Using a data science approach, we estimated sales forecast for Titan Medical, a public, pre-revenue medical robotics company

1. J u n e 2 0 1 6
Market Analysis

2. 1
This presentation contains“forward-looking statements” which reflect the current expectationsof management of the
Company’s future growth, results of operations, performanceand business prospectsand opportunities. Wherever possible,
words such as “may”, “would”, “could”, “will”, “anticipate”, “believe”, “plan”, “expect”, “intend”, “estimate”, “potential for” and similar
expressions havebeenusedto identifythese forward-looking statements. These statements reflect management’s current
beliefswithrespect to future market potential and are basedon informationcurrently availableto management. Forward-looking
statements involvesignificant risks, uncertainties andassumptions. Manyfactors or related assumptions couldcause the
Company’s actual results, performance or achievements to be materiallydifferent from any future results, performanceor
achievements that may be expressed or implied by suchforward-looking statements, including, without limitation, thoserisks
and assumptions listed in the “RiskFactors” section of the Company’sAnnual Information Form dated March 30, 2016 (which
may be viewed at www.sedar.com). Should one or more of these risksor uncertainties materialize, or should assumptions
underlying the forward lookingstatements prove incorrect, actual results, performanceor achievements may vary materially
from those expressedor impliedby the forward-lookingstatements containedin thispresentation. These factorsshouldbe
consideredcarefully, and prospectiveinvestors shouldnot place unduereliance on the forward-looking statements.Althoughthe
forward-lookingstatements contained inthe presentation are baseduponwhat management currently believesto be reasonable
assumptions, the Companycannot assure prospectiveinvestors that actual results, performanceor achievements will be
consistent with theseforward-looking statements. This presentation does not constitute an offer to sell anyclass of securitiesof
the Company in any jurisdiction.
Forward Looking Statements, Risks, and Assumptions

3. 2
• American Hospital Directory (AHD)
• Centers for Medicare & Medicaid Services (CMS)
• Intuitive Surgical Annual Reports
• da Vinci Surgeon Locator
• Expert opinion provided by Reiza Rayman, President,
Founder, Titan Medical
Data Sources
The following sources were commonly used to inform the assumptions and analyses in this
document:

4. Limitations
Market Sizing
Executive Summary
Translating to Unit Sales
Conclusion

5. Limitations
Market Sizing
Executive Summary
Translating to Unit Sales
Conclusion

6. 5
• Titan has a market opportunity of approximately 1,120 healthcare facilities across
US and Europe
• 83% of facilities are in US, 17% in Europe
• 87% of US opportunity is in hospitals, 13% in Ambulatory Surgery Centers (ASCs)
• Expected 1,965 unit sales opportunity in 15-year forecast
• 73% of units are in US hospitals, 10% in US ASCs, and 17% in Europe
• 77% are new units, while 23% are replacement units
• Titan could pursue a highly targeted marketing strategy
• Targeting only 19% of US hospitals leads to a capture rate of 63% of target US hospital
market
• Targeting only 6% of ASCs leads to a capture rate of 40% of target ASC market
• 1st year sales have material impact on 15-year forecast, underscoring importance of starting
year sales
• Replacements and 2nd unit sales comprise 30% of cumulative sales, underscoring
importance of ongoing relationships with healthcare facilities
Executive Summary
Executive Summary

7. 6
A number of sales channels were considered in this analysis Market Sizing
Markets segments considered Types of sales considered
US Hospitals
• Approximately 5,600 hospitals considered across the US
• Data source: AHD1, CMS2, Intuitive Surgical
US ASCs
Europe
• Approximately 4,300 ASCs considered across the US
• Data source: CMS2
• Opportunity modeled after Intuitive sales in Europe
• Data source: Intuitive Surgical
1st Unit Sales
• First-time SPORT sale to a healthcare facility
2nd Unit Sales
Replacements
• Sales to healthcare facilities that want a second
operational SPORT unit
• Replacement sales for 1st and 2nd unit sales
Cross-section of all sales channels considered
1. American Hospital Directory
2. Center for Medicare & Medicaid Services
1st Unit Sales 2nd Unit Sales Replacements
US Hospitals x x x
US ASCs x – x
Europe x – x
Assumed no
2nd unit sales
in ASCs and
Europe

8. Limitations
Market Sizing
Executive Summary
Translating to Unit Sales
US Hospitals
US ASCs
Europe
Conclusion

9. 8
Titan can target 1,118 hospitals globally
Overview
Opportunity
(# hospitals)
US
Hospitals
US
ASCs
Europe
Insights
• Developed 2 models to
estimate target market
size
• Considered
approximately 5,600 US
hospitals
744
181
193• Modeled after da Vinci’s
Europe sales
• Only international
segment considered
• Approximately 2/3rd of total
target market
• 3 factors are strong predictors
of target hospitals: teaching
status, revenue to price ratio,
procedure volumes
• Half of target hospitals are
concentrated in 10 states
• Considered
approximately 4,300
US ASCs
• Approximately 1/6th of total
target market
• 57% of targeted hospitals are
concentrated in 10 states
• Approximately 1/6th of total
target market
Breakdown by Segment
Market Sizing
Total: 1,118
US
Hospitals
67%
US ASCs
16%
Europe
17%

10. Market Sizing
Executive Summary
US Hospitals
US ASCs
Europe
Limitations
Translating to Unit Sales
Conclusion

11. 10
Adjusted for SPORT’s
price and capabilities
Regression
Model
Projected Outcomes
744 potential target hospitals
All predictors were statistically
significant (P Value < 0.001)
Highly accurate
(Area under the curve = 0.93)
Trained on da Vinci
machine market data
Robust analysis,
7 predictors used
Utilization
Model
Projected Outcomes
Based on procedure data
for 5,661 US Hospitals
4 factors analyzed to
determine machine utilization
Identified hospitals with
sufficient procedure volumes
1025 potential target hospitals
40% of potential hospitals
do not own a da Vinci
Sensitivity ranges from 909 to 1166
US Hospitals
US hospital market was estimated using two models
Model 1: Estimation Model 2: Validation

12. 11
LinkageData Aggregation Training
Ran a logistic regression using 7 factors;
adjusted for SPORT’s factors to calculate
a purchase probability for each hospital
Records were
comprehensive90%
Databases were joined through
probabilistic record linkage; missing
values were imputed or found through
research
Missing 10% of data were imputed
Imputing methods
Regional Income Data
Mean State income was used for
missing records
For-Profit Status
Missing records were assumed to
be non-profit (more conservative)
Aggregated 23 factors from four databases
Staffed Beds
Total Discharge
Patient Days
Gross Patient Revenue
APC1 Number
APC Number of Claims
APC Units of Service
APC Total Charges
APC Total Cost
APC Total Payment
DRG2 Code
DRG Total Cases
DRG Total Charges
DRG Total Cost
DRG Total Payment
Regional Population
Hospital
Intuitive Surgeon Name
Surgeon’s Specialisations
Surgeon’s Employers
Educational Facility For Profit Status
Regional Median Income
SPORT surgeries considered
US Hospitals –
Model IModel I Methodology
Endometriosis resection Ventral hernia repair
Benign hysterectomy
Cholecystectomy
Inguinal hernia repair
Colorectal procedures
Adjusted for SPORT capabilities and
price at each hospital
1. APC = Ambulatory Payment Classification (Classification for out-patient hospital procedures)
2. DRG = Diagnostic Related Group (Classificationfor in-patienthospital procedures)
Developed relevantfactors using original
factors as well as ratios of original factors
Ran regression on dependantvariable:
does the hospital have a da Vinci
machine?
Market Potential Calculation
Determined probability of purchasing
a SPORT machine per hospital
Calculated market potential as sum of
purchasing probabilities across all
hospitals
1
2

13. 12
Assumptions Results
Upper case
• Will capture some da Vinci Hospitals
• Penalty applied to be conservative and
account for da Vinci’s existing capacity and
brand loyalty
Lower case
• Will not capture any da Vinci Hospitals
• Brand loyalty significantly influences hospital
decision making
Base case
• Simple average between lower and upper
cases
0
100
200
300
400
500
600
700
800
900
1000
Lower Base Upper
Numberofhospitals
Non da Vinci Hospitals da Vinci Hospitals
591
744
896
US Hospitals –
Model IModel I predicts a base case of 744 target hospitals

14. 13
Projected Hospitals Heat Map Distribution Analysis by State
Potential Hospitals – Top 10 States
89
69
54 48
40 37 37 32 29 26
0
20
40
60
80
100
CA TX FL NY PA IL OH NJ VA IN
Top 5 33%
Top 10 51%
Top 20 75%
Top 30 89%
Top 40 96%
80
70
60
50
40
30
20
10
744 Potential Hospitals (Base Case)
US Hospitals –
Model ITop 10 states comprise 50+% of target hospitals

15. 14
Market PotentialMethodology
Set usage thresholds
Projected SPORT procedure volumes
• Used hospital usage data to project
SPORT’s surgical volumes annually
Determined candidate hospitals
• Hospitals that surpassed the utilization
threshold were deemed candidate hospitals
1239
4422
407 618
• Based on hospital revenues, for-profit
status, teaching status, and da Vinci
ownership
Hospitals with da Vinci SPORT targeted hospitals
with da Vinci
Hospitals without da
Vinci
SPORT targeted hospitals
without da Vinci
• Market potential of 1,025 target hospitals; supports conservative market
estimate of Model I
• Model I was selected as primary model given greater number of factors
considered, simpler assumptions, and high accuracy of that model
US Hospitals –
Model IIModel II – Validation: predicts 1,025 target hospitals
14

16. Market Sizing
Executive Summary
US Hospitals
US ASCs
Europe
Limitations
Translating to Unit Sales
Conclusion

17. 16
Methodology
Market Sizing -
ASCsTitan can target 181 ASCs in the US
Assumptions
181 Potential
ASCs
Collected ASC data
• Used CMS database of approximately 4,300 ASCs containing
procedure volumes by surgical category
Calculated financial metrics for ASCs
• Using operating margin figures, estimated the average ASC’s
revenue assuming it had the same cost structure as hospitals
• Allocated revenue estimate using procedure volumes as a proxy
Matched to the nearest US hospital on revenue basis
• Calculated probability of purchase by matching ASC revenue to
hospital revenue
• Used k-NN algorithm to find 3 nearest hospital neighbors
• ASCs are assumed to require a minimum of approximately 230 surgeries
annually to qualify as a target
• ASC’s are a potential target because of SPORT’s low price tag

18. 17
Projected Hospitals Heat Map Distribution Analysis by State
Potential ASCs – Top 10 States
Top 5 39%
Top 10 57%
Top 20 79%
Top 30 92%
Top 40 98%
181 Potential ASCs
Market Sizing -
ASCsTop 10 states comprise 57% of target ASCs
22
17
14
9 9 8 7 6 6 6
0
10
20
30
FL CA TX NJ PA NY NC MD OH GA
20
15
10
5
0

19. Market Sizing
Executive Summary
US Hospitals
US ASCs
Europe
Limitations
Translating to Unit Sales
Conclusion

20. 19
Market PotentialMethodology
Market Sizing -
EuropeTitan can target 193 hospitals in Europe
Assumptions
744
193
0
100
200
300
400
500
600
700
800
US hospitals
(base case)
Europe
Numberofhospitals
Compared da Vinci’s Europe and US sales
• On average, Europe sales comprised
26% of US sales
Applied multiplier to SPORT
• 26% was applied to SPORT single unit
hospital sales, including replacements
• Europe is assumed to be the only international
market for Titan sales

21. Limitations
Market Sizing
Executive Summary
Translating to Unit Sales
1st Unit Sales
2nd Unit Sales
Replacements
Summary
Conclusion

22. 21
Titan has a 1,965 unit sales opportunity over 15 years
Overview
1st Unit Sale
2nd Unit Sale
Replacements
Opportunity
(# Units)
• All hospitals and ASCs across all
segments are assumed to have a 1st unit
sale
1,385
136
444
• Only US hospitals are considered to have
2nd unit sales
• 2nd unit sales are assumed to take place
2 years after 1st unit purchase
• Replacement units are considered for all
1st and 2nd unit sales across all channels
• Assumes 5 year life span for a SPORT
machine
Breakdown by Segment
Total: 1,965
Translating to Units
1st Unit
70%
2nd Unit
7%
Replacements
23%

23. Market Sizing
Executive Summary
Translating to Unit Sales
1st Unit Sales
2nd Unit Sales
Replacements
Summary
Limitations
Conclusion

24. 23
Representative Bass Curve
SPORT Forecasting Application
Bass Model Overview
• Used to model adoption of new products
• Incorporates market size and saturation
• Considers 2 sales channels: adopters and imitators
• Used widely in sales forecasting
Core assumption: SPORT’s new sales follow
same pattern as da Vinci’s historical sales
Unit sale forecasts were modeled using a Bass model
Starting sales values change in different
channels
Bass Model
• Coefficients of innovation and imitation fitted
to da Vinci sales
Ongoing sales Cumulative sales
S-shaped adoption curve: Market reaches
saturation over time, causing sales to decline
Assumed 2% growth in number of target hospitals
annually, in line with US and Europe average GDP
growth

25. 24
Titan has 1,385 first unit sales opportunity
Bass Model Assumptions
US
Hospitals
ASCs
Europe
15-year forecast
(# Units)
• Assumes accelerated
adoption, given exiting
hospital relationships
• Base case assumes starting
sales of 15 units in Year 1
964
170
251
• Assumes same innovation
and imitation rates as US
hospitals, but slower starting
sales
• Modeled as a multiplier
applied to the US hospital 1st
unit sales
Annual Sales
Total: 1,385
1st Unit Sales
-
40
80
120
160
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
UnitsSold
Year
0
10
20
30
40
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
UnitsSold
Year
0
10
20
30
40
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
UnitsSold
Year

26. Market Sizing
Executive Summary
Translating to Unit Sales
1st Unit Sales
2nd Unit Sales
Replacements
Summary
Limitations
Conclusion

27. 26
Hospital 2nd Unit Annual SalesMethodology
2nd Unit Sales
Titan can sell 136 second units to US Hospitals over 15 years
Adjusted procedure volumes
• Accounted for procedures conducted by
1st purchased SPORT unit
Determined probability of purchase
• Ran logistic regression to determine
probability of a hospital purchasing a 2nd
SPORT unit given adjusted volumes
Applied Bass Model
• Model was applied to base case estimate,
fitted to da Vinci sales; sales assumed to
begin 2 years after initial sale
136 Second Unit Sales
0
5
10
15
20
25
30
35
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
UnitsSold
Year

28. Market Sizing
Executive Summary
Translating to Unit Sales
1st Unit Sales
2nd Unit Sales
Replacements
Summary
Limitations
Conclusion

29. 28
Segmentation of Replacement Sales
Titan can sell 444 replacements in 15-year forecast
444 Replacement Units
Approach
Considered for all segments
• Replacement units were considered
for US hospitals (1st and 2nd unit
purchases), ASCs, and Europe
Assumed 5 year lifespan for
SPORT
• Operationalized as 20%
replacement of installed base every
year, starting 5 years after first sale
Annual Replacement Sales
Replacements
335
26
83
US Hospitals
US ASCs
Europe
0
50
100
150
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
UnitsSold
Year

30. Summary
Market Sizing
Executive Summary
Translating to Unit Sales
1st Unit Sales
2nd Unit Sales
Replacements
Limitations
Conclusion

31. 30
Annual Sales by Stream
Summary
Detailed summary of SPORT’s 15-year sales forecast
-
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Unitssold
Year
Hospital 1st Unit Hospital 2nd Unit Hospital 1st Unit Replacement
Hospital 2nd Unit Replacement ASC 1st Unit ASC Replacement
Europe 1st Unit Europe Replacement
Sales Drivers
Hospital 1st unit sales
are the primary sales
driver in the earlier
years, but begin to drop
off after Year 11
Hospital unit
replacements are the
primary sales driver in
the later years,
overtaking new unit
sales in Year 15

32. Limitations
Market Sizing
Executive Summary
Translating to Unit Sales
Conclusion

33. 32
Titan’s opportunity ranges 1202 to 2237 units in different scenarios Insights
15 Year Cumulative Sales
US Hospital
sales
With 2nd unit
sales
With 2nd unit and ASC
sales
With 2nd unit, ASC, and
European sales
Lower case 1202 1398 1697
Base case 1435 1631 1965
Upper case 1658 1854 2237
Base case

34. 33
Limitations
Limitations of modeling analysis
All Models
(Except European)
Titan is a pre-revenue company; no
historical sales to inform adoption
Difficult to estimate
market diffusion
Used Bass Diffusion Model to forecast
adoption, fitted based on comparable product
(da Vinci)
US Hospitals Some demographic data missing for certain
hospitals
Small potential error Imputed missing values using state averages
US Hospitals Brand loyalty to da Vinci is difficult to
quantify
Overstates market size Low case scenario assumed da Vinci
hospitals cannot be captured
US Hospitals Assumed future competitive landscape is
equivalent to da Vinci's past competitive
landscape
Overstates market size Conducted sensitivity analysis to consider
low-case scenarios. SPORT also has its own
competitive value proposition
Europe Difficult to estimate Titan’s market size in
Europe given lack of hospital-level data
Potential error Assumed SPORT's European sales as % of
US sales is the same as da Vinci’s historical
average
ASC Limited ASC financial and procedure
volume data
Potential error Matched ASCs to hospitals metrics using
k-NN algorithm
All Models
(Except European)
Uses only Medicare data Small Potential Error Assumed Medicare data is a consistent
representation across all hospitals
Limitation Overall Impact MitigationModel

35. Conclusion
Limitations
Insights
Market Sizing
Executive Summary
Translating to Unit Sales

36. 35
Summary of Findings
Conclusion
Summary of findings
73% of units are in US hospitals, 10% in US
ASCs, and 17% in Europe
1,965 units opportunity in 15 years (base case)
Titan can pursue a targeted marketing strategy
to capture high-likelihood healthcare facilities
or groups (i.e. Group Purchasing
Organizations or Integrated Delivery Networks)
Estimated unit opportunity ranges from 1,202
to 2,237 in different scenarios

37. 36
Appendix
Appendix

38. 37
Appendix
Glossary
Term Definition
AHD American Hospital Directory
CMS Center for Medicare & Medicaid Services
ASC Ambulatory Surgery Center – Stand-alone facilities that only perform outpatient
procedures
k-NN K-Nearest Neighbor Machine Learning Algorithm
Bass Model Model to estimate market penetration based on market size, adoption rate, and
word of mouth effect. Trained on historical data
Logistic
Regression
Machine learning model which predicts probability of a certain outcome based
on factors
DRG Diagnostic Related Group (Classification for in-patient hospital procedures)
APC Ambulatory Payment Classification (Classification for out-patient hospital
procedures)

39. 38
Appendix
SPORT 5-year forecast by stream
Year
Category 1 2 3 4 5
Hospital 1st Unit 15 15 15 22 31
Hospital 2nd Unit 1 1 1
Hospital 1st Unit Replacement - - - - -
Hospital 2nd Unit Replacement - - -
Hospital - Total 15 15 16 23 32
ASC 1st Unit 1 1 1 2 2
ASC Replacement - - - - -
ASC - Total 1 1 1 2 2
US - Total 16 16 17 25 34
Europe 1st Unit 4 4 4 6 8
Europe Replacement - - - - -
Europe Total 4 4 4 6 8
Total - Global 19 20 21 30 42
Cumulative 19 39 60 91 133