Medical Design Briefs - April 2021 - 15

Automating

Orthopedic Revision Planning

Fig. 1 - Example of an automatically segmented hip using AI.

otal hip and
knee replacements are very
common and
have an excellent track record of success. Over time, however, they can fail
for a variety of reasons like implant
wear/loosening, recurrent dislocation
or infection. When failure occurs, the
physician may recommend a second
operation, called a revision surgery, to
remove some or all the parts of the original implant and replace them with a
new ones.
Revision surgeries are much more
complex than the primary surgeries and
as a result, planning these surgeries
requires a detailed understanding of an
individual subject's anatomy and how it
interacts with an implant. To model the
latter, 3D imaging such as computed
tomography (CT) can be used to create
a realistic representation of anatomical
structures.
However, taking this image data and
extracting relevant information from it
is often a time-consuming and predominantly manual task for clinical professionals, involving careful segmentation

T

of key body parts and selecting relevant anatomical landmarks. This significant bottleneck then has an effect on
the time needed to plan surgeries and
can contribute to unwanted delays. One
emerging solution for this challenge is
automated segmentation and landmarking using machine learning-based artificial intelligence (AI) to speed up tedious tasks. More generally, AI-based
solutions enable significant acceleration
of image-to-model workflows in the life

sciences and give clinicians and
medical device engineers more time
for analysis and innovation.1 How does
this work, and what kind of cases can it
benefit?
Revision Surgery Challenges
According to recent studies, the number of total knee arthroplasties (TKAs)
in the United States alone will increase
from 719,000 in 2015 to 3.48 million by
2030, with contributing factors includ-

Simpleware AS Ortho
Final model

0 mins

3 mins

1 hour

3 mins Simpleware AS Ortho
1 hour Traditional Segmentation

Total processing time
Typical threshold
starting point

Fig. 2 - Example of a common primary surgical case workflow with automated segmentation versus
manual segmentation.

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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
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Medical Design Briefs - April 2021 - Cov3
Medical Design Briefs - April 2021 - Cov4
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