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.
Citation: Gul Munir, Muhammad Zeeshan Ul Haque, Muhammad Fahad Shamim, Muhammad Wasim Munir, Tooba Khan. Computational and AI-assisted modeling of human hand peripheral nerves: a systematic review[J]. AIMS Bioengineering, 2026, 13(2): 168-208. doi: 10.3934/bioeng.2026008
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.
| [1] |
Keith MW, Masear V, Chung KC, et al. (2009) American academy of orthopaedic surgeons clinical practice guideline on: diagnosis of carpal tunnel syndrome. J Bone Joint Surg Am 91: 2478-2479. https://doi.org/10.2106/JBJS.I.00643 
|
| [2] |
Dawson DM (1993) Entrapment neuropathies of the upper extremities. N Engl J Med 329: 2013-2018. https://doi.org/10.1056/NEJM199312303292707
|
| [3] | American Academy of Physical Medicine and Rehabilitation.Carpal Tunnel Syndrome. AAPM&R KnowledgeNow (2023) . Available from: https://now.aapmr.org/carpal-tunnel-syndrome |
| [4] |
Nakano KK (1997) Nerve entrapment syndromes. Curr Opin Rheumatol 9: 165-173. https://doi.org/10.1097/00002281-199703000-00015
|
| [5] |
Seddon H (1942) A classification of nerve injuries. Br Med J 2: 237-239. https://doi.org/10.1136/bmj.2.4260.237
|
| [6] |
Walia P, Erdemir A, Li ZM (2017) Subject-specific finite element analysis of the carpal tunnel cross-sectional to examine tunnel area changes in response to carpal arch loading. Clin Biomech 42: 25-30. https://doi.org/10.1016/j.clinbiomech.2017.02.017
|
| [7] |
Peshin S, Karakulova Y, Kuchumov AG (2023) Finite element modeling of the fingers and wrist flexion/extension effect on median nerve compression. Appl Sci 13: 1219. https://doi.org/10.3390/app13021219
|
| [8] |
Yu L, Jia J, Lakshminarayanan K, et al. (2023) A finite element analysis of the carpal arch with various locations of carpal tunnel release. Front Surg 10: 1134129. https://doi.org/10.3389/fsurg.2023.1134129
|
| [9] |
Peng L, Wu Y, Lakshminarayanan K, et al. (2023) The relationship between shear wave velocity in transverse carpal ligament and carpal tunnel pressure: a finite element analysis. Med Eng Phys 116: 103995. https://doi.org/10.1016/j.medengphy.2023.103995
|
| [10] |
Dyck PJ, Giannini C (1996) Pathologic alterations in the diabetic neuropathies of humans: a review. J Neuropathol Exp Neurol 55: 1181-1193. https://doi.org/10.1097/00005072-199612000-00001
|
| [11] |
Tekieh T, Shahzadi S, Rafii-Tabar H, et al. (2016) Are deformed neurons electrophysiologically altered? A simulation study. Curr Appl Phys 16: 1413-1417. https://doi.org/10.1016/j.cap.2016.07.012
|
| [12] |
Vasas NC, Forrest AM, Meyers NA, et al. (2024) A finite element model for biomechanical characterization of ex vivo peripheral nerve dysfunction during stretch. Physiol Rep 12: e70125. https://doi.org/10.14814/phy2.70125
|
| [13] |
Chaudhary N, Pandey AS, Gemmete JJ (2013) Endovascular treatment of adult spinal arteriovenous lesions. Neuroimaging Clin N Am 23: 729-747. https://doi.org/10.1016/j.nic.2013.03.008
|
| [14] |
Dembek TA, Petry-Schmelzer JN, Reker P, et al. (2020) PSA and VIM DBS efficiency in essential tremor depends on distance to the dentatorubrothalamic tract. Neuroimage Clin 26: 102235. https://doi.org/10.1016/j.nicl.2020.102235
|
| [15] |
Hodgkin AL, Huxley AF (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol 117: 500-544. https://doi.org/10.1113/jphysiol.1952.sp004764
|
| [16] |
Rattay F (2007) Analysis of models for external stimulation of axons. IEEE Trans Biomed Eng 54: 974-977. https://doi.org/10.1109/TBME.2006.889192
|
| [17] |
D'Arcy CA, McGee S (2000) Does this patient have carpal tunnel syndrome?. JAMA 283: 3110-3117. https://doi.org/10.1001/jama.283.23.3110
|
| [18] |
Hiltunen J, Suortti T, Arvela S, et al. (2005) Diffusion tensor imaging and tractography of distal peripheral nerves at 3 T. Clin Neurophysiol 116: 2315-2323. https://doi.org/10.1016/j.clinph.2005.06.009
|
| [19] |
Takagi T, Nakamura M, Yamada M, et al. (2009) Visualization of peripheral nerve degeneration and regeneration: monitoring with diffusion tensor tractography. Neuroimage 44: 884-892. https://doi.org/10.1016/j.neuroimage.2008.09.022
|
| [20] |
Yoon D, Lutz AM (2023) Diffusion tensor imaging of peripheral nerves: current status and new developments. Semin Musculoskelet Radiol 27: 641-648. https://doi.org/10.1055/s-0043-1770916
|
| [21] |
Wolny T, Glibov K, Wieczorek M, et al. (2024) Changes in ultrasound parameters of the median nerve at different positions of the radiocarpal joint in patients with carpal tunnel syndrome. Sensors 24: 4487. https://doi.org/10.3390/s24144487
|
| [22] |
Maki Y, Takayama M, Okawa T, et al. (2024) Diffusion tensor imaging combined with the dual-echo steady-state (DESS) protocol for the evaluation of the median nerve in the carpal tunnel: a preliminary study. Surg Neurol Int 15: 110. https://doi.org/10.25259/SNI_110_2024
|
| [23] |
Heckel A, Weiler M, Xia A, et al. (2015) Peripheral nerve diffusion tensor imaging: assessment of axon and myelin sheath integrity. PLoS One 10: e0130833. https://doi.org/10.1371/journal.pone.0130833
|
| [24] |
Anderson DA, Oliver ML, Gordon KD (2022) Carpal tunnel volume distribution and morphology changes with flexion-extension and radial-ulnar deviation wrist postures. PLoS One 17: e0277234. https://doi.org/10.1371/journal.pone.0277234
|
| [25] |
Jordan D, Li ZM (2022) Cross-sectional changes of the distal carpal tunnel with simulated carpal bone rotation. Comput Methods Biomech Biomed Engin 25: 1599-1607. https://doi.org/10.1080/10255842.2022.2051234
|
| [26] |
Cheever D, Kirk B, Miles R, et al. (1995) A mathematical model of the pathophysiology of carpal tunnel syndrome. Proc IEEE Northeast Bioeng Conf 21: 57-60. https://doi.org/10.1109/NEBC.1995.513732
|
| [27] |
Oflaz H, Gunal I (2019) Maximum loading of carpal bones during movements: a finite element study. Eur J Orthop Surg Traumatol 29: 47-50. https://doi.org/10.1007/s00590-018-2271-0
|
| [28] |
Wei Y, Zou Z, Wei G, et al. (2020) Subject-specific finite element modelling of the human hand complex: muscle-driven simulations and experimental validation. Ann Biomed Eng 48: 1181-1195. https://doi.org/10.1007/s10439-019-02421-3
|
| [29] |
Piao CD, Yang K, Li P, et al. (2015) Autologous nerve graft repair of different degrees of sciatic nerve defect: stress and displacement at the anastomosis in a three-dimensional finite element simulation model. Neural Regen Res 10: 804-807. https://doi.org/10.4103/1673-5374.156986
|
| [30] |
Chang CT, Chen YH, Lin CCK, et al. (2015) Finite element modeling of hyper-viscoelasticity of peripheral nerve ultrastructures. J Biomech 48: 1982-1987. https://doi.org/10.1016/j.jbiomech.2015.04.017
|
| [31] |
Zhang Q, Liu Y, Zhang J, et al. (2025) Electrophysiological analysis of distal, proximal, and dual compression of the median nerve. IBRO Neurosci Rep 19: 205-209. https://doi.org/10.1016/j.ibneur.2025.06.010
|
| [32] |
Carpio A (2005) Asymptotic construction of pulses in the discrete Hodgkin-Huxley model for myelinated nerves. Phys Rev E 72: 011905. https://doi.org/10.1103/PhysRevE.72.011905
|
| [33] |
Tigra W, Dali M, William L, et al. (2020) Selective neural electrical stimulation restores hand and forearm movements in individuals with complete tetraplegia. J Neuroeng Rehabil 17: 66. https://doi.org/10.1186/s12984-020-00686-5
|
| [34] | Sundar S, González-Cueto JA (2006) Selective activation of small nerve fibers for assessing carpal tunnel syndrome. Proc IEEE EMBS 27: 3668-3671. https://doi.org/10.1109/IEMBS.2005.1617278 |
| [35] |
Drakesmith M, Harms R, Rudrapatna SU, et al. (2019) Estimating axon conduction velocity in vivo from microstructural MRI. Neuroimage 203: 116186. https://doi.org/10.1016/j.neuroimage.2019.116186
|
| [36] |
Mena YL, Bravo MVS (2023) Comparison of electrophysiological diagnostic methods for carpal tunnel syndrome. Revista San Gregorio 1: 72-83. https://doi.org/10.36097/rsan.v1i56.2415
|
| [37] |
Shao Y, Hayward V, Visell Y (2020) Compression of dynamic tactile information in the human hand. Sci Adv 6: eaaz1158. https://doi.org/10.1126/sciadv.aaz1158
|
| [38] | Alp NB, Kaleli T, Kalay OC, et al. (2020) The effect of hamatum curvature angle on carpal tunnel volumetry: a mathematical simulation model. Comput Math Methods Med 2020: 7582181. https://doi.org/10.1155/2020/7582181 |
| [39] |
Coste CA, William L, Fonseca L, et al. (2022) Activating effective functional hand movements in individuals with complete tetraplegia through neural stimulation. Sci Rep 12: 16189. https://doi.org/10.1038/s41598-022-20422-9
|
| [40] |
Elseddik M, Mostafa RR, Elashry A, et al. (2023) Predicting CTS diagnosis and prognosis based on machine learning techniques. Diagnostics 13: 492. https://doi.org/10.3390/diagnostics13030492
|
| [41] |
Shinohara I, Inui A, Mifune Y, et al. (2022) Using deep learning for ultrasound images to diagnose carpal tunnel syndrome with high accuracy. Ultrasound Med Biol 48: 2052-2059. https://doi.org/10.1016/j.ultrasmedbio.2022.05.016
|
| [42] |
Faeghi F, Ardakani AA, Acharya UR, et al. (2021) Accurate automated diagnosis of carpal tunnel syndrome using radiomics features with ultrasound images: a comparison with radiologists' assessment. Eur J Radiol 136: 109518. https://doi.org/10.1016/j.ejrad.2021.109518
|
| [43] |
Park D, Kim BH, Lee SE, et al. (2021) Machine learning-based approach for disease severity classification of carpal tunnel syndrome. Sci Rep 11: 17464. https://doi.org/10.1038/s41598-021-96933-9
|
| [44] |
Peng J, Zeng J, Lai M, et al. (2024) One-stop automated diagnostic system for carpal tunnel syndrome in ultrasound images using deep learning. Ultrasound Med Biol 50: 304-314. https://doi.org/10.1016/j.ultrasmedbio.2023.10.010
|
| [45] |
Mohammadi A, Torres-Cuenca T, Mirza-Aghazadeh-Attari F, et al. (2023) Deep radiomics features of median nerves for automated diagnosis of carpal tunnel syndrome with ultrasound images: a multi-center study. J Ultrasound Med 42: 2257-2268. https://doi.org/10.1002/jum.16252
|
| [46] |
Mekki YM, Rhim HC, Daneshvar D, et al. (2025) Applications of artificial intelligence in ultrasound imaging for carpal tunnel syndrome diagnosis: a scoping review. Int Orthop 49: 965-973. https://doi.org/10.1007/s00264-025-06497-1
|
| [47] |
Shi X, Yu T, Yuan Y, et al. (2025) Multimodal deep learning for grading carpal tunnel syndrome: a multicenter study in China. Acad Radiol 32: 4705-4723. https://doi.org/10.1016/j.acra.2025.02.043
|
| [48] |
Sim J, Lee S, Kim S, et al. (2025) Diagnosis of carpal tunnel syndrome using deep learning with comparative guidance. Clin Neurophysiol 174: 191-197. https://doi.org/10.1016/j.clinph.2025.03.038
|
| [49] |
Wang JC, Shu YC, Lin CY, et al. (2023) Application of deep learning algorithms in automatic sonographic localization and segmentation of the median nerve: a systematic review and meta-analysis. Artif Intell Med 137: 102496. https://doi.org/10.1016/j.artmed.2023.102496
|
| [50] |
Ando S, Loh PY (2024) Convolutional neural network approaches in median nerve morphological assessment from ultrasound images. J Imaging 10: 13. https://doi.org/10.3390/jimaging10010013
|
| [51] |
Cinelli I, Destrade M, Duffy M, et al. (2018) Electro-mechanical response of a 3D nerve bundle model to mechanical loads leading to axonal injury. Int J Numer Methods Biomed Eng 34: e2942. https://doi.org/10.1002/cnm.2942
|
| [52] | Peshin S, Karakulova Y, Kuchumov AG (2023) A coupled electro-mechanical approach for early diagnostic of carpal tunnel syndrome. medRxiv 2023: 2023.06.16.23291511. https://doi.org/10.1101/2023.06.16.23291511 |
| [53] |
Chen T, Li H, Yin Y, et al. (2017) Interactions of Notch1 and TLR4 signaling pathways in DRG neurons of in vivo and in vitro models of diabetic neuropathy. Sci Rep 7: 14923. https://doi.org/10.1038/s41598-017-15149-7
|
| [54] |
Andersen T, Bo APL, Watkins G, et al. (2022) Neural stimulation technologies. Principles and Technologies for Electromagnetic Energy Based Therapies . USA: Academic Press 235-254. https://doi.org/10.1016/B978-0-12-820594-5.00011-3
|
| [55] |
Raspopovic S, Capogrosso M, Micera S (2011) A computational model for the stimulation of rat sciatic nerve using a transverse intrafascicular multichannel electrode. IEEE Trans Neural Syst Rehabil Eng 19: 333-344. https://doi.org/10.1109/TNSRE.2011.2119391
|
| [56] | Snarrenberg S, Sevak BN, Patton JL (2018) Modeling nerve compression in carpal tunnel syndrome. Proc IEEE EMBS 40: 5858-5861. https://doi.org/10.1109/EMBC.2018.8513580 |
| [57] |
Chu XL, Song XZ, Li YR, et al. (2023) An ultrasound-guided percutaneous electrical nerve stimulation regimen devised using finite element modeling promotes functional recovery after median nerve transection. Neural Regen Res 18: 683-688. https://doi.org/10.4103/1673-5374.351395
|
| [58] |
Wu WT, Chang KV, Hsu YC, et al. (2023) Ultrasound imaging and guidance for distal peripheral nerve pathologies at the wrist/hand. Diagnostics 13: 1928. https://doi.org/10.3390/diagnostics13111928
|
| [59] |
Charkhkar H, Christie BP, Pinault GJ, et al. (2019) A translational framework for peripheral nerve stimulating electrodes: reviewing the journey from concept to clinic. J Neurosci Methods 328: 108414. https://doi.org/10.1016/j.jneumeth.2019.108414
|
| [60] |
Elder CW, Yoo PB (2018) A finite element modeling study of peripheral nerve recruitment by percutaneous tibial nerve stimulation in the human lower leg. Med Eng Phys 53: 32-38. https://doi.org/10.1016/j.medengphy.2017.12.004
|
| [61] |
Borrella-Andrés S, Rodríguez-Sanz J, López-de-Celis C, et al. (2025) Effect of ultrasound-guided percutaneous electrolysis and nerve stimulation on pain and function in carpal tunnel syndrome: a randomized clinical trial. Pain Med 2025: pnaf170. https://doi.org/10.1093/pm/pnaf170
|
| [62] |
Deshmukh S, Sun K, Komarraju A, et al. (2023) Peripheral nerve imaging: magnetic resonance and ultrasound correlation. Magn Reson Imaging Clin N Am 31: 181-191. https://doi.org/10.1016/j.mric.2022.12.002
|
| [63] |
Kaluskar P, Bharadwaj D, Iyer KS, et al. (2024) A systematic review to compare electrical, magnetic, and optogenetic stimulation for peripheral nerve repair. J Hand Surg Glob Online 6: 722-739. https://doi.org/10.1016/j.jhsg.2024.05.005
|
| [64] |
Rangavajla G, Mokarram N, Masoodzadehgan N, et al. (2015) Noninvasive imaging of peripheral nerves. Cells Tissues Organs 200: 69-77. https://doi.org/10.1159/000375273
|
| [65] |
Badi M, Wurth S, Scarpato I, et al. (2021) Intrafascicular peripheral nerve stimulation produces fine functional hand movements in primates. Sci Transl Med 13: eabg6463. https://doi.org/10.1126/scitranslmed.abg6463
|
| [66] |
Yildiz KA, Shin AY, Kaufman KR (2020) Interfaces with the peripheral nervous system for the control of a neuroprosthetic limb: a review. J Neuroeng Rehabil 17: 43. https://doi.org/10.1186/s12984-020-00657-w
|
| [67] |
Perevoshchikova N, Moerman KM, Akhbari B, et al. (2021) Finite element analysis of the performance of additively manufactured scaffolds for scapholunate ligament reconstruction. PLoS One 16: e0256528. https://doi.org/10.1371/journal.pone.0256528
|
| [68] |
Marqués R, Melchor J, Sánchez-Montesinos I, et al. (2022) Biomechanical finite element method model of the proximal carpal row and experimental validation. Front Physiol 12: 749372. https://doi.org/10.3389/fphys.2021.749372
|
| [69] |
Stephanova DI, Daskalova MS (2008) Differences between the channels, currents and mechanisms of conduction slowing/block and accommodative processes in simulated cases of focal demyelinating neuropathies. Eur Biophys J 37: 829-842. https://doi.org/10.1007/s00249-008-0283-9
|
| [70] |
Nordlie E, Gewaltig MO, Plesser HE (2009) Towards reproducible descriptions of neuronal network models. PLoS Comput Biol 5: e1000456. https://doi.org/10.1371/journal.pcbi.1000456
|
Figures(4) / Tables(8)
Gul Munir, Muhammad Zeeshan Ul Haque, Muhammad Fahad Shamim, Muhammad Wasim Munir, Tooba Khan. Computational and AI-assisted modeling of human hand peripheral nerves: a systematic review[J]. AIMS Bioengineering, 2026, 13(2): 168-208. doi: 10.3934/bioeng.2026008
DownLoad: