Department of Joint and Sports Medicine, Zaozhuang Municipal Hospital, Affiliated to Jining Medical University, Zaozhuang, China.
Front Cell Infect Microbiol. 2024 Apr 3;14:1380136. doi: 10.3389/fcimb.2024.1380136. eCollection 2024.
Osteoporosis, arthritis, and fractures are examples of orthopedic illnesses that not only significantly impair patients' quality of life but also complicate and raise the expense of therapy. It has been discovered in recent years that the pathophysiology of orthopedic disorders is significantly influenced by the microbiota. By employing machine learning and deep learning techniques to conduct a thorough analysis of the disease-causing microbiome, we can enhance our comprehension of the pathophysiology of many illnesses and expedite the creation of novel treatment approaches. Today's science is undergoing a revolution because to the introduction of machine learning and deep learning technologies, and the field of biomedical research is no exception. The genesis, course, and management of orthopedic disorders are significantly influenced by pathogenic microbes. Orthopedic infection diagnosis and treatment are made more difficult by the lengthy and imprecise nature of traditional microbial detection and characterization techniques. These cutting-edge analytical techniques are offering previously unheard-of insights into the intricate relationships between orthopedic health and pathogenic microbes, opening up previously unimaginable possibilities for illness diagnosis, treatment, and prevention. The goal of biomedical research has always been to improve diagnostic and treatment methods while also gaining a deeper knowledge of the processes behind the onset and development of disease. Although traditional biomedical research methodologies have demonstrated certain limits throughout time, they nevertheless rely heavily on experimental data and expertise. This is the area in which deep learning and machine learning approaches excel. The advancements in machine learning (ML) and deep learning (DL) methodologies have enabled us to examine vast quantities of data and unveil intricate connections between microorganisms and orthopedic disorders. The importance of ML and DL in detecting, categorizing, and forecasting harmful microorganisms in orthopedic infectious illnesses is reviewed in this work.
骨质疏松症、关节炎和骨折是骨科疾病的例子，这些疾病不仅严重影响患者的生活质量，而且还使治疗变得复杂和增加费用。近年来发现，骨科疾病的病理生理学受微生物群的影响很大。通过运用机器学习和深度学习技术对致病微生物组进行全面分析，我们可以提高对许多疾病病理生理学的认识，并加速新的治疗方法的研发。由于机器学习和深度学习技术的引入，当今的科学正在发生一场革命，生物医学研究领域也不例外。致病微生物对骨科疾病的发生、发展和治疗都有很大的影响。传统的微生物检测和鉴定技术冗长且不准确，这使得骨科感染的诊断和治疗变得更加困难。这些先进的分析技术为骨科健康与致病微生物之间的复杂关系提供了前所未有的见解，为疾病的诊断、治疗和预防开辟了以前无法想象的可能性。生物医学研究的目标一直是改善诊断和治疗方法，同时更深入地了解疾病发生和发展的过程。尽管传统的生物医学研究方法在整个历史中已经显示出了一定的局限性，但它们仍然严重依赖于实验数据和专业知识。这正是深度学习和机器学习方法擅长的领域。机器学习（ML）和深度学习（DL）方法的进步使我们能够检查大量的数据，并揭示微生物和骨科疾病之间复杂的联系。本工作回顾了 ML 和 DL 在检测、分类和预测骨科感染性疾病中的有害微生物方面的重要性。
