Universidad Icesi, Medical School, Cali, Colombia.
Department of Medical Microbiology, Immunology and Cell Biology, Southern Illinois University-School of Medicine, Springfield, IL, United States of America.
Autoimmun Rev. 2024 Sep;23(9):103611. doi: 10.1016/j.autrev.2024.103611. Epub 2024 Aug 28.
Autoimmune diseases comprise a spectrum of disorders characterized by the dysregulation of immune tolerance, resulting in tissue or organ damage and inflammation. Their prevalence has been on the rise, significantly impacting patients' quality of life and escalating healthcare costs. Consequently, the prediction of autoimmune diseases has recently garnered substantial interest among researchers. Despite their wide heterogeneity, many autoimmune diseases exhibit a consistent pattern of paraclinical findings that hold predictive value. From serum biomarkers to various machine learning approaches, the array of available methods has been continuously expanding. The emergence of artificial intelligence (AI) presents an exciting new range of possibilities, with notable advancements already underway. The ultimate objective should revolve around disease prevention across all levels. This review provides a comprehensive summary of the most recent data pertaining to the prediction of diverse autoimmune diseases and encompasses both traditional biomarkers and the latest innovations in AI.
自身免疫性疾病包括一大类以免疫耐受失调为特征的疾病，导致组织或器官损伤和炎症。其患病率一直在上升，严重影响患者的生活质量并推高医疗保健成本。因此，最近研究人员对预测自身免疫性疾病产生了浓厚的兴趣。尽管它们具有广泛的异质性，但许多自身免疫性疾病表现出一致的临床前发现模式，具有预测价值。从血清生物标志物到各种机器学习方法，可用方法的范围一直在不断扩大。人工智能 (AI) 的出现带来了令人兴奋的新可能性，已经取得了显著进展。最终目标应该是在各个层面预防疾病。本综述全面总结了有关预测各种自身免疫性疾病的最新数据，包括传统生物标志物和人工智能的最新创新。
