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THE KNEE SOCIETY | VIRTUAL FELLOWSHIP
Robotics in Knee Arthroplasty
Presented by:
Jess H. Lonner, MD
Rothman Institute
Philadelphia, PA

DISCLOSURES
 Royalties
 Zimmer Biomet, Smith and Nephew
 Consultant
 Zimmer Biomet, Smith and Nephew
 Speaker’s bureau
 Zimmer Biomet, Smith and Nephew
 Publishers:
 Saunders, Lippincott Williams Wilkins, Springer
 Shareholder:
 Blue Belt Technologies, CD Diagnostics

ROBOTS IN INDUSTRY

 Efficient
 Economical

 Exacting

“Robotics industry today is where the PC industry was
30 years ago.”**
Bill Gates, Scientific American 2007

BILL GATES

**(Especially healthcare)

EXPERIENCE WITH ORTHOPAEDIC ROBOTS
 Initial skepticism

 Early adopters showed value
 Alignment
 Soft tissue balance

 Recovery
 Blood loss
 Safety (semi-autonomous)

 Increased utilization with pricing improvements

Lonner JH. Operative Techniques in Orthopaedics 2015

STORY OF ROBOTICS IN KNEE ARTHROPLASTY
 Study in patterns that define technological progress and innovation, in general
 Newer companies/technologies

 Declining capital and maintenance costs
 Smaller space requirements
 Broadening access

 Increased utilization
 Expanding applications
Lonner JH. Operative Techniques in Orthopaedics 2015

STAKEHOLDERS WILL INFLUENCE FURTHER GROWTH OF
ROBOTICS

EXPANDING ROLE FOR ROBOTICS IN UKA

15% of UKA’s in US (2013)

www.OrthopedicNetworkNews.com. 2013

PATENTS AS A SURROGATE INDICATOR OF INNOVATION

Dalton DM et al. J Arthroplasty 2016

ROBOTIC LANDSCAPE:
PROJECTED PENETRATION

 UKA
 ~29% in five years

 ~37% in 10 years

 TKA
 ~10% in two years
 ~18% in five years

 ~23% in ten years
Medical Device and Diagnostic Industry, March 5, 2015
http://www.mddionline.com

KEY DISTINCTION IN ORTHOPAEDIC
ROBOTICS
 Autonomous- robot operates independently

 TCat (formerly Robodoc)– iThink Surgical
 FDA approved for THA
 Not FDA approved for TKA
 Semi-autonomous- surgeon guided; haptic or speed/exposure constraint

 Mako (Stryker)
 FDA approved for THA, UKA, PFA, TKA

 Navio (Smith and Nephew)
 FDA approved for UKA, PFA, TKA

 OmniBot (Omni)
 FDA approved for TKA

COMPLICATIONS WITH AUTONOMOUS
SYSTEMS

 Complications THA
 Soft tissue injuries, over-resection
Severe abductor injuries/sciatic nerve injuries

 18% revision due to instability (vs 4% control)
 Aborted cases TKA

 8% soft tissue injury
Honl et al JBJS 2003
Chun et al J Arthrop 2011

ADVANCEMENT OF SEMI-AUTONOMOUS ROBOTIC SYSTEMS

 Safety and avoidance of soft tissue complications has been key distinction

ROBOTICS FOR TKA?
 Unclear need for “precise” alignment
 Potential roles:
 Optimizing soft tissue balance?
 Bicruciate retaining TKA?
 Access, balance

 Facilitating efficiencies?

 Reducing instrument storage/sterilization needs/costs?
 Applicable for ASC’s

ROBOTICS FOR UKA?

 94% survivorship at 10-15 yrs in hands of high volume

surgeons…

…BUT

< Age 65

> Age 65

10-yr survivorship 77%

7-yr survivorship 74%

Ong, Kurtz, Hansen, Lonner AAHKS 2014

WHAT IMPACTS THE RESULTS OF UKA?
 Pathology/Disease
 Patient selection
 Component design
 Polyethylene quality
 Surgeon experience/volume

 Accuracy of implantation
 Soft tissue balance

MALALIGNMENT PREDISPOSES TO FAILURE

 Coronal malalignment of tibial component >3° varus
 Mechanical limb varus >8°
 Posterior tibial slope >7°
Collier /Engh et al. J Arthroplasty 2006;
Hernigou JBJS 2004; Chatellard Orthop
Traumatol Surg Res 2013

UKA MALALIGNMENT > IN MIS THAN OPEN WITH STANDARD
INSTRUMENTATION

 Greater inaccuracy in tibial component alignment and limb alignment


Fisher DA et al. (J Arthrop 2003)



Hamilton WG et al. (J Arthrop 2006)

OUTLIERS IN ALIGNMENT IN UKA WITH
CONVENTIONAL METHODS

 40-60% of cases are malaligned beyond 2° of plan

Keene G et al JBJS Br 2006;
Cobb J et al JBJS Br 2006

RATIONALE OF ROBOTICS FOR UKA
 Simplify the procedure

 Reduce the amount of instrumentation
 Eliminate surgical steps
 Enhance accuracy

 Bone preparation/component alignment
 Soft tissue balance
 Improve clinical results
Lonner JH. American Journal of Orthopedics 2009

SEMI-AUTONOMOUS ROBOTICS IN KNEE
ARTHROPLASTY IN U.S.

 Virtual planning
 Bone resection
 Component sizing

 Implant alignment
 Soft tissue balancing

1ST GENERATION SYSTEM

 Image based CT planning and computer guidance
 Balance & alignment
 Implant positioning and sizing
 Intraop virtual gap balancing

 Bone prep with 6 mm burr attached to robotic arm

1ST GENERATION SEMI-AUTONOMOUS
ROBOTIC ARM FOR UKA:

 Haptic constraint
 Efficient

 Accurate
 Safe
 Image-based (preop CT scan)

DOWNSIDES OF 1ST GENERATION SEMI-AUTONOMOUS
ROBOTIC SYSTEM
 Capital expense
 Preop CT scan
 Additional expense
 Denials common; high copays; bundled payments
 Hospitals “eat cost”

 Time/Inconvenience
 Radiation exposure

2ND GENERATION SEMI-AUTONOMOUS ROBOTIC SYSTEMS:

 Image-free (No CT scan)

 Intraop registration/mapping/planning
 Intraop gap balancing
 Burr Speed/Exposure control

 Cost favorable
 35% being used in ASC’s for UKA

SURGICAL TECHNIQUES

IMAGE FREE SYSTEM: SURFACE MAPPING

DYNAMIC INTRAOP GAP BALANCING

SELECTION OF IMPLANT SIZE/POSITION AND
VIRTUAL GAP BALANCE

VIRTUAL TRACKING OF FEMUR ON TIBIA

TECHNIQUE: BONE PREPARATION

PREPARED SURFACE

CT-BASED SYSTEM: PREOP PLANNING

IMAGE-BASED SYSTEM: DYNAMIC SOFT-TISSUE GAP BALANCING

 Remove osteophytes

 Tension MCL/LCL
 Capture tissue tension through

ROM
 Adjust prn

IMAGE BASED SYSTEM: HAPTIC CONSTRAINT
Bone resection volume based upon planned component
placement and size

IMAGE-BASED SYSTEM: ASSESSING ACCURACY
OF IMPLANT POSITION

DATA???

KEY STUDIES
 Accuracy of bone preparation
 Pre-clinical (cadaveric specimens) and clinical studies
 Comparison of intraoperative plan for limb alignment with postop limb alignment

 Clinical (navigated measures)
 Accuracy of tibial component alignment and volumetric bone preservation

 Radiographic
 Learning Curve

 Safety
 Radiation avoidance by using image-free systems (eliminating preop CT scans)
 Survivorship and satisfaction

TIBIAL ALIGNMENT -- UKA

 Initial 31 robotic UKA’s with Haptic, CT-based robotic system
 Matched group of preceding 27 conventional UKA

 Height, weight, ROM, alignment
 Study parameter: Tibial alignment

(Lonner, John, Conditt CORR 2009)

TIBIAL ALIGNMENT -- UKA

 Variance: 2.6x greater with manual techniques (p<0.05)

 RMS error: 3.4 (manual) vs. 1.8 (robot)
 Coronal alignment – Avg error:

 Manual: 2.7 +/- 2.1 more varus
 Robot: 0.2 +/-1.8  (p<0.0001)

(Lonner, John, Conditt CORR 2009)

ACCURACY OF COMPONENT POSITIONING IN UKA:
SEMI-AUTONOMOUS ROBOT VS. CONVENTIONAL

 Prospective RCT, 120 patients

 62 robotic UKA (Robotic)
 58 conventional (Conventional)
 Component alignment and position determined by CT scan
 Coronal, sagittal and axial positioning

Bell SW et al. J Bone Joint Surg. 2016

ACCURACY OF COMPONENT POSITIONING IN UKA:
SEMI-AUTONOMOUS ROBOT VS. CONVENTIONAL

 Robotic assistance had:
 significantly lower component median implantation errors in all 3 component

parameters (p<0.01)
 Significantly fewer outliers >2° of target positions

Bell SW et al. J Bone Joint Surg. 2016

PRE-CLINICAL ACCURACY

 25 cadaveric specimens
 Image-free semi-autonomous system (2nd Generation robot)

 Medial UKA
 3 surgeons
Lonner, Smith, Picard, Hamlin - Clin Orthop 2014

ANALYSIS METHOD
 Preop plan

 Postop analysis
 Optical probe inserted into implant divots
 Surface positions mapped
 Postop position compared to plan
Lonner, Smith, Picard, Hamlin - Clin Orthop 2014

ALIGNMENT:
SEMI-AUTONOMOUS ROBOTS VS. MANUAL
2.6x less variability than manual techniques (p<0.05)
RMS Error

Image-Free

CT-Based

Manual

Flex/Ext (°)

1.6

2.1

4.1

Varus/Valgus (°)

2.3

2.1

6.0

Int/Ext (°)

1.7

3.0

6.3

Prox/Dist (mm)

1.3

1.0

2.8

Ant/Post (mm)

1.3

1.6

2.4

Med/Lat (mm)

0.9

1.0

1.6

Dunbar et al J Arthrop 2012
Jenny J Arthrop 2002
Lonner et al CORR 2014

ALIGNMENT: NO APPARENT DIFFERENCE -- CT-BASED VS IMAGE-FREE
ROBOTIC SYSTEMS

6 wks post Image- free

6 yrs post CT-based

PLANNED VERSUS ACHIEVED LIMB ALIGNMENT

 65 cases, image-free robotic system
 Multiple surgeons

 Postop limb alignment ≤1° from plan 92% (60/65)

F Picard, A Gregori, J Bellemans, J Lonner, J Smith, D Gonzales, A Simone, B
Jaramaz – CAOS July 2014

TIBIAL RESECTION (ROBOTIC VS.
CONVENTIONAL)

 Industry Data
 27,989 conventional UKA’s
 8421 semi-autonomous robotic UKA’s
 Studied variable: tibial poly thickness

 Implications for revision to TKA
 Complexity, need for augments/stems
Ponzio DY, Lonner JH. Am J Orthop 2016

Robotic
8 mm

Conventional
10 mm

TIBIAL RESECTION (POLY SIZES)
 8-mm and 9-mm polyethylene inserts
 Robotic group: 93.6%
 Conventional group: 84.5% (P < .0001).
 Aggressive tibial resection, requiring tibial inserts ≥10 mm
 Robotic group: 6.4%
 Conventional group: 15.5%
 Tibial inserts >11 mm

 Robotic group: 0.3%
 Conventional group: 5.7%
 No differences between 2 semi-autonomous robots

Ponzio DY, Lonner JH. Am J Orthop 2016

LEARNING CURVE
 Eleven novice users (2nd generation image-free system)
 Precision achieved immediately

 Mean of 8 procedures to reach a steady state surgical time

(95% confidence interval 6-11)
 Avg. steady state surgical time 45 minutes (range 37-55 minutes)

A Gregori, F Picard, J Lonner, R Marquez, J Smith, A Simone, B Jaramaz - CAOS Abstract 2014

LEARNING CURVE
 Greatest improvement in “Cutting Phase”:
 Average improvement from 42 to 24 minutes.

 Least improvement in “Anatomic Registration” and “Implant Planning”:
 Average improvement from 14 minutes to 6 minutes.
 The mean steady state surgical time for all surgeons was 45 minutes (SE 4.3, p<0.001).
Learning curve

Surgical Time (mins)

110.

82.5

55.

27.5

0.
0

10

20
Surgical Case Number

30

40

GAP BALANCING
• Final ligament balance after implantation accurate within
0.53 mm compared to dynamic plan

Plate JF et al Advances in Orthopedics 2013

SAFETY: SEMI-AUTONOMOUS ROBOTIC SYSTEMS

 Initial 1010 cases
 Single surgeon (JHL)
 No robot-related soft tissue complications

RADIATION FROM
PREOP CT SCANS
 236 scans 2011-2013
 1st generation image-based system

 ED of radiation from LE CT scan:
 4.8 +/- 3.0 mSv

 25% had add’l CT scans (est cumulative ED of 6-103 mSv)
 Note: 10 mSv increases risk of fatal cancer by 1 in 2000

Ponzio DY, Lonner JH. J Arthroplasty 2015

SURVIVORSHIP AND SATISFACTION
 909 consecutive semi-autonomous robotic UKA’s

 6 surgeons
 FB metal-backed implant
 Follow up: mean 30 mos [range, 22-52 mos]

 Survivorship: 98.8% (96% if non-responders failed)
 92% satisfied in patients without revision

COPYRIGHT © 2016 THE KNEE SOCIETY

Pearle AD et al. Knee 2017

ROBOTICS FOR TKA?

 100 TKA’s
 50 conventional

 50 autonomous robotic-assisted (currently not approved for use in U.S.)

 Mechanical axis outliers >3°:
 Robotic: 0%
 Conventional: 24%

 No differences in ROM or function scores
Song EK, Bargar WL et al. Clin Orthop 2013

ROBOTICS FOR TKA?
 Prospective RCT
 60 TKA’s

 29 conventional
 31 autonomous robotic-assisted (currently not approved for use in U.S.)

 Mechanical axis outliers >3°:
 Robotic: 0%

 Conventional: 19% (p=0.05)
 Joint line outliers (>5mm):

 Robotic: 3.2%
 Conventional: 20% (p=0.05)

Liow MHL et al. J Arthrop 2014

ROBOTICS FOR TKA
 Image-free semi-autonomous system (FDA approved)
 108 initial cases

 Radiographic alignment data:
 Mechanical axis within 3°: 91%*
 Tibial component alignment within 3°: 99%

 Femoral axis alignment within 3°: 99%
* Unpublished data suggests improved
mechanical alignment with new kinematic
balancing algorithm
COPYRIGHT © 2016 THE KNEE SOCIETY

Koenig JA, Plaskos C. Influence of Pre-Operative Deformity on
Surgical Accuracy and Time in Robotic-Assisted TKA. Bone Joint
J 2013;95-B (S-28) 62

CONCLUSION: ROBOTICS
 Image-free vs CT based
 Autonomous vs. semi-autonomous

 Cost favorable?
 ASC-feasible?
 Expanding applications
 UKA, PFA, BiKA
 THA, TKA

 Etc, etc.

CONCLUSION:
ROBOTICS

 Semi-autonomous systems:
 Accurate bone preparation, implant position, soft

tissue gap balance
 Safe
 Further study needed to determine:
 Functional outcomes
 Impact on late results/durability

CONCLUSION: ROBOT

 Medicine is prime for a “disruption”
 Growing influence of smart technologies in knee arthroplasty

 Robotics fits into that paradigm
 Exponential utilization and development
 Stay tuned…

THANK YOU.



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