Vulpe Diana Elena, Anghel Catalin, Scheau Cristian, Dragosloveanu Serban, Săndulescu Oana
The "Carol Davila" University of Medicine and Pharmacy, 050474 Bucharest, Romania.
Department of Orthopaedics, "Foisor" Clinical Hospital of Orthopaedics, Traumatology and Osteoarticular TB, 021382 Bucharest, Romania.
Biomedicines. 2025 Jul 30;13(8):1855. doi: 10.3390/biomedicines13081855.
Periprosthetic joint infections (PJIs) represent one of the most problematic complications following total joint replacement, with a significant impact on the patient's quality of life and healthcare costs. The early and accurate diagnosis of a PJI remains the key factor in the management of such cases. However, with traditional diagnostic measures and risk assessment tools, the early identification of a PJI may not always be adequate. Artificial intelligence (AI) algorithms have been integrated in most technological domains, with recent integration into healthcare, providing promising applications due to their capability of analyzing vast and complex datasets. With the development and implementation of AI algorithms, the assessment of risk factors and the prediction of certain complications have become more efficient. This review aims to not only provide an overview of the current use of AI in predicting PJIs, the exploration of the types of algorithms used, and the performance metrics reported, but also the limitations and challenges that come with implementing such tools in clinical practice.
人工关节周围感染（PJIs）是全关节置换术后最棘手的并发症之一，对患者的生活质量和医疗成本有重大影响。PJI的早期准确诊断仍然是此类病例管理的关键因素。然而，使用传统的诊断措施和风险评估工具，PJI的早期识别可能并不总是足够的。人工智能（AI）算法已被集成到大多数技术领域，最近也被应用于医疗保健领域，由于其能够分析大量复杂数据集，因此具有广阔的应用前景。随着AI算法的开发和应用，风险因素的评估和某些并发症的预测变得更加高效。本综述旨在不仅概述AI在预测PJI方面的当前应用、所使用算法类型及报告的性能指标，还探讨在临床实践中应用此类工具的局限性和挑战。
