REFERENCES
1. Molina CS, Faulk J. Lower extremity amputation. StatPearls Publishing: 2024. Available from: https://www.ncbi.nlm.nih.gov/books/NBK546594/. [Last accessed on 22 Apr 2025].
2. Ziegler-Graham K, MacKenzie EJ, Ephraim PL, Travison TG, Brookmeyer R. Estimating the prevalence of limb loss in the United States: 2005 to 2050. Arch Phys Med Rehabil. 2008;89:422-9.
3. Goodney PP, Beck AW, Nagle J, Welch HG, Zwolak RM. National trends in lower extremity bypass surgery, endovascular interventions, and major amputations. J Vasc Surg. 2009;50:54-60.
4. Abu Dabrh AM, Steffen MW, Undavalli C, et al. The natural history of untreated severe or critical limb ischemia. J Vasc Surg. 2015;62:1642-51.e3.
5. Humphries MD, Brunson A, Li CS, Melnikow J, Romano PS. Amputation trends for patients with lower extremity ulcers due to diabetes and peripheral artery disease using statewide data. J Vasc Surg. 2016;64:1747-55.e3.
6. Fortington LV, Geertzen JH, van Netten JJ, Postema K, Rommers GM, Dijkstra PU. Short and long term mortality rates after a lower limb amputation. Eur J Vasc Endovasc Surg. 2013;46:124-31.
7. Oh TS, Lee HS, Hong JP. Diabetic foot reconstruction using free flaps increases 5-year-survival rate. J Plast Reconstr Aesthet Surg. 2013;66:243-50.
8. Gailey R, Allen K, Castles J, Kucharik J, Roeder M. Review of secondary physical conditions associated with lower-limb amputation and long-term prosthesis use. J Rehabil Res Dev. 2008;45:15-29.
9. Kuiken TA, Fey NP, Reissman T, Finucane SB, Dumanian GA. Innovative use of thighplasty to improve prosthesis fit and function in a transfemoral amputee. Plast Reconstr Surg Glob Open. 2018;6:e1632.
10. Herr HM, Clites TR, Srinivasan S, et al. Reinventing extremity amputation in the era of functional limb restoration. Ann Surg. 2021;273:269-79.
11. Sinha R, van den Heuvel WJ, Arokiasamy P. Factors affecting quality of life in lower limb amputees. Prosthet Orthot Int. 2011;35:90-6.
12. van der Schans CP, Geertzen JH, Schoppen T, Dijkstra PU. Phantom pain and health-related quality of life in lower limb amputees. J Pain Symptom Manage. 2002;24:429-36.
13. Reid RT, Johnson CC, Gaston RG, Loeffler BJ. Impact of timing of targeted muscle reinnervation on pain and opioid intake following major limb amputation. Hand. 2024;19:200-5.
14. Kuiken TA, Barlow AK, Hargrove L, Dumanian GA. Targeted muscle reinnervation for the upper and lower extremity. Tech Orthop. 2017;32:109-16.
15. Dumanian GA, Potter BK, Mioton LM, et al. Targeted muscle reinnervation treats neuroma and phantom pain in major limb amputees: a randomized clinical trial. Ann Surg. 2019;270:238-46.
16. Mauch JT, Kao DS, Friedly JL, Liu Y. Targeted muscle reinnervation and regenerative peripheral nerve interfaces for pain prophylaxis and treatment: a systematic review. PM R. 2023;15:1457-65.
17. Mohanty AJ, Cederna PS, Kemp SWP, Kung TA. Prophylactic regenerative peripheral nerve interface (RPNI) surgery in pediatric lower limb amputation patients. Ann Surg. 2024.
18. Fleming A, Stafford N, Huang S, Hu X, Ferris DP, Huang HH. Myoelectric control of robotic lower limb prostheses: a review of electromyography interfaces, control paradigms, challenges and future directions. J Neural Eng. 2021;18:041004.
19. Sup F, Bohara A, Goldfarb M. Design and control of a powered transfemoral prosthesis. Int J Rob Res. 2008;27:263-73.
20. Huang H, Kuiken TA, Lipschutz RD. A strategy for identifying locomotion modes using surface electromyography. IEEE Trans Biomed Eng. 2009;56:65-73.
21. Hargrove LJ, Simon AM, Young AJ, et al. Robotic leg control with EMG decoding in an amputee with nerve transfers. N Engl J Med. 2013;369:1237-42.
22. Keller M, Guebeli A, Thieringer F, Honigmann P. Artificial intelligence in patient-specific hand surgery: a scoping review of literature. Int J Comput Assist Radiol Surg. 2023;18:1393-403.
23. Komura D, Ishikawa S. Machine learning methods for histopathological image analysis. Comput Struct Biotechnol J. 2018;16:34-42.
24. Siontis KC, Noseworthy PA, Attia ZI, Friedman PA. Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. Nat Rev Cardiol. 2021;18:465-78.
25. Dai L, Zhou Q, Zhou H, et al. Deep learning-based classification of lower extremity arterial stenosis in computed tomography angiography. Eur J Radiol. 2021;136:109528.
26. Zhang JL, Conlin CC, Li X, et al. Exercise-induced calf muscle hyperemia: rapid mapping of magnetic resonance imaging using deep learning approach. Physiol Rep. 2020;8:e14563.
27. McDermott MM. Lower extremity manifestations of peripheral artery disease: the pathophysiologic and functional implications of leg ischemia. Circ Res. 2015;116:1540-50.
28. Hippe DS, Balu N, Chen L, et al. Confidence weighting for robust automated measurements of popliteal vessel wall magnetic resonance imaging. Circ Genom Precis Med. 2020;13:e002870.
29. Chen L, Canton G, Liu W, et al. Fully automated and robust analysis technique for popliteal artery vessel wall evaluation (FRAPPE) using neural network models from standardized knee MRI. Magn Reson Med. 2020;84:2147-60.
30. Kim S, Hahn JO, Youn BD. Detection and severity assessment of peripheral occlusive artery disease via deep learning analysis of arterial pulse waveforms: proof-of-concept and potential challenges. Front Bioeng Biotechnol. 2020;8:720.
31. Allen J, Liu H, Iqbal S, Zheng D, Stansby G. Deep learning-based photoplethysmography classification for peripheral arterial disease detection: a proof-of-concept study. Physiol Meas. 2021;42:054002.
32. Chemello G, Salvatori B, Morettini M, Tura A. Artificial intelligence methodologies applied to technologies for screening, diagnosis and care of the diabetic foot: a narrative review. Biosensors. 2022;12:985.
33. Howard T, Ahluwalia R, Papanas N. The advent of artificial intelligence in diabetic foot medicine: a new horizon, a new order, or a false dawn? Int J Low Extrem Wounds. 2023;22:635-40.
34. Cassidy B, Hoon Yap M, Pappachan JM, et al. Artificial intelligence for automated detection of diabetic foot ulcers: a real-world proof-of-concept clinical evaluation. Diabetes Res Clin Pract. 2023;205:110951.
35. Chung J, Freeman NLB, Kosorok MR, Marston WA, Conte MS, McGinigle KL. Analysis of a machine learning-based risk stratification scheme for chronic limb-threatening ischemia. JAMA Netw Open. 2022;5:e223424.
36. Oei CW, Chan YM, Zhang X, et al. Risk prediction of diabetic foot amputation using machine learning and explainable artificial intelligence. J Diabetes Sci Technol. 2024:19322968241228606.
37. Tjardes T, Marche B, Imach S. Mangled extremity: limb salvage for reconstruction versus primary amputation. Curr Opin Crit Care. 2023;29:682-8.
38. Perkins ZB, Yet B, Sharrock A, et al. Predicting the outcome of limb revascularization in patients with lower-extremity arterial trauma: development and external validation of a supervised machine-learning algorithm to support surgical decisions. Ann Surg. 2020;272:564-72.
39. Soffin EM, Lee BH, Kumar KK, Wu CL. The prescription opioid crisis: role of the anaesthesiologist in reducing opioid use and misuse. Br J Anaesth. 2019;122:e198-208.
40. Lawal OD, Gold J, Murthy A, et al. Rate and risk factors associated with prolonged opioid use after surgery: a systematic review and meta-analysis. JAMA Netw Open. 2020;3:e207367.
41. Gabriel RA, Harjai B, Prasad RS, et al. Machine learning approach to predicting persistent opioid use following lower extremity joint arthroplasty. Reg Anesth Pain Med. 2022;47:313-9.
42. Ortiz-Catalan M, Guðmundsdóttir RA, Kristoffersen MB, et al. Phantom motor execution facilitated by machine learning and augmented reality as treatment for phantom limb pain: a single group, clinical trial in patients with chronic intractable phantom limb pain. Lancet. 2016;388:2885-94.
43. Romeo-Guitart D, Forés J, Herrando-Grabulosa M, et al. Neuroprotective drug for nerve trauma revealed using artificial intelligence. Sci Rep. 2018;8:1879.
44. Daeschler SC, Bourget MH, Derakhshan D, et al. Rapid, automated nerve histomorphometry through open-source artificial intelligence. Sci Rep. 2022;12:5975.
45. Huang Y, Wu W, Liu H, et al. 3D printing of functional nerve guide conduits. Burns Trauma. 2021;9:tkab011.
46. Xiao B, Feturi F, Su AA, et al. Nerve wrap for local delivery of FK506/tacrolimus accelerates nerve regeneration. Int J Mol Sci. 2024;25:847.
47. Guo JL, Januszyk M, Longaker MT. Machine learning in tissue engineering. Tissue Eng Part A. 2023;29:2-19.
48. Li F, Han J, Cao T, et al. Design of self-assembly dipeptide hydrogels and machine learning via their chemical features. Proc Natl Acad Sci U S A. 2019;116:11259-64.
49. Kosuri S, Borca CH, Mugnier H, et al. Machine-assisted discovery of chondroitinase ABC complexes toward sustained neural regeneration. Adv Healthc Mater. 2022;11:e2102101.
50. Miller WC, Speechley M, Deathe AB. Balance confidence among people with lower-limb amputations. Phys Ther. 2002;82:856-65.
51. Miller WC, Speechley M, Deathe B. The prevalence and risk factors of falling and fear of falling among lower extremity amputees. Arch Phys Med Rehabil. 2001;82:1031-7.
52. Blanke O. Multisensory brain mechanisms of bodily self-consciousness. Nat Rev Neurosci. 2012;13:556-71.
53. Jaegers SM, Arendzen JH, de Jongh HJ. Prosthetic gait of unilateral transfemoral amputees: a kinematic study. Arch Phys Med Rehabil. 1995;76:736-43.
54. Crea S, Cipriani C, Donati M, Carrozza MC, Vitiello N. Providing time-discrete gait information by wearable feedback apparatus for lower-limb amputees: usability and functional validation. IEEE Trans Neural Syst Rehabil Eng. 2015;23:250-7.
55. Fan RE, Culjat MO, King CH, et al. A haptic feedback system for lower-limb prostheses. IEEE Trans Neural Syst Rehabil Eng. 2008;16:270-7.
56. Dietrich C, Nehrdich S, Seifert S, et al. Leg prosthesis with somatosensory feedback reduces phantom limb pain and increases functionality. Front Neurol. 2018;9:270.
57. Crea S, Edin BB, Knaepen K, Meeusen R, Vitiello N. Time-discrete vibrotactile feedback contributes to improved gait symmetry in patients with lower limb amputations: case series. Phys Ther. 2017;97:198-207.
59. Tan DW, Schiefer MA, Keith MW, Anderson JR, Tyler J, Tyler DJ. A neural interface provides long-term stable natural touch perception. Sci Transl Med. 2014;6:257ra138.
60. Davis TS, Wark HA, Hutchinson DT, et al. Restoring motor control and sensory feedback in people with upper extremity amputations using arrays of 96 microelectrodes implanted in the median and ulnar nerves. J Neural Eng. 2016;13:036001.
61. Charkhkar H, Shell CE, Marasco PD, Pinault GJ, Tyler DJ, Triolo RJ. High-density peripheral nerve cuffs restore natural sensation to individuals with lower-limb amputations. J Neural Eng. 2018;15:056002.
62. Koh RGL, Balas M, Nachman AI, Zariffa J. Selective peripheral nerve recordings from nerve cuff electrodes using convolutional neural networks. J Neural Eng. 2020;17:016042.
63. Petrini FM, Valle G, Bumbasirevic M, et al. Enhancing functional abilities and cognitive integration of the lower limb prosthesis. Sci Transl Med. 2019;11:eaav8939.
64. Zelechowski M, Valle G, Raspopovic S. A computational model to design neural interfaces for lower-limb sensory neuroprostheses. J Neuroeng Rehabil. 2020;17:24.
65. Hebert JS, Rehani M, Stiegelmar R. Osseointegration for lower-limb amputation: a systematic review of clinical outcomes. JBJS Rev. 2017;5:e10.
66. Lu L, Zhang J, Guan K, Zhou J, Yuan F, Guan Y. Artificial neural network for cytocompatibility and antibacterial enhancement induced by femtosecond laser micro/nano structures. J Nanobiotechnology. 2022;20:365.
67. Revilla-León M, Gómez-Polo M, Vyas S, et al. Artificial intelligence applications in implant dentistry: a systematic review. J Prosthet Dent. 2023;129:293-300.
