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Wang et al. Art Int Surg. 2025;5:465-75 https://dx.doi.org/10.20517/ais.2025.03 Page 473

RPNI signals, while impressive, may not account for variations in signal patterns across different patient
populations or over even longer periods. This could lead to algorithms that perform well for some users but
less optimally for others. To mitigate these potential biases, future research should focus on expanding the
diversity of participants in RPNI studies, considering factors such as age, gender, and amputation type.
Furthermore, developing adaptive algorithms that can continuously learn and adjust to individual user
patterns over extended periods could help address potential biases and improve the generalizability of
RPNI-based prosthetic control systems.

DECLARATIONS
Acknowledgments
The Authors would like to thank Alex Vaskov, PhD, for providing surgical images, flow diagrams, and
expertise in mechanical engineering and machine learning algorithms.

Authors’ contributions
Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration,
Resources, Supervision, Validation, Writing - original draft, Writing - review & editing: Wang MJ
Validation, Writing - original draft, Writing - review & editing: Cubillos LH
Supervision, Validation, Writing - original draft, Writing - review & editing: Kung TA, Kemp SWP, Snyder-
Warwick AK
Corresponding Author, Conceptualization, Supervision, Validation, Writing - original draft, Writing -
review & editing: Cederna PS

Availability of data and materials
Not applicable.

Financial support and sponsorship
None.

Conflicts of interest
Cederna PS, serves as President of Blue Arbor Technologies, Inc., a company that designs, manufactures,
and produces neural prosthetic control systems. None of the Blue Arbor Technology products is discussed
in this article. No funding was received from any source for this article.
Kung TA, serves as Chief Medical Officer of Blue Arbor Technologies, Inc., a company that designs,
manufactures, and produces neural prosthetic control systems. None of the Blue Arbor Technology
products is discussed in this article. No funding was received from any source for this article.
Snyder-Warwick AK has no disclosures relevant to this work. She has received research funding from
Checkpoint Surgical.
Kemp SWP, Wang MJ and Cubillos LH have no disclosures.

Ethical approval and consent to participate
Not applicable.

Consent for publication
Not applicable.

Copyright
© The Author(s) 2025.