Savitz Benjamin L, Dean Yomna E, Popa Nikolas K, Cornely Ronald M, Byers Victor, Gutama Barite W, Abbott Erin N, Torres-Guzman Ricardo, Alter Noah, Stehr Justin D, Hill J Bradford, Elmaraghi Shady
From the Department of Plastic Surgery, Section of Surgical Sciences, Vanderbilt, University Medical Center, Nashville, TN.
Alexandria University, Faculty of Medicine, Alexandria, Egypt.
Ann Plast Surg. 2025 Jun 1;94(6S Suppl 4):S572-S576. doi: 10.1097/SAP.0000000000004273.
Prosthetic rehabilitation after amputation poses significant challenges, often due to functional limitations, residual limb pain (RLP), and phantom limb pain (PLP). These issues not only affect physical health but also mental well-being and quality of life. In this review, we describe targeted muscle reinnervation (TMR) and regenerative peripheral nerve interface (RPNI) and explore their clinical role in the evolution of myoelectric prosthetic control as well as postamputation pain and neuroma management. Early myoelectric prostheses, which detected electrical potentials from muscles to control prosthetic limbs, faced limitations such as inconsistent signal acquisition and complex control modes. Novel microsurgical techniques at the turn of the century such as TMR and RPNI significantly advanced myoelectric prosthetic control. TMR involves reinnervating denervated muscles with residual nerves to create electromyography (EMG) potentials and prevent painful neuromas. Similarly, RPNI relies on small muscle grafts to amplify EMG signals and distinguish from stochastic noise for refined prosthetic control. Techniques like TMR and RPNI not only improved prosthetic function, but also significantly reduced postamputation pain, making them critical in improving amputees' quality of life. Modern myoelectric prostheses evolved with advancements in microprocessor and sensor technologies, enhancing their functionality and user experience. Today, researchers have developed more intuitive and reliable prosthetic control by utilizing pattern recognition software and machine learning algorithms that may supersede reliance on surgically amplifying EMG signals. Future developments in brain-computer interfaces and machine learning hold promise for even greater advancements in prosthetic technology, emphasizing the importance of continued innovation in this field.
截肢后的假肢康复面临重大挑战，这通常是由于功能受限、残肢疼痛（RLP）和幻肢痛（PLP）所致。这些问题不仅影响身体健康，还会影响心理健康和生活质量。在本综述中，我们描述了靶向肌肉再支配（TMR）和再生外周神经接口（RPNI），并探讨了它们在肌电假肢控制发展以及截肢后疼痛和神经瘤管理中的临床作用。早期的肌电假肢通过检测肌肉的电位来控制假肢肢体，面临着诸如信号采集不一致和控制模式复杂等局限性。世纪之交出现的新型显微外科技术，如TMR和RPNI，显著推动了肌电假肢控制的发展。TMR涉及用残留神经对失神经肌肉进行再支配，以产生肌电图（EMG）电位并预防疼痛性神经瘤。同样，RPNI依靠小肌肉移植来放大EMG信号并与随机噪声区分开来，以实现精确的假肢控制。TMR和RPNI等技术不仅改善了假肢功能，还显著减轻了截肢后疼痛，使其在提高截肢者生活质量方面至关重要。现代肌电假肢随着微处理器和传感器技术的进步而不断发展，增强了其功能和用户体验。如今，研究人员通过利用模式识别软件和机器学习算法开发出了更直观、可靠的假肢控制方式，这可能会取代对通过手术放大EMG信号的依赖。脑机接口和机器学习的未来发展有望在假肢技术上取得更大进步，强调了该领域持续创新的重要性。
