• Victoria Ashley Lang, Torbjörn Lundh, and Max Ortiz-Catalan.
• Center for Bionics and Pain Research, Sweden.
• Pain Med. 2021 Dec 11; 22 (12): 2806-2817.

ObjectiveThere is no single prevailing theory of pain that explains its origin, qualities, and alleviation. Although many studies have investigated various molecular targets for pain management, few have attempted to examine the etiology or working mechanisms of pain through mathematical or computational model development. In this systematic review, we identified and classified mathematical and computational models for characterizing pain.MethodsThe databases queried were Science Direct and PubMed, yielding 560 articles published prior to January 1st, 2020. After screening for inclusion of mathematical or computational models of pain, 31 articles were deemed relevant.ResultsMost of the reviewed articles utilized classification algorithms to categorize pain and no-pain conditions. We found the literature heavily focused on the application of existing models or machine learning algorithms to identify the presence or absence of pain, rather than to explore features of pain that may be used for diagnostics and treatment.ConclusionsAlthough understudied, the development of mathematical models may augment the current understanding of pain by providing directions for testable hypotheses of its underlying mechanisms. Additional focus is needed on developing models that seek to understand the underlying mechanisms of pain, as this could potentially lead to major breakthroughs in its treatment.© The Author(s) 2021. Published by Oxford University Press on behalf of the American Academy of Pain Medicine.

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