The current applications of the virtual elements of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in total knee arthroplasty (TKA) are diverse. ML can predict the length of stay (LOS) and costs before primary TKA, the risk of transfusion after primary TKA, postoperative dissatisfaction after TKA, the size of TKA components, and poorest outcomes. The prediction of distinct results with ML models applying specific data is already possible; nevertheless, the prediction of more complex results is still imprecise. Remote patient monitoring systems offer the ability to more completely assess the individuals experiencing TKA in terms of mobility and rehabilitation compliance. DL can accurately identify the presence of TKA, distinguish between specific arthroplasty designs, and identify and classify knee osteoarthritis as accurately as an orthopedic surgeon. DL allows for the detection of prosthetic loosening from radiographs. Regarding the architectures associated with DL, artificial neural networks (ANNs) and convolutional neural networks (CNNs), ANNs can predict LOS, inpatient charges, and discharge disposition prior to primary TKA and CNNs allow for differentiation between different implant types with near-perfect accuracy.

中文翻译：

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人工智能虚拟元素在全膝关节置换术中的当前作用。

目前人工智能 (AI)、机器学习 (ML) 和深度学习 (DL) 虚拟元素在全膝关节置换术 (TKA) 中的应用是多种多样的。ML 可以预测初次 TKA 前的住院时间 (LOS) 和费用、初次 TKA 后输血的风险、TKA 后的术后不满意、TKA 组件的大小和最差的结果。使用特定数据的 ML 模型预测不同的结果已经成为可能；然而，对更复杂结果的预测仍然不准确。远程患者监测系统能够更全面地评估经历 TKA 的个体的移动性和康复依从性。DL 可以准确识别 TKA 的存在，区分特定的关节成形术设计，并像整形外科医生一样准确地识别和分类膝关节骨性关节炎。DL 允许从射线照片中检测假体松动。关于与 DL、人工神经网络 (ANN) 和卷积神经网络 (CNN) 相关的架构，ANN 可以在初次 TKA 之前预测 LOS、住院费用和出院处置，而 CNN 允许以近乎完美的准确度区分不同的植入物类型.