Medical Design Briefs - April 2021 - 17

can be very time-consuming, taking multiple hours to achieve a result that is ready
as a starting point for a clinician or medical device engineer. The automated solutions seen in products like Simpleware AS
Ortho are able to take this segmentation
time down to roughly three minutes.
Although an automated method is unable
to completely remove all manual work, it
can cover the majority of basic steps to
reach a point where only additional
cleanup is needed to make sure that the
model is accurate and ready for whatever
the next stage of the project might be.
Another complicating factor in revision surgeries involves dealing with an
unusual amount of noise in the scan
data, or unique pathologies that cannot
be handled by typical segmentation routines (see Figure 4). For these situations,
automated segmentation is able to solve
many manual tasks before a user has to
work on the data. For example, this
might include removing unwanted artefacts or scatter and is potentially able to
reduce challenging cases from 8-10
hours to get to a workable model down
to 30 minutes. It is therefore worth emphasizing how AI approaches do not

completely automate revision and other
surgical planning challenges but allows
users to get to a place where much of the
more tedious jobs have been dealt with.
Conclusions
The ultimate goal of using AI-based
machine learning for orthopedic revision
surgeries is to cut down on the headaches
of manual segmentation and landmarking, even when working with more complex cases. Engineers, technicians, and
clinical professionals working with image
data can free up more time for highervalue tasks, including analysis and simulation of procedures. What this means for
the patient is a shorter route from an initial patient scan through to a diagnosis or
surgical plan. Getting the manual segmentation and editing down to a minimum
also means that it is possible to rapidly generate models for computational simulation, something that is being gradually
accepted by FDA for in silico trials, and to
produce patient-specific 3D prints.
Of course, there are still future challenges to improving the accuracy and
applicability of automated segmentation,
including the availability of high-quality

datasets and the scalability of solutions
for different levels of healthcare. To this
end, the technology behind software like
Simpleware AS Ortho is now being
applied to cardiovascular and other
cases, as well as part of more comprehensive measurement and simulation workflows that benefit from automation.
References
1. Jiang F, Jiang Y, Zhi,H, et al., " Artificial
intelligence in healthcare: past, present
and future, " Stroke and Vasc Neuro,
2(4):230-243.
2. Klug A, Gramlich Y, Rudert M, et al., " The
projected volume of primary and revision
total knee arthroplasty will place an
immense burden on future health care
systems over the next 30 years, " Knee Surg,
Sports Traumat, Arthro, https://link.
springer.com/article/10.1007/s00167-02006154-7, 1-12, 2020.
3. Haglin JM, Eltorai AEM, Gil JA, et al.,
" Patient-Specific Orthopaedic Implants, "
Ortho Surg, 8(4):417-424, 2016.
4. Turner T, " Hip Revision Surgery, "
Drugwatch, https://www.drugwatch.com/
hip-replacement/surgery, 2020.

This article was written by Dr. Jessica
James, Business Process Analyst, Simpleware
Product Group, Synopsys Northern Europe,
Exeter, UK. For more information, visit
http://info.hotims.com/79412-342.

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Medical Design Briefs - April 2021

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