Page 78 - Read Online

Page 408 Johnson et al. Art Int Surg 2024;4:401-10 https://dx.doi.org/10.20517/ais.2024.40

Limitations to this technique include the ever-present potential for overfitting, the computing resources
required, and the technical expertise required to run the analysis algorithms. An important next step would
be to test these methods on an external cohort. Another consideration with this methodology is that the
thoracic MRI did not capture the top of the implanted construct in a few subjects. This can be seen as both a
potential weakness and potential strength of this study because the results were robust despite this
consideration - this indicates that there is possibly a global imaging feature that the 3D-CNN detects to aid
accurate classification. Finally, the proper de-identification of raw data is paramount to model creation to
ensure patient privacy when deploying trained models.

Overall, the use of machine learning in medical imaging has garnered attention but has still been limited in
scope compared to tabular data machine learning and large language models. We aimed to demonstrate the
potential of a simple classification scheme on available 3D MRIs to predict the development of
pseudarthrosis following ASD surgery. Beyond the cross-validated accuracy of the model, our approach has
the benefit of providing a level of interpretation by outlining imaging features used by the model to make
classification decisions. Overall, this work demonstrates the capabilities of raw imaging AI in spine surgery
and can serve as the basis for a deeper biological inquiry into the pathogenesis of pseudarthrosis.

DECLARATIONS
Authors’ contributions
Conceptualization, data acquisition, data analysis, results interpretation, and manuscript preparation:
Johnson GW, Chanbour H
Conceptualization, data analysis, results interpretation, and manuscript preparation: Doss DJ
Conceptualization, results interpretation, and manuscript preparation: Makhoul GS
Conceptualization, data acquisition, data analysis, results interpretation, and manuscript preparation:
Abtahi AM, Stephens BF, Zuckerman SL

Availability of data and materials
All data and code are available upon reasonable request to the corresponding author.

Financial support and sponsorship
None.

Conflicts of interest
Stephens BF receives institutional research support from Nuvasive and Stryker Spine. Zuckerman SL reports
being an unaffiliated neurotrauma consultant for the National Football League. Abtahi AM receives
institutional research support from Stryker Spine. The other authors declared that there are no conflicts of
interest.

Ethical approval and consent to participate
All procedures performed in studies involving human participants were in accordance with the ethical
standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration
and its later amendments or comparable ethical standards. IRB: Approval Attained (#211290).

Consent for publication
Not applicable.