Department of Biomedical Engineering, University of Illinois Chicago, Chicago, IL, USA.
Department of Civil, Materials, and Environmental Engineering, University of Illinois Chicago, Chicago, IL, USA.
Med Biol Eng Comput. 2023 Jun;61(6):1239-1255. doi: 10.1007/s11517-023-02779-1. Epub 2023 Jan 26.
The digital health industry is experiencing fast-paced research which can provide digital care programs and technologies to enhance the competence of healthcare delivery. Orthopedic literature also confirms the applicability of artificial intelligence (AI) and machine learning (ML) models to medical diagnosis and clinical decision-making. However, implant monitoring after primary surgery often happens with a wellness visit or when a patient complains about it. Neglecting implant design and other technical errors in this scenario, unmonitored circumstances, and lack of post-surgery monitoring may ultimately lead to the implant system's failure and leave us with the only option of high-risk revision surgery. Preventive maintenance seems to be a good choice to identify the onset of an irreversible prosthesis failure. Considering all these aspects for hip implant monitoring, this paper explores existing studies linking ML models and intelligent systems for hip implant diagnosis. This paper explores the feasibility of an alternative continuous monitoring technique for post-surgery implant monitoring backed by an in vitro ML case study. Tribocorrosion and acoustic emission (AE) data are considered based on their efficacy in determining irreversible alteration of implant material to prevent total failures. This study also facilitates the relevance of developing an artificially intelligent implant monitoring methodology that can function with daily patient activities and how it can influence the digital orthopedic diagnosis. AI-based non-invasive hip implant monitoring system enabling point-of-care testing.
数字健康产业正在进行快速的研究，可以提供数字护理计划和技术，以提高医疗保健提供的能力。骨科文献也证实了人工智能（AI）和机器学习（ML）模型在医学诊断和临床决策中的适用性。然而，初次手术后的植入物监测通常发生在健康检查时，或者当患者抱怨时。在这种情况下，忽略植入物设计和其他技术错误、未监测的情况以及缺乏手术后监测可能最终导致植入物系统的失败，使我们只能选择高风险的翻修手术。预防性维护似乎是一种识别不可逆转假体失效开始的好方法。考虑到髋关节植入物监测的所有这些方面，本文探讨了将机器学习模型和智能系统用于髋关节植入物诊断的现有研究。本文探讨了基于体外机器学习案例研究的术后植入物监测的替代连续监测技术的可行性。基于它们在确定植入材料不可逆变化以防止完全失效方面的功效，考虑了摩擦腐蚀和声发射（AE）数据。本研究还促进了开发一种人工智能植入物监测方法的相关性，该方法可以与日常患者活动一起使用，以及它如何影响数字骨科诊断。基于人工智能的非侵入性髋关节植入物监测系统，实现即时护理测试。
