Algorithms – How the methods evolves with the data
4.  Future – the challenges ahead
CONTENTS
4
Women face various health challenges along their reproductive life stages
THE CHALLENGE
Understand your body
33%
Trying to conceive
10%
PregnancyContraception
33%
Menopause
60 years20
Ava is a long-term companion for women,
giving them data-driven and scientifically
proven insights along all stages of their
reproductive lives.
OUR VISION
5
Ava is the world’s first artificial
intelligence (AI) platform in women’s
health
6
TODAY’S SOLUTION
Bracelet
App
Big data / AI
•  Breathing rate
•  Skin temperature
•  Heat loss
•  Pulse rate
•  Various heart rate variability (HRV) components
•  Movement
•  Bioimpedance
•  Sleep (duration, deep/light, REM/non-REM)
•  Perfusion
Generating big data sets is the basis for
Ava’s AI approach
THE BRACELET
7
The Ava bracelet is a Class 1
registered medical device
Juli16
Aug.16
Sep.16
Okt.16
Nov.16
Dez.16
Jan.17
Feb.17
März17
Apr.17
Mai17
Juni17
Juli17
Aug.17
Sep.17
Okt.17
Nov.17
Dez.17
Jan.18
Feb.18
März18
9
1.  AVA – The vision and solution
2.  The basics - Why fertility tracking works
3.  Algorithms – How the methods evolves with the data
4.  Future – the challenges ahead
CONTENTS
PresentationforXXX|August10,2017|Strictlyconfidential
10
HORMONAL CHANGES
Average woman:
Cycle length: 28 days
Luteal length: 13 days
Averages apply to groups
not individuals!
Ava’s measured signals correlates with progesterone and estradiol
11
Five different algorithms operating at different phases of the cycle
ALGORITHMS FOR FERTILITY TRACKING
High Peak
High
High
Peak
I
II
III
IV
PeakV
Peak
Peak
Peak
PeakHigh
PeakHigh
Priors
Priors Signals
Priors Signals
Signals
Priors Signals
12
OUR DATA
•  Every night 3 million data
points are collected
•  Too little reference labels
Too much data!
13
Features: e.g. average temperature during the
night
Currently our algorithms are based on these
features
Calculate daily features to reduce the complexity of nightly data
OUR DATA
14
1.  AVA – The vision and solution
2.  The basics - Why fertility tracking works
3.  Algorithms – How the methods evolves with the data
4.  Future – the challenges ahead
CONTENTS
PresentationforXXX|August10,2017|Strictlyconfidential
Clinical data
15
Before launch – clinical data only
EARLY ALGORITHMS
Signals
LiteratureSignal
statistics
Woman
specific
info
Clinical data
16
New data from Ava users & clinical
study coming in every day!
TRANSITION TO AI
Signals
LiteratureSignal
statistics
Woman
specific
info
AI
Clinical & user data
Signals Woman
specific
info
17
WORKFLOW – RELEASED ALGO
Machine learning with 1’000 cycles
Temperature
HR
Feature 1
Feature 2
Raw data Signal features Engineered features Classifier
18
WORKFLOW – IN DEVELOPMENT
Machine learning with 10’000+ cycles
Temperature
HR
Feature 1
Feature 2
Raw data Signal features Engineered features Classifier
In theory
19
Better performance
Easier to include information from many sources
Faster development
Capture more variations
The more data we collect – the better we become
WHY GO FOR AI
AI
20
1.  AVA – The vision and solution
2.  The basics - Why fertility tracking works
3.  Algorithms – How the methods evolves with the data
4.  Future – the challenges ahead
CONTENTS
PresentationforXXX|August10,2017|Strictlyconfidential
21
WORKFLOW – COMING SOON
End-to-end learning: Same data but better infrastructure needed
Temperature
HR
Feature 1
Feature 2
Raw data Signal features Engineered features Classifier
22
Good algorithms isn’t everything
CHALLENGES: PRODUCT
User behavior
•  Users wear the bracelet only a few days per
cycle out of personal curiosity
•  Users wear the bracelet only during the fertile
window
•  Users wear the bracelet diligently in general,
but not for the first week of a cycle
•  Missing data due to other reasons
23
How to be more than a hype
CRITICAL POINTS FOR FUTURE OF DIGITAL HEALTH
Solve real problems and bring a real value to the customer
Products need to be based on solid science and validated by clinical studies
THANK YOU!
24

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Ava Women’s Health Director Details AI Advancements

  • 1. Ava Women’s health Lisa Falco, Director of Data Science Geek Girl’s Carrot, 2018.10.27
  • 2. 2 •  MSc in Engineering Physics, Chalmers, Sweden, 2001 •  PhD in Biomedical image analysis, EPFL, Switzerland, 2006 •  Solianis Monitoring, Zürich, 2006 - 2011 •  Data scientist in the algorithm development team •  Non-invasive glucose monitoring •  Scanco Medical, Zürich, 2011 - 2015 •  Product manager •  micro-Computer Tomography •  Joined AVA in 2015 as Director of Data Science MY WAY TO AVA
  • 3. 3 1.  AVA – The vision and solution 2.  The basics - Why fertility tracking works 3.  Algorithms – How the methods evolves with the data 4.  Future – the challenges ahead CONTENTS
  • 4. 4 Women face various health challenges along their reproductive life stages THE CHALLENGE Understand your body 33% Trying to conceive 10% PregnancyContraception 33% Menopause 60 years20
  • 5. Ava is a long-term companion for women, giving them data-driven and scientifically proven insights along all stages of their reproductive lives. OUR VISION 5
  • 6. Ava is the world’s first artificial intelligence (AI) platform in women’s health 6 TODAY’S SOLUTION Bracelet App Big data / AI
  • 7. •  Breathing rate •  Skin temperature •  Heat loss •  Pulse rate •  Various heart rate variability (HRV) components •  Movement •  Bioimpedance •  Sleep (duration, deep/light, REM/non-REM) •  Perfusion Generating big data sets is the basis for Ava’s AI approach THE BRACELET 7 The Ava bracelet is a Class 1 registered medical device
  • 9. 9 1.  AVA – The vision and solution 2.  The basics - Why fertility tracking works 3.  Algorithms – How the methods evolves with the data 4.  Future – the challenges ahead CONTENTS PresentationforXXX|August10,2017|Strictlyconfidential
  • 10. 10 HORMONAL CHANGES Average woman: Cycle length: 28 days Luteal length: 13 days Averages apply to groups not individuals! Ava’s measured signals correlates with progesterone and estradiol
  • 11. 11 Five different algorithms operating at different phases of the cycle ALGORITHMS FOR FERTILITY TRACKING High Peak High High Peak I II III IV PeakV Peak Peak Peak PeakHigh PeakHigh Priors Priors Signals Priors Signals Signals Priors Signals
  • 12. 12 OUR DATA •  Every night 3 million data points are collected •  Too little reference labels Too much data!
  • 13. 13 Features: e.g. average temperature during the night Currently our algorithms are based on these features Calculate daily features to reduce the complexity of nightly data OUR DATA
  • 14. 14 1.  AVA – The vision and solution 2.  The basics - Why fertility tracking works 3.  Algorithms – How the methods evolves with the data 4.  Future – the challenges ahead CONTENTS PresentationforXXX|August10,2017|Strictlyconfidential
  • 15. Clinical data 15 Before launch – clinical data only EARLY ALGORITHMS Signals LiteratureSignal statistics Woman specific info
  • 16. Clinical data 16 New data from Ava users & clinical study coming in every day! TRANSITION TO AI Signals LiteratureSignal statistics Woman specific info AI Clinical & user data Signals Woman specific info
  • 17. 17 WORKFLOW – RELEASED ALGO Machine learning with 1’000 cycles Temperature HR Feature 1 Feature 2 Raw data Signal features Engineered features Classifier
  • 18. 18 WORKFLOW – IN DEVELOPMENT Machine learning with 10’000+ cycles Temperature HR Feature 1 Feature 2 Raw data Signal features Engineered features Classifier In theory
  • 19. 19 Better performance Easier to include information from many sources Faster development Capture more variations The more data we collect – the better we become WHY GO FOR AI AI
  • 20. 20 1.  AVA – The vision and solution 2.  The basics - Why fertility tracking works 3.  Algorithms – How the methods evolves with the data 4.  Future – the challenges ahead CONTENTS PresentationforXXX|August10,2017|Strictlyconfidential
  • 21. 21 WORKFLOW – COMING SOON End-to-end learning: Same data but better infrastructure needed Temperature HR Feature 1 Feature 2 Raw data Signal features Engineered features Classifier
  • 22. 22 Good algorithms isn’t everything CHALLENGES: PRODUCT User behavior •  Users wear the bracelet only a few days per cycle out of personal curiosity •  Users wear the bracelet only during the fertile window •  Users wear the bracelet diligently in general, but not for the first week of a cycle •  Missing data due to other reasons
  • 23. 23 How to be more than a hype CRITICAL POINTS FOR FUTURE OF DIGITAL HEALTH Solve real problems and bring a real value to the customer Products need to be based on solid science and validated by clinical studies