Medical Design Briefs - April 2021 - 16

Orthopedic Revision Planning
Simpleware AS Ortho
Final model

3 mins

3 mins

4 hours

6 mins

Simpleware AS Ortho

4 hours

Traditional Segmentation

Total processing time
Typical threshold
starting point

Fig. 3 - More complex orthopedic case with intermediate noise levels.

ing an aging population, increased obesity rates, and the potential of new technology to open up access to procedures
at lower costs. Furthermore, total hip
arthroplasties (THAs) are predicted to
rise from 332,000 to 572,000 over the
same time span, creating increased demand from patients and increased pressures on clinicians.2
Subject-specific surgical planning is also
now becoming common, with image data
taken from CT and magnetic resonance
imaging (MRI) making it easier to create
3D models that accurately capture unique
anatomies and pathologies. Advantages of
this approach include optimization of hip
and knee implants to individual patients,
reducing the risk of complications and
improving comfort. The use of customized implants requires careful presurgical planning using simulation of how
medical devices interact with the body,
reducing the time spent on design iterations and increasing confidence in decision-making in the run-up to operations.3
However, revision surgeries are still
required for hip replacements after ten
years in about 4-5 percent of cases, going

up to 15 percent after 20 years, while it is
expected that the annual number of knee
revision procedures will increase at a rate
of almost 90 percent from 2020 to 2050.4
As a result, it is important for these procedures to be efficient and designed to prevent recurring problems in both straightforward and more drastic scenarios. This
issue is compounded as success rates for
revisions are generally lower than initial
surgeries due to a weakening of the bone.
Manual 3D Image Segmentation
Bottlenecks. When planning a revision
surgery, clinical professionals working
with MRI and CT data face several bottlenecks to creating a workable solution
for an individual patient. Most notably,
segmentation and landmarking of the
different regions of interest within anatomical data can be a painstakingly manual process. Depending on the technical
experience and skill of the user, many
hours may be spent just getting to the
point of identifying the anatomical
structures that are relevant to a surgery.
Automated Segmentation for Different
Revision Cases. AI is becoming increasingly recognized in the medical field

Simpleware AS Ortho
Final model

30 mins

3 mins

10 hours

30 mins

Simpleware AS Ortho

10 hours

Traditional Segmentation

Total processing time
Typical threshold
starting point

Fig. 4 - More complex orthopedic case with intermediate noise levels.

16

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when dealing with these repetitive challenges, for clinical detection and patientspecific surgical planning or design purposes. Synopsys Simplewareâ„¢ software is
one example of where AI has been applied
to a specific problem in manual segmentation, and uses machine learning to automate repetitive steps and landmarking in
order to speed up workflows (see Figure
1). Patient datasets and extensive training
inputs are used to develop algorithms that
can, with a single click, automate a large
part of the segmentation on an anatomy,
with clinical reviews used to ensure suitability for real-world cases. This technology has been applied through Simpleware
AS Ortho, a software module that works
specifically with hips, knees, and ankles.
In the case of primary and revision
planning, those working with patient data
can use automated segmentation to identify different anatomical structures to
quickly generate a standard result, including masks and landmarks in both a 2D
and 3D output that is ready for further
analysis. Each femur, the pelvis, and
sacrum can be distinguished, and common landmarks automatically placed.
From this point, additional editing tools
then enables users to work on specific features and prepare a final model for CAD
work, such as positioning an implant, or
finite element analysis (FEA)-based simulation. For common workflows, this
means a 20-50 times faster rate of segmentation and landmarking, which saves
time and cost when scaling medical device
R&D through to surgical planning.
For primary surgical cases involving
hips, automated segmentation makes it
much easier to work with CT scans to
extract the relevant information than typical approaches. While separating the hip
and femur in these planning stages is relatively straightforward for users, it can still
take an hour or so for the average user to
complete with manual and semiautomatic
methods such as thresholding, region
growing, etc. When using automated segmentation, a final model can be obtained
in a few minutes with just one click, as
shown in Figure 2.
By comparison, planning revision surgeries means having to account for situations where an implant is already in the
body, or where there is noise in the scan
(see Figure 3). As a result, it is necessary to
separate the metallic material of the
implant from the rest of the patient's
anatomy, for example, to separate features such as the acetabular ball and
implant stem. Again, manual approaches
Medical Design Briefs, April 2021


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Medical Design Briefs - April 2021

Table of Contents for the Digital Edition of Medical Design Briefs - April 2021

Medical Design Briefs - April 2021 - Intro
Medical Design Briefs - April 2021 - Cov4
Medical Design Briefs - April 2021 - Cov1a
Medical Design Briefs - April 2021 - Cov1b
Medical Design Briefs - April 2021 - Cov1
Medical Design Briefs - April 2021 - Cov2
Medical Design Briefs - April 2021 - 1
Medical Design Briefs - April 2021 - 2
Medical Design Briefs - April 2021 - 3
Medical Design Briefs - April 2021 - 4
Medical Design Briefs - April 2021 - 5
Medical Design Briefs - April 2021 - 6
Medical Design Briefs - April 2021 - 7
Medical Design Briefs - April 2021 - 8
Medical Design Briefs - April 2021 - 9
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Medical Design Briefs - April 2021 - 11
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Medical Design Briefs - April 2021 - Cov3
Medical Design Briefs - April 2021 - Cov4
https://www.nxtbook.com/smg/techbriefs/22MDB01
https://www.nxtbook.com/smg/techbriefs/21MDB12
https://www.nxtbook.com/smg/techbriefs/21MDB11
https://www.nxtbook.com/smg/techbriefs/21MDB10
https://www.nxtbook.com/smg/techbriefs/21MDB09
https://www.nxtbook.com/smg/techbriefs/21MDB08
https://www.nxtbook.com/smg/techbriefs/21MDB07
https://www.nxtbook.com/smg/techbriefs/21MDB06
https://www.nxtbook.com/smg/techbriefs/21MDB05
https://www.nxtbook.com/smg/techbriefs/21MDB04
https://www.nxtbook.com/smg/techbriefs/21MDB02
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