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GloFlow
Lessons Learned
From Slide Digitization technology To Response Prediction For Cancer Treatment
126 interviews
Anirudh Joshi
Hustler
Damir Vrabac
Picker
Viswesh Krishna
Hacker
Pranav Rajpurkar
Designer
Nicky Kamra
Mentor
Our background
Anirudh Joshi
M.S. Computer Science
Built technologies @
Damir Vrabac
M.S. Electrical Engineering
Startup Founder +
AI in Industry
Viswesh Krishna
B.S. Computer Science
Pranav Rajpurkar
Ph.D. Computer Science
Startup founder on phone-
based eye care screening
36 publications (8500+
citations) in AI + Medicine
Instructed Coursera AI for
medicine to 40k+ students
For pathology labs who have
difficult cases who find mailing
physical slides for secondary
consults too slow and
expensive. GloFlow was a
platform that enables digital
secondary consults with
expert pathologists.
Week 1-2: External Consultations
make Digital Pathology useful.
HIPAA compliant
cloud storage
Pathology Lab Academic
Center
1. Pathology labs were very interested and wanted to use this platform.
2. Academic Center had no immediate incentive to move digital.
3. Two sided marketplace not possible with only one endpoint.
💡
Week 3: Pivot to AI tools for Pathology labs.
Lessons 💡
1. Pre-clinical studies have few
barriers to entry
2. AI can speed up existing
workflows and identify new
features
3. Current tools satisfy needs
Rare Event
Detection
What task would you like to perform?
Tissue
Quantification
Cell
Segmentation
Tissue
Segmentation
Went back to the drawing board -> Pivot
For pathology labs who need to provide
insights to customers, GloFlow is a suite of
AI tools for preclinical tasks that enables
accurate scalable insights without needing
an in house AI team and identifies new
features not detected by current AI tools.
Week 4: Pivot to AI Pathology tools for Genomics
companies.
Lessons 💡
1. Quality Control in Genomic
workflows could use AI tools to
scale up
2. Mistakes in Quality Control can
seriously impact downstream
sequencing tasks
3. It was cost-effective to simply
sequence everything
On your selected patch:
Tumor purity = 60%
Epithelial cell count = 5
Region of interest on whole
slide image fraction:
25%
Inflammation score
6 of 10
For heads of product and R&D for
genomic companies who need to
automate high volume pathology tasks,
GloFlow is a platform
that uses AI to scale pathology tasks.
Week 5-6: Virtual Genomic Sequencing from slides
could identify drug targets.
HCC
CRC
NSCLC
SAMPLE_1
SAMPLE_2
SAMPLE_3
CTNNB1 FMN2 TP53
Key Mutation Prediction Molecular Pathway Prediction
Lessons 💡
1. Virtual Genomic Sequencing was was
a vitamin (not a painkiller)!
2. To motivate pharma to share data
and initiate pilots, we needed to
complete the value chain and make
the value prop more significant.
Mutation Prediction helps identify drug
targets for pharma companies.
Research points to there being a signal in
slides for spatial expression prediction.
The slides will be available for diagnosis for
years to come.
Biomarker discovery & patient selection with Pharma
- “If this tool could be applied to ovarian tumor and identify subtypes of patients
that didn’t respond that could be a path of proposing a combination of
checkpoint inhibitor therapy”
- - Head of Computational Biology Oncology R&D, Pfizer
Is Cost a Value Prop:
- “Given clinical trials are millions of dollars, pharma is not going to skip out on
cost for the patients. Technologies are likely more valuable for other
purposes.” - Professor of BMDS at Stanford
Week 7: Predicting response to treatment instead
of virtual molecular test may be more useful!
MVP: Multimodal Patient Data to Response
Prediction with Analysis
Upload
Patient Data
H&E Slide
Genomics
Likelihood of
patient
responding to
drug
Explaining why
the patient may
not respond to
the drug
Biomarker discovery & patient selection with Pharma
- “Focus on having the largest effect size, not the number of patients with a
disease. Unless you’re a Pfizer or Merck, it doesn’t matter if you have a
relatively smaller patient population” - CEO of AI biotech
What the technology needs to show?
- “Show me a marker of resistance, and show me a marker of sensitivity (will
respond to chemo but not another treatment type)” - medical oncologist
studying genomics of cancer, UCSF
Week 8: Be careful what cancer you choose, but clear
value if you are able to make these predictions
Week 1-2: Digital Pathology
Consult Platform
Week 3: Pre-
clinical AI tools
Week 4: Quality
Control for Genomics
Week 5-6: Virtual
Genomic Sequencing
Week 7-8: Response
Prediction for Oncology
What’s Next
Summer
Data Partner
Partner to build
data / tech
Non-binding
agreements for
Pharma
Fall
Seed Round
Results from first
pilot
First Pharma
contract
Incorporation
June 14th
Moving Forward
Anirudh
Joshi
Damir
Vrabac
Viswesh
Krishna
Pranav
Rajpurkar
Pear Accelerator over the Summer! Assistant Professor, Harvard
Full-time Part-time
Contact us at: founders@gloflow.ai
Every piece of our business model canvas changed
Pathology labs at
hospitals and private
practices who need
assistance on
difficult cases.
Academic centers
who want to reduce
administrative cost
associated with
consults.
Integration with
current medical
record systems
Academic pathology
labs on the platform
Validated AI models
for diagnosis
assistance
Problem:
Secondary
consultations in
pathology involve
physically mailing
slides which takes a
long time due to
administrative and
logistical overhead
Needs:
Platform for on
demand secondary
expert consults.
Access to AI
assistance for
diagnosis
Hospital Pathology
Labs
Pathology
Reference Labs
Academic Hospitals
AI Pathology
Companies
Whole Slide
Scanning
Companies
Salaries for employees
Cloud compute for software platform and AI inference
Regulatory validation trials
Marketing for conference sponsorship
Percentage of the secondary consultation fee for
facilitating the consult (similar to marketplace
model).
Subscription fee for using AI assistance
Medical Conferences
Website
Professional
Associations
Expert pathologists
at academic centers
Software platform
Annotated data for
AI
As a B2B company,
our customers
expect an
enterprise product
with emphasis on
reliability, support
and privacy.
Predict key mutations
directly from H&E
slides for for either
sequencing,
retrospective analysis
and developing
targeted therapeutics
Direct: Pharma &
Genomic companies
Heads of Oncology
at Pharma
Companies
Pharma contracts
Compute
Annotated Data
Integrate into
pathology workflows
at pharma co.
Train AI solutions
Head of Oncology at
Pharma
Providing our
customers with
higher quality
outputs with
increased usage
CDx reimbursement
Head of Biomarker
Discovery at Pharma
Companies
Head of Biomarker
Discovery at Pharma
company
Grow portfolio of
cancers targeted
Response prediction of
patients based on
multimodal data (H&E +
genomics)
AACR, SITC
Drug specific
models for response
prediction
Salaries for
employees
Cloud Compute
Dataset purchases
We came in with a research lens, and are
walking out with a broader lean launchpad lens
“Be bold in your vision, the future looks very different from today.
Be the one to rethink it.”
“Iterate! You don’t need to build a product to validate the market.
Ask questions, present clear MVPs, and make quick iterations
before building.”
Thank you to the teaching staff, and especially to our
mentors Steve Blank and Nicky Kamra, for extremely
valuable steering each week.
Gained new
insights
every week

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Gloflow Engr245 2021 Lessons Learned

  • 1.
  • 2. GloFlow Lessons Learned From Slide Digitization technology To Response Prediction For Cancer Treatment 126 interviews Anirudh Joshi Hustler Damir Vrabac Picker Viswesh Krishna Hacker Pranav Rajpurkar Designer Nicky Kamra Mentor
  • 3. Our background Anirudh Joshi M.S. Computer Science Built technologies @ Damir Vrabac M.S. Electrical Engineering Startup Founder + AI in Industry Viswesh Krishna B.S. Computer Science Pranav Rajpurkar Ph.D. Computer Science Startup founder on phone- based eye care screening 36 publications (8500+ citations) in AI + Medicine Instructed Coursera AI for medicine to 40k+ students
  • 4. For pathology labs who have difficult cases who find mailing physical slides for secondary consults too slow and expensive. GloFlow was a platform that enables digital secondary consults with expert pathologists. Week 1-2: External Consultations make Digital Pathology useful. HIPAA compliant cloud storage Pathology Lab Academic Center 1. Pathology labs were very interested and wanted to use this platform. 2. Academic Center had no immediate incentive to move digital. 3. Two sided marketplace not possible with only one endpoint. 💡
  • 5. Week 3: Pivot to AI tools for Pathology labs. Lessons 💡 1. Pre-clinical studies have few barriers to entry 2. AI can speed up existing workflows and identify new features 3. Current tools satisfy needs Rare Event Detection What task would you like to perform? Tissue Quantification Cell Segmentation Tissue Segmentation Went back to the drawing board -> Pivot For pathology labs who need to provide insights to customers, GloFlow is a suite of AI tools for preclinical tasks that enables accurate scalable insights without needing an in house AI team and identifies new features not detected by current AI tools.
  • 6. Week 4: Pivot to AI Pathology tools for Genomics companies. Lessons 💡 1. Quality Control in Genomic workflows could use AI tools to scale up 2. Mistakes in Quality Control can seriously impact downstream sequencing tasks 3. It was cost-effective to simply sequence everything On your selected patch: Tumor purity = 60% Epithelial cell count = 5 Region of interest on whole slide image fraction: 25% Inflammation score 6 of 10 For heads of product and R&D for genomic companies who need to automate high volume pathology tasks, GloFlow is a platform that uses AI to scale pathology tasks.
  • 7. Week 5-6: Virtual Genomic Sequencing from slides could identify drug targets. HCC CRC NSCLC SAMPLE_1 SAMPLE_2 SAMPLE_3 CTNNB1 FMN2 TP53 Key Mutation Prediction Molecular Pathway Prediction Lessons 💡 1. Virtual Genomic Sequencing was was a vitamin (not a painkiller)! 2. To motivate pharma to share data and initiate pilots, we needed to complete the value chain and make the value prop more significant. Mutation Prediction helps identify drug targets for pharma companies. Research points to there being a signal in slides for spatial expression prediction. The slides will be available for diagnosis for years to come.
  • 8. Biomarker discovery & patient selection with Pharma - “If this tool could be applied to ovarian tumor and identify subtypes of patients that didn’t respond that could be a path of proposing a combination of checkpoint inhibitor therapy” - - Head of Computational Biology Oncology R&D, Pfizer Is Cost a Value Prop: - “Given clinical trials are millions of dollars, pharma is not going to skip out on cost for the patients. Technologies are likely more valuable for other purposes.” - Professor of BMDS at Stanford Week 7: Predicting response to treatment instead of virtual molecular test may be more useful!
  • 9. MVP: Multimodal Patient Data to Response Prediction with Analysis Upload Patient Data H&E Slide Genomics Likelihood of patient responding to drug Explaining why the patient may not respond to the drug
  • 10. Biomarker discovery & patient selection with Pharma - “Focus on having the largest effect size, not the number of patients with a disease. Unless you’re a Pfizer or Merck, it doesn’t matter if you have a relatively smaller patient population” - CEO of AI biotech What the technology needs to show? - “Show me a marker of resistance, and show me a marker of sensitivity (will respond to chemo but not another treatment type)” - medical oncologist studying genomics of cancer, UCSF Week 8: Be careful what cancer you choose, but clear value if you are able to make these predictions
  • 11. Week 1-2: Digital Pathology Consult Platform Week 3: Pre- clinical AI tools Week 4: Quality Control for Genomics Week 5-6: Virtual Genomic Sequencing Week 7-8: Response Prediction for Oncology
  • 12. What’s Next Summer Data Partner Partner to build data / tech Non-binding agreements for Pharma Fall Seed Round Results from first pilot First Pharma contract Incorporation June 14th
  • 13. Moving Forward Anirudh Joshi Damir Vrabac Viswesh Krishna Pranav Rajpurkar Pear Accelerator over the Summer! Assistant Professor, Harvard Full-time Part-time Contact us at: founders@gloflow.ai
  • 14. Every piece of our business model canvas changed Pathology labs at hospitals and private practices who need assistance on difficult cases. Academic centers who want to reduce administrative cost associated with consults. Integration with current medical record systems Academic pathology labs on the platform Validated AI models for diagnosis assistance Problem: Secondary consultations in pathology involve physically mailing slides which takes a long time due to administrative and logistical overhead Needs: Platform for on demand secondary expert consults. Access to AI assistance for diagnosis Hospital Pathology Labs Pathology Reference Labs Academic Hospitals AI Pathology Companies Whole Slide Scanning Companies Salaries for employees Cloud compute for software platform and AI inference Regulatory validation trials Marketing for conference sponsorship Percentage of the secondary consultation fee for facilitating the consult (similar to marketplace model). Subscription fee for using AI assistance Medical Conferences Website Professional Associations Expert pathologists at academic centers Software platform Annotated data for AI As a B2B company, our customers expect an enterprise product with emphasis on reliability, support and privacy. Predict key mutations directly from H&E slides for for either sequencing, retrospective analysis and developing targeted therapeutics Direct: Pharma & Genomic companies Heads of Oncology at Pharma Companies Pharma contracts Compute Annotated Data Integrate into pathology workflows at pharma co. Train AI solutions Head of Oncology at Pharma Providing our customers with higher quality outputs with increased usage CDx reimbursement Head of Biomarker Discovery at Pharma Companies Head of Biomarker Discovery at Pharma company Grow portfolio of cancers targeted Response prediction of patients based on multimodal data (H&E + genomics) AACR, SITC Drug specific models for response prediction Salaries for employees Cloud Compute Dataset purchases
  • 15. We came in with a research lens, and are walking out with a broader lean launchpad lens “Be bold in your vision, the future looks very different from today. Be the one to rethink it.” “Iterate! You don’t need to build a product to validate the market. Ask questions, present clear MVPs, and make quick iterations before building.” Thank you to the teaching staff, and especially to our mentors Steve Blank and Nicky Kamra, for extremely valuable steering each week. Gained new insights every week