Turimov Mustapoevich Dilmurod, Kim Wooseong
Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 461-701, Gyeonggi-do, Republic of Korea.
Healthcare (Basel). 2023 Sep 7;11(18):2483. doi: 10.3390/healthcare11182483.
This extensive review examines sarcopenia, a condition characterized by a loss of muscle mass, stamina, and physical performance, with a particular emphasis on its detection and management using contemporary technologies. It highlights the lack of global agreement or standardization regarding the definition of sarcopenia and the various techniques used to measure muscle mass, stamina, and physical performance. The distinctive criteria employed by the European Working Group on Sarcopenia in Older People (EWGSOP) and the Asian Working Group for Sarcopenia (AWGSOP) for diagnosing sarcopenia are examined, emphasizing potential obstacles in comparing research results across studies. The paper delves into the use of machine learning techniques in sarcopenia detection and diagnosis, noting challenges such as data accessibility, data imbalance, and feature selection. It suggests that wearable devices, like activity trackers and smartwatches, could offer valuable insights into sarcopenia progression and aid individuals in monitoring and managing their condition. Additionally, the paper investigates the potential of blockchain technology and edge computing in healthcare data storage, discussing models and systems that leverage these technologies to secure patient data privacy and enhance personal health information management. However, it acknowledges the limitations of these models and systems, including inefficiencies in handling large volumes of medical data and the lack of dynamic selection capability. In conclusion, the paper provides a comprehensive summary of current sarcopenia research, emphasizing the potential of modern technologies in enhancing the detection and management of the condition while also highlighting the need for further research to address challenges in standardization, data management, and effective technology use.
这篇全面的综述探讨了肌肉减少症，这是一种以肌肉质量、耐力和身体机能丧失为特征的病症，特别强调了使用当代技术对其进行检测和管理。它突出了在肌肉减少症的定义以及用于测量肌肉质量、耐力和身体机能的各种技术方面缺乏全球共识或标准化。文中研究了老年人肌肉减少症欧洲工作组（EWGSOP）和亚洲肌肉减少症工作组（AWGSOP）用于诊断肌肉减少症的独特标准，强调了在比较不同研究结果时可能存在的障碍。该论文深入探讨了机器学习技术在肌肉减少症检测和诊断中的应用，指出了数据可获取性、数据不平衡和特征选择等挑战。它表明，像活动追踪器和智能手表这样的可穿戴设备可以为肌肉减少症的进展提供有价值的见解，并帮助个人监测和管理自身状况。此外，论文还研究了区块链技术和边缘计算在医疗数据存储方面的潜力，讨论了利用这些技术来保护患者数据隐私和加强个人健康信息管理的模型和系统。然而，它也承认这些模型和系统存在局限性，包括处理大量医疗数据时的低效率以及缺乏动态选择能力。总之，该论文对当前肌肉减少症的研究进行了全面总结，强调了现代技术在加强该病症检测和管理方面的潜力，同时也突出了进一步研究以应对标准化、数据管理和有效技术应用方面挑战的必要性。
