Dr. Shalini Ananda discusses Quantified Skin's personalized artificial intelligence health platform that builds a tailored guidance plan that engages with a user everyday to help them achieve optimal health. This platform utilizes the core technology called NuSilico™, which incorporates several layers of decision making using Deep learning methods. NuSilico™ incorporates user-generated data including selfies, Apple HealthKit integration and the peer-reviewed scientific study results to generate user-specific guidance to improved metabolism and skin health. This presentation was presented at the SF Artificial Intelligence in Health meetup at UCSF. ----- For more information please contact Dr. Shalini Ananda at shalini@quantifiedskin.com

1. Artificial Intelligence in Health
Shalini Ananda, PhD
shalini@quantifiedskin.com
http://quantifiedskin.com1061 Market St. #511 San Francisco, CA 94103

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Deep Learning Teaches Computers to Think
Our brain’s neocortex utilizes layers to create
representation from low level inputs to
transform them into meaningful assertions.
Deep learning enables computers to do the
same.

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Deep Learning Assists in Feature Engineering
Raw Data
Feature
Extractor
Features
Algorithm
(machine learning)
(train or predict)

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Deep Learning Assists in Feature Engineering
Raw Data
Feature
Extractor
Features
Algorithm
(machine learning)
(train or predict)
Deep learning

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Easy to Tell These Are Cookies

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Necessary to Have a Deep Understanding of Material Science

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What If a Computer Could Think Like a Researcher?

8. Published experiments contain magnitudes of errors, but there are meaningful patterns if you know what to look for
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We Can Teach Computers to Learn the Relevant Scientific Data
Ever increasing
number of
experiments

9. Experimentation steps
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Humans Use a Logical Approach to Experimentation
1
Elucidating the role of a
material or molecular
system
i.e.
Targeting and sustained
release
2
Estimating the effect of a
material system in the
state of action
i.e.
Biodistribution and half-life
3
Design of material
Run experiment
Prepare for next iteration

10. Human’s can’t physically test all possibilities
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Need for Reducing Irrelevant Experiments
Experiments must factor characteristics such
as:
•Targeting modalities
•Charge modifications
•Shape, and more…
If there are 10 nanoparticles and 6 possible
parameters to change and 43 variants; that’s
over:
10258
experiments
Not enough time to run every experiment.
Smart materials
Lets use nanoparticles as an example

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Our Validation Approach
1
We outreached to
researchers working on
smart materials.
The researchers provided us
characterization input data.
Three step process
We conducted 152 blind experiments with 31 institutions.
A few of the institutions we worked with

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Our Validation Approach
1
We outreached to
researchers working on
smart materials.
The researchers provided us
characterization input data.
2
Our team ran the
characterization inputs
through our deep learning
engine, NuSilico™.
We predicted biodistribution
and half-life outputs.
3
Researchers ran their
experiments
(avg. length 4 months).
We compared NuSilico™’s
outputs to the researcher’s
experimental data.
Three step process

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Our Results - Our Predictions vs Observed Empirical Experiments
From 152 experiments with 31 institutions
R2
= 0.9370 R2
= 0.9656

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Deep Learning Is Better for Feature Selection than SVM
recall: 61.40
precision: 66.80
AUC: 84.01
SVM
polynomial kernel
deep learning
recall: 76.80
precision: 88.10
AUC: 93.70
In terms of sensitivity/recall, ROC, and precision where the laplacian score was utilized as the feature selection method for
building the model. We compare SVM polynomial kernel to deep learning

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Our Simulations Produced Accurate Models in a Fraction of the Time
We have repeatably demonstrated an order of magnitude time improvement using deep learning
Over 39 months
Just 1 month
Current researching methods
What we’ve demonstrated with deep learning

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Building Personalized Therapies - Systematically
1
Determine the target
cells.
What are you trying to
achieve physically?
Three step process
2
Understand the building
blocks.
Train the computer to test
outcomes of experiments.
3
Use deep learning to
simulate and rank the best
designs.
Test orders of magnitude
of experiments to find the
most optimal design path.

17. Thank You