By Dr. Matthew Chua, Associate Lecturer & Consultant, Medical & Cybernetics Systems, NUS-ISS Digital manufacturing is an integrated approach to manufacturing that is centered around a computer system. Current limitations in bio-fabrication for medical needs includes long synthesis time, exceptional skilled labour and costs. We propose an intelligent digital manufacturing system that can perform patient-specific 3D modelling of implants, material selection and optimsation. Lastly, a virtual reality (VR) manufacturing lab is proposed to evaluate the prototype manufacturing process and train new manufacturers.

1. #ISSlearn
#ISSlearn
TOWARDS DIGITAL
MANUFACTURING OF PATIENT
SPECIFIC MEDICAL DEVICES
11 August 2017 / Dr. CHUA Chin Heng Matthew
© 2017 National University of Singapore. All Rights Reserved

2. #ISSlearn 2
About Me…
• Medical & Cybernetics Systems at
NUS ISS
• Have a research programme in
Medical Technology and Robotics
• PhD in Medical Engineering
• Research Interests: Artificial
Organs, Medical Devices, Artificial
Intelligence and Robotics
• Email: isscchm@nus.edu.sg
2© 2017 National University of Singapore. All Rights Reserved

3. #ISSlearn#ISSlearn
Contents of the Presentation
• Introduction to Digital Manufacturing and
Motivation
• Overview of Proposed Intelligent Digital
Manufacturing Platform for Hybrid Medical
Implants
• Anatomical Background
• Patient-Specific 3D Modeling of Implant
• Material Selection and Optimization Platform
• Virtual Reality (VR) Manufacturing Laboratory
• Conclusion
3© 2017 National University of Singapore. All Rights Reserved

4. #ISSlearn#ISSlearn
What is Digital Manufacturing?
• Digital manufacturing is an integrated
approach to manufacturing that is centered
around a computer system.
• Automates and Integrates:
• 3D Modelling
• Simulation
• Knowledge Bases
• Analysis
• Brings about the age of Virtual
Laboratories/Workshops
4© 2016 National University of Singapore. All Rights Reserved

5. #ISSlearn#ISSlearn
Why the need in Medical
Devices?
• The estimated cost of manufacturing medical
devices is high due to:
• Manpower
• Time
• Wastage and failures
• Rigorous Testing
• Customizability
5© 2016 National University of Singapore. All Rights Reserved

6. #ISSlearn#ISSlearn
How can DM solve our
problems?
• Integrate design, modeling and testing into a
single phase
• Robust and flexible to create patient-specific
devices/implants
• Reduce failure rates and the need for in-vitro
and in-vivo experiments
• Reduce the need for a physical lab space
6© 2016 National University of Singapore. All Rights Reserved

7. #ISSlearn#ISSlearn
Overview of an Intelligent
Digital Manufacturing
Platform
7© 2016 National University of Singapore. All Rights Reserved

8. #ISSlearn#ISSlearn
Novelty of our Intelligent
System
• Patient-specific 3D reconstruction and modelling
of implant
• Automation of Medical Implant Design, Material
Selection and Optimization
• First Intelligent System in the world for soft tissue
implant optimization
• Employ algorithms to make use of material
knowledge bases for design optimization
• A virtual reality (VR) manufacturing lab to
evaluate the prototype manufacturing process
and train new manufacturers.
• Our focus is on artificial tracheal implant
8© 2016 National University of Singapore. All Rights Reserved

9. #ISSlearn#ISSlearn
Anatomical Background
• Pharynx
• Back of mouth
• Pushes food down
• Involves in vocalization
• Larynx
• Epiglottis
• Piece of elastic cartilage flap
• Prevents food and liquid from
entering trachea
• Vocal Cords
• Two sheets of muscle
• Vibrates to produce pitch
9© 2016 National University of Singapore. All Rights Reserved

10. #ISSlearn#ISSlearn
Anatomical Background
• Trachea (Windpipe)
• Cartilage Rings
• C- shaped rings
• Provide skeletal support
• Maintain airway patency
• Mucosa Membrane
• Produce mucus to trap foreign
particles
• Air tight
• Ciliated Epithelium
• Surface cilia beat to transport
mucus up for expulsion
10© 2016 National University of Singapore. All Rights Reserved

11. #ISSlearn#ISSlearn
Anatomical Background
• Airway replacement
• For severe trachea cancer
• A permanent conduit prosthesis
needed
• Challenges of current airway
replacements:
• Lack of or slow epithelium and
tissue ingrowth
• Mismatch of mechanical properties
and size
• Long preparation time
11© 2016 National University of Singapore. All Rights Reserved
Trachea cancer. Source:
http://www.lexingtonpulmonary.com/lu
ngca/lungca.html

12. #ISSlearn#ISSlearn
Need for Patient Specific
Implant Models
• Required for FEA as difficult to quantitatively
measure results in-vivo
• Differences in anatomical dimensions between
individuals
• Simulation using patient’s imaging data to tailor
design
• Achieved even stress distribution in orthopaedic
implants design studies (Harrysson 2007)
12© 2016 National University of Singapore. All Rights Reserved

13. #ISSlearn#ISSlearn
Need for Patient Specific
Implant Models
• Importance of material modelling
• For the prediction of material properties
• Required in simulation-based design and
optimization (Heissler 1998)
• Provides a physical and mathematical explanation
to material behaviour
• Attenuation and mechanical models are important
for airway implants
13© 2016 National University of Singapore. All Rights Reserved

14. #ISSlearn#ISSlearn
Method for Patient Specific
Design
1. CT scan of subject
2. 2D Auto Segmentation
of organ dimensions
• Minor and Major radii of
tracheal
3. Volumetric
Reconstruction
4. Input into Material
Optimization Platform
14© 2016 National University of Singapore. All Rights Reserved

15. #ISSlearn#ISSlearn
Overview of the Material
Selection and Optimization
Platform
15© 2016 National University of Singapore. All Rights Reserved
Chua M & Chui CK (2016). Optimization of Patient-
Specific Design of Medical Implants for Manufacturing.
Procedia CIRP, 40, 402-406.

16. #ISSlearn#ISSlearn
Overview
• Open-source Biomaterial Knowledge Base is the
source of material behavior and properties
• Rule-base classification algorithm sorts materials
into potential composite combinations
• User evaluation based on qualitative biological
requirement sieve out candidates into cluster
pool
• Genetic algorithm (GA) used to optimize cluster
composition
• Patient-specific implant model input and FEM
evaluation
16© 2016 National University of Singapore. All Rights Reserved

17. #ISSlearn#ISSlearn
Rule-Based Classification
• The selection of candidate materials to form
a new composite that will be most suitable for
an implant from the plethora of available
materials knowledge base known and the
possible permutations.
• Employs a systematic method utilizing rules
to streamline down the possible
combinations of materials
17© 2016 National University of Singapore. All Rights Reserved

18. #ISSlearn#ISSlearn
Rule-Based Classification
• In an expert system, a rule is a conditional
statement “if X condition is satisfied then the
outcome is Y”
• According to the laws of mixture in composite,
the final physical property of a composite falls
within the range of the lowest value and highest
value of the same property of constituents within
depending on the composition fraction
• Hence rule applied:
“If within a cluster, at least one of the materials has a Young
Modulus less than the required value and at least one material
has a Young Modulus more than the required value, cluster is
accepted. If not, discard.”
18© 2016 National University of Singapore. All Rights Reserved
Quantitative Association Rule:

19. #ISSlearn#ISSlearn
Rule-Based Classification
• The rule implemented can be expanded to
cover many other physical properties like
density, strength, electrical resistivity and
many more
• The greater the number of rules generated
into the system, the more efficient and
effective the process of streamlining the
possible clusters of materials down.
19© 2016 National University of Singapore. All Rights Reserved

20. #ISSlearn#ISSlearn
Genetic Algorithm
Optimization
• Genetic Algorithm (GA) is the most
commonly-used evolutionary optimization
technique which can provide the global
optimal solution for complex problems
involving a large number of variables and
constraints
• Each individual genotype in a population is
coded as a fixed length binary string, which
depends on the parameters to be
investigated
20© 2016 National University of Singapore. All Rights Reserved

21. #ISSlearn#ISSlearn
Steps for GA Optimization
• Population initialization: pool of candidate solution is randomly
generated. The initialization involves creating a pool of random
compositions of materials belonging to a cluster.
• Parent selection: the fitness of the population is evaluated and
the “desired” parents are selected while the remaining is
discarded. The selection process is based on a probabilistic
distribution and fitter parents have a higher chance of being
selected.
• Modification: crossing over and mutations are performed on the
selected parents to produce offspring solutions.
• Survival selection: the fitness of the offspring population is
evaluated and the individuals to form the next generation are
selected from the combined pool of parent and offspring
population.
• If a satisfactory level of solutions has been attained or certain
stopping criteria is met, the process terminates. If not, repeat
from step 2.
21© 2016 National University of Singapore. All Rights Reserved

22. #ISSlearn#ISSlearn
Assessment Method for GA
• Fitness assessment of each genotype:
• Objective function to monitor fitness of genotype to
over population to determine when to terminate:
22© 2016 National University of Singapore. All Rights Reserved

23. #ISSlearn#ISSlearn
Application
• The material properties of a list
of possible biomaterials are
firstly consolidated from
available knowledge-bases
• Tensile and flexural moduli are
chosen because of the natural
stretching and bending motion of
the trachea.
• The rules implemented in this
case is such that the required
value for cluster acceptance is
equals to the tensile modulus,
flexural modulus and density of
the natural trachea cartilage,
which are 18MPa, 15MPa and
1050kg/m3 respectively
23© 2016 National University of Singapore. All Rights Reserved

24. #ISSlearn#ISSlearn
Application
• From the generated set of candidate material
clusters, we eliminated according to the
qualitative requirements of the application.
• The PDMS/CNT/Ag composite cluster was
chosen due to biological properties of non-
biodegradability, antibacterial and bio-
activeness.
24© 2016 National University of Singapore. All Rights Reserved

25. #ISSlearn#ISSlearn
Optimization with GA
• The cluster with the
material properties
was then optimized
using GA to
determine the
possible material
compositions to
yield material
properties as close
as possible to the
tracheal tissue.
• The algorithm
terminates after 20
generations or when
the solution
converges
25© 2016 National University of Singapore. All Rights Reserved

26. #ISSlearn#ISSlearn
FEM Evaluation of Implant
• Simulated evaluation of design
• Multi-physics software
26© 2016 National University of Singapore. All Rights Reserved

27. #ISSlearn#ISSlearn
Virtual Reality (VR) Laboratory for
Manufacturing Evaluation
• Use of conventional laboratory to brings
about many issues:
• contamination and safety risks
• long setup period
• high skill and experience requirement
• High costs
• A VR-based laboratory can be used to
evaluate the manufacturing process and train
potential human operators
27© 2016 National University of Singapore. All Rights Reserved

28. #ISSlearn#ISSlearn
Overview of the VR training
laboratory
28© 2016 National University of Singapore. All Rights Reserved

29. #ISSlearn#ISSlearn
Overview
• Two main components:
• Virtual Environment
• all virtual activities and interactions take place in real time,
ranging from operator assembly training step actions and
walking movements to movements caused by gravity and
other forces
• Consists also of a VR supervisor ( an A.I)
• Interactive Channel
• communication bridge between the assembly
operator and the VR supervisor
• operator can send special request commands
• VR supervisor can give step guidance
29© 2016 National University of Singapore. All Rights Reserved

30. #ISSlearn#ISSlearn
VR Training Flowchart
30© 2016 National University of Singapore. All Rights Reserved

31. #ISSlearn#ISSlearn
VR Supervisor Algorithm
31© 2016 National University of Singapore. All Rights Reserved

32. #ISSlearn#ISSlearn
VR Supervisor Finite State
Machine
32© 2016 National University of Singapore. All Rights Reserved

33. #ISSlearn#ISSlearn
Application of VR
Laboratory
• VR Lab is constructed using the software
UNITY, with basic physics-based modelling
(e.g. collision detection, object
manipulations) and relevant assembly steps.
• It is a platform that houses all virtual training
activities (tutorials, practices and
assessments), interactions between operator
and the equipment/materials/supporting
items, and interactions between the operator
and the VR supervisor.
33© 2016 National University of Singapore. All Rights Reserved

34. #ISSlearn#ISSlearn
Application of VR
Laboratory
• User placed in front of a workbench in the lab with
provided equipment, materials and supporting items
• Full functionality and interaction with them using his
virtual hands
• Operator-to-VR supervisor interactive channel, in form of
a virtual panel with buttons, is placed on the wall behind
the workbench to allow the trainee to send out special
request commands
34© 2016 National University of Singapore. All Rights Reserved

35. #ISSlearn#ISSlearn
Step-by-step Instruction and
Supervision
35© 2016 National University of Singapore. All Rights Reserved

36. #ISSlearn#ISSlearn
Conclusion
• The availability of a huge knowledge base for
biomaterials and their properties and
behavior requires an intelligent expert system
to effectively harness and utilize it
• The intelligent digital manufacturing platform
presented can help produce more effective
hybrid medical implants in a shorter time and
at lower costs
• The platform integrates artificial intelligence,
knowledge base and VR in a seamless
program
36© 2016 National University of Singapore. All Rights Reserved

37. #ISSlearn 37
THANK YOU 
isscchm@nus.edu.sg
37© 2017 National University of Singapore. All Rights Reserved