About the webinar The entity that has caused a newfound global love of hand sanitizers and masks? The Coronavirus (known as ‘2019-nCov’ or ‘Covid-19), which has infected about 5,00,000 people globally within a few months! According to the WHO: 'In the most severe cases, the infection can cause pneumonia, severe acute respiratory syndrome, and even death.' Statements like these beg the question: 'How accurate are the tests to spot the disease?' 'Can AI assist in giving a more accurate diagnosis?' The AI Model generated via Skyl.ai’s deep learning platform can accurately detect COVID-19 through patterns in X-ray scans and differentiate it from community-acquired pneumonia and other lung diseases that may otherwise be overlooked by a doctor. Through this webinar, we will demo how AI can be used to test the Covid19 infections, and help in treating patients with critical conditions quickly. What you'll learn - How healthcare institutions can leverage AI to detect COVID-19 and reduce the time taken to provide critical care to patients who are affected. - Discuss the approach to automate the machine learning workflow, creating and deploying models in hours and not weeks or months. - Demo: How to create an ML model that can detect COVID-19 from chest x-rays using Skyl.ai.

1. AI in Healthcare
Can AI help us in diagnosing
Coronavirus?

2. Technology leader with 20+ years expertise in Product
Development, Business strategy and Artificial Intelligence
acceleration. Active contributor in the New York AI
community.
Extensively worked with global organizations in BFSI,
Healthcare, Insurance, Manufacturing, Retail and
Ecommerce to define and implement AI strategies.
Nisha Shoukath
Co-founder,
People10 & Skyl.ai
The Speaker

3. Extensive experience building future tech products using
Machine Learning and Artificial Intelligence.
Areas of expertise includes Deep Learning, Data Analysis,
full stack development and building world class products
in ecommerce, travel and healthcare sector.
Shruti Tanwar
Lead - Data Science
The Speaker

4. Bikash Sharma
CTO and Co-founder
at Skyl.ai
CTO & Software Architect with 15 years of experience
working at the forefront of cutting-edge technology
leading innovative projects.
Areas of expertise include Architecture design, rapid
product development, Deep Learning and Data Analysis.
The Panelist

5. Getting familiar with ‘Zoom’
All dial-in participants will be muted to enable the presenters
to speak without interruption
Questions can be submitted via Zoom Questions chat
window and will be addressed at the end during Q&A
The recording will be emailed to you after the webinar
Please familiarize yourself with the Zoom ‘Control Panel’ on your screen

6. Live Demo of
detecting COVID-19
from chest X-rays using
AI and Computer Vision
Why AI &
Computer Vision
is important in
Covid-19 diagnosis
How to quickly
overcome the
challenges in building
ML models
1 2 3
In the next 45 minutes

7. Machine Learning automation platform for unstructured data
A quick intro about Skyl.ai
Guided Machine Learning Workflow
Build & deploy ML models faster on
unstructured data
Collaborative Data Collection & Labelling
Easy-to-use & scalable AI SaaS platform

8. POLL #1
At what stage of Machine learning adoption your
organization is at?
⊚ Exploring - Curious about it
⊚ Planning - Creating AI/ML strategy
⊚ Experimenting - Building proof of concepts
⊚ Scaling up - Some departments are using it
⊚ In production - Using it in product features
⊚ Transforming - AI/Ml driven business

9. How healthcare centers can use
AI to quickly and accurately
detect COVID-1901

10. > 2.6 Million
Confirmed cases of
COVID-19 worldwide
8/1000 tests as
Global average in a nation
60-80%
Perceptual error estimate
300,000+
People tested daily
using different
measures
Distinguishing COVID-
19 from similar conditions like
conventional pneumonia,
respiratory infections etc.
Large number of
affected
patients
Shortage of tests
and capacity
Complex and
error-prone data
Severe shortage of
reagents and chemicals
required for RT-PCR tests
Coronavirus Testing - Challenges now and ahead

11. Handle large volume of Data
● Learn automatically from previous diagnosis and continuously
improve
● Streamline and automate the repetitive tasks
Faster processing to aid human experts
● Identify features in images quickly and precisely
● Aid human experts to be more efficient and focus on value-add areas
Eliminate human errors
Machine Learning to the rescue!

12. Machine Learning and Computer vision
Machine learning is an application of artificial intelligence (AI) that provides
systems the ability to automatically learn and improve from experience
without being explicitly programmed.
Computer vision is the branch of machine learning specializing in how
computers “see” photos and videos.
Hence, Medical imaging is treated as Computer Vision
problems in the AI world
Hence, Medical imaging is treated as Computer Vision problem in
the AI world

13. Analysing Brain
Anomalies
Identifying Fractures
& Musculoskeletal injury
Detecting Cardiovascular
Abnormalities
Machine Learning can
flag images that offer
risk ratios that the
images contain
evidence of ALS or PLS
etc.
AI can help in identifying
hard-to-see fractures,
dislocations, or soft
tissue injuries helping
doctors to avoid
misdiagnosis
Computer Vision can
help in Cardiac
segmentation that
helps heart surgeons
to be minimally
invasive in diagnosis
and treatments

14. Coronavirus Diagnosis

15. Chest X-Ray (CXR)
● For preliminary classification, due to its prevalent usage as a primary diagnostic test
● Use of CXR for COVID-19 diagnosis, or triage for patient management has become an
important issue to preserve limited medical resources
Computed Tomography Scan (CT Scan)
● One of the most promising techniques that might lift some of the heavy weight of the
physicians’ shoulders
● Distinguishing COVID-19 from community acquired pneumonia based on chest CT
claims a sensitivity and specificity of 90% and 96% respectively[1]
[1] Li L, Qin L, Xu Z et al. Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT. Radiology. Radiological Society of North
America; 2020;200905.
AI Diagnostic Pathways for Covid-19

16. Left : An example of a CXR with a label of COVID-19.
Right : An example of a CXR labeled as showing
evidence of pneumonia.
Using Deep Learning in CXR and CT scan imaging
Availability of CXRs, especially in
rural and isolated areas, including
the fact that mobile X-rays are
commonly used, makes using CXR
for a preliminary classification of
COVID-19 worth pursuing.
A deep learning model can
accurately detect COVID-19 from
CT scans. CT scans, however, are
expensive and inaccessible in
many areas.
Figure : CT scan showing Covid-19 infection, red color
highlights the activation region

17. Detecting COVID-19 from
CXR
Radiology images are used to segment COVID-19
from conditions like pneumonia.
X-ray readings may resemble community
acquired pneumonia (bacterial) or other
respiratory infections.
An X-ray will show formations in the lung that
are associated with a number of respiratory
conditions including pneumonia.
AI can detect patterns specific to COVID-19
to provide accurate diagnosis.

18. Live Demo of detecting COVID-
19 using Artificial Intelligence
and Computer Vision02

19. 8 stages of Machine Learning workflow

20. We would like to credit Joseph Paul Cohen for collecting this data and
making it available as open source.
Github Repo https://github.com/ieee8023/covid-chestxray-dataset
By: Joseph Paul Cohen
Github: https://github.com/ieee8023
Website: http://josephpcohen.com
Sources:
https://radiopaedia.org/ (license CC BY-NC-SA)
https://www.sirm.org/category/senza-categoria/covid-19/
https://www.eurorad.org/ (license CC BY-NC-SA)
Credits

21. Live Demo of detecting
COVID-19 from chest X-rays
using AI and Computer Vision

22. POLL #2
Some challenges that you are facing while
implementing AI & Machine Learning
⊚ Not started yet, so no challenges
⊚ Data collection
⊚ Data Labeling
⊚ Large volumes of data
⊚ Identifying the right data set to
train
⊚ Data Security
⊚ Lack of knowledge of ML tools
⊚ Lack of end to end platform
⊚ Lack of expertise
⊚ Choosing the right algorithms

23. Advantages of a unified
platform Speed, Visibility,
Quality, Collaboration,
Flexibility, Security
03

24. Data Collection - Flexible options
(CSV bulk upload, APIs, Mobile capture, Form based…)

25. DataSecurity- on premise solutions
(encrypted data sources, access controlled flow..)

26. Data Labeling - Simple 4 steps process
(collaboration jobs, guided workflow…)

27. Data Labeling - Real-time early visibility
(class balance, missing data…)

28. Data Labeling - Early Visibility
(data frequency, data intuition, outliers, trends, labeling accuracy…)

29. Data Labeling with Effective Collaboration
(Job allocation, trend, statistics, interactive messaging…)
Analyse trends and progress
of your data labeling job in
real time with statistics and
interactive visualizations
Manage collaborator
progress, activity, interactive
messaging

30. Data Visualization to build strong data intuition
( visuals for data composition, data adequacy)

31. One click training at scale
(Easy feature sets, out of the box algorithms, API integration, hyper
parameter tuning, auto scaling…)
● Train, Deploy and Version your models
by creating feature-sets in no time with
our easy feature selection provision.
● Choose from state-of-art neural
network algorithms, tune
hyperparameters and see logs for
your training in real time.
● Integrate our powerful inference API
with your application for AI-driven
actionable intelligence.
● Auto scaling of model training based on
data and hyperparameters.

32. Model Monitoring of metrics in real-time
(inference count, execution time, accuracy…)
● Monitor your deployed
models and analyse
inference count, accuracy
and execution time.
● See how your models are
performing in real-time.
No black boxes here.

33. ● Monitor your deployed
models and analyse
inference count, accuracy
and execution time.
● See how your models are
performing in real-time. No
black boxes here.
Model Evaluation - Release Confidently
(Accuracy, Precision, Recall, F1 Score)

34. No upfront cost in Infrastructure set up
(no DevOps needed, auto-deploy, SaaS & On-prem models…)
No DevOps
required
01
Latest tech
stack
02
On premise
and saas
models
03
Scalable
On
demand
04

35. Skyl.ai - as ML automation platform

36. Offers for you!
1. Personalised demo
2. 15 days free trial with data credits
3. Complimentary consultation on pilot project
4. AI Implementation Playbook
www.skyl.ai contact@skyl.ai

37. Questions?
?

38. 85 Broad Street, New York, NY, 10004
+1 718 300 2104, +1 646 202 9343
contact@skyl.ai
Thank you for joining!
We hope to hear from you soon