Computational and AI-assisted modeling of human hand peripheral nerves: a systematic review

Abstract

The peripheral nerves of the human hand play a crucial role in sensorimotor function. However, their complex anatomy, multiscale biomechanics, and electrically coupled behavior make them difficult to model computationally. Recent advances in medical imaging, finite element modeling, and data-driven methods have enabled the development of increasingly realistic models of nerve mechanics and signal conduction. The results obtained from modeling nerve mechanics and electrical conduction can provide useful insight into the underlying mechanisms of several neuropathies such as carpal tunnel syndrome (CTS), diabetic neuropathy, and traumatic nerve injury. This systematic review examined key modeling strategies, including the use of anatomical reconstruction, finite element analysis (FEA) for stress and strain analysis, computer simulations of the electrophysiological activity of nerves to model nerve conduction, multi-physics analyses, and artificial intelligence (AI)-assisted modeling approaches. A systematic literature search was conducted in PubMed, IEEE Xplore, Scopus, and Web of Science, covering studies published up to December 2025 to identify studies related to computational and AI-assisted modeling of the human hand’s peripheral nerves. After removing duplicates, screening, and eligibility assessment, 50 studies were included. The literature was synthesized across four thematic areas: anatomical reconstruction and imaging-based models, biomechanical and finite element modeling, electrophysiological and conduction models, and AI-assisted data-driven approaches. Comparative analysis reveals substantial variability in the modeling assumptions, validation strategies, and clinical relevance, with limited consensus on standardized evaluation metrics. Persistent gaps include the inadequate representation of nerve branching networks, insufficient patient-specific connective tissue modeling, and weak experimental or clinical validation. Despite advances in peripheral nerve modeling, no comprehensive review exists focusing specifically on anatomically accurate computational modeling of the human hand’s nerves. Future work should improve the alignment of imaging and computational pipelines, develop hybrid electro-mechanical and AI-enhanced frameworks, and enhance translation into surgical planning and neuroprosthetics.

Keywords

computational modeling / AI-assisted modeling / finite element modeling / median nerve

References

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