Caputo Valerio, Letteri Ivan, Santini Silvano Junior, Sinatti Gaia, Balsano Clara
Department of Life, Health and Environmental Sciences, University of L'Aquila, P.le Salvatore Tommasi, 67100 L'Aquila, Italy.
Geriatric Unit, Department of Life, Health and Environmental Sciences, University of L'Aquila, P.le Salvatore Tommasi, 67100 L'Aquila, Italy.
Int J Mol Sci. 2025 May 7;26(9):4428. doi: 10.3390/ijms26094428.
Sarcopenia, a condition characterised by the progressive decline in skeletal muscle mass and function, presents significant challenges in geriatric healthcare. Despite advances in its management, complex etiopathogenesis and the heterogeneity of diagnostic criteria underlie the limited precision of existing assessment methods. Therefore, efforts are needed to improve the knowledge and pave the way for more effective management and a more precise diagnosis. To this purpose, emerging technologies such as artificial intelligence (AI) can facilitate the identification of novel and accurate biomarkers by modelling complex data resulting from high-throughput technologies, fostering the setting up of a more precise approach. Based on such considerations, this review explores AI's transformative potential, illustrating studies that integrate AI, especially machine learning and deep learning, with heterogeneous data such as clinical, anthropometric and molecular data. Overall, the present review will highlight the relevance of large-scale, standardised studies to validate biomarker signatures using AI-driven approaches.
肌肉减少症是一种以骨骼肌质量和功能进行性下降为特征的病症，给老年医疗保健带来了重大挑战。尽管在其管理方面取得了进展，但复杂的病因和诊断标准的异质性导致现有评估方法的精度有限。因此，需要努力提高认识，为更有效的管理和更精确的诊断铺平道路。为此，人工智能（AI）等新兴技术可以通过对高通量技术产生的复杂数据进行建模，促进新型准确生物标志物的识别，推动建立更精确的方法。基于这些考虑，本综述探讨了人工智能的变革潜力，阐述了将人工智能，特别是机器学习和深度学习，与临床、人体测量和分子数据等异质数据相结合的研究。总体而言，本综述将强调大规模标准化研究对于使用人工智能驱动方法验证生物标志物特征的相关性。
