关键词: 人工智能, 神经再生, 脊髓损伤, 机器外骨骼, 脑机接口, 神经电刺激, 脑皮质重组, 深度学习, 机器学习, 神经网络

Abstract: BACKGROUND: In recent years, artificial intelligence has gradually emerged and has been applied in various fields such as spinal cord nerve injury and repair, which has a positive impact on clinical treatment.
OBJECTIVE: To study the application progress of artificial intelligence in the diagnosis, treatment, and rehabilitation of spinal cord nerve injury and repair, clarify the research hotspots and shortcomings in this field, and provide suggestions for future research work.
METHODS: Relevant literature on artificial intelligence in the field of spinal cord nerve injury and repair was retrieved on the Web of Science core collection database until 2023. CiteSpace 6.1.R6 and VOSviewer 1.6.19 software was used to perform general literature analysis, co-citation of literature, co-citation of journals, double image overlay of journals, keyword clustering, and other visual analysis on the literature data.
RESULTS AND CONCLUSION: (1) A total of 1 713 articles were selected, and the annual publication volume in this field showed a fluctuating upward trend, with the United States taking the lead, and Kadone and Hideki being the authors with the highest publication volume. ARCH PHYS MED REHAB was the journal with the highest number of citations. (2) Keyword co-occurrence and cluster analysis showed that after removing keywords similar to the search terms, the main keywords were divided into three main clusters: Exoskeleton and exercise rehabilitation (the largest core hotspot); machine learning and neural plasticity; robotics and rehabilitation training. (3) Keyword burst analysis showed that deep learning and artificial intelligence had become burst terms in the past five years. (4) The results of in-depth analysis of co cited and highly cited literature showed that the hotspots of artificial intelligence in the field of spinal cord nerve injury and repair were mainly focused on powered exoskeletons, gaits, electrical nerve stimulation, intracortical brain-computer interface (IBCI), robots, and polymer biomaterials, and neural stem cell. (5) The research on artificial intelligence in the field of spinal cord nerve injury and repair has shown an upward trend in recent years. The focus of this field had gradually shifted from single treatment methods such as exoskeletons and electrical stimulation to intelligent, precise, and personalized directions. (6) There were some limitations in this field, such as the consequences of missing or imbalanced data, low data accuracy and reproducibility, and ethical issues (such as privacy, research transparency, and clinical reliability). Future research should address the issue of data collection, requiring large sample, high-quality clinical datasets to establish effective artificial intelligence models. At the same time, the research on genomics and other mechanisms in this field is very weak. In the future, various machine learning technologies such as brain chips can be used, and gene editing therapy, single-cell spatial transcriptome and other methods can be used to study the basic mechanisms of regeneration-related gene upregulation and axon growth structural protein production.

Key words: artificial intelligence, nerve regeneration, spinal cord injury, robotic exoskeleton, brain-computer interface, electrical nerve stimulation, cortical recombination, deep learning, machine learning, neural network