The current role of the virtual elements of artificial intelligence in total knee arthroplasty

Abstract

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.

Novel Machine Learning Approach to Predict and Personalize Length of Stay for Patients Admitted with Syncope from the Emergency Departmen

“…ML has previously been used in patients with inflammatory bowel disease to accurately identify high-need, high-cost patients and LoS [ 20 ], as well as ICU mortality and LoS [ 21 ]. Similarly, virtual elements of artificial intelligence can predict LoS in patients undergoing total knee arthroplasty [ 22 ], lumbar decompression surgery [ 23 ], craniotomy clipping, [ 24 ] and other conditions.…”

A role for artificial intelligence applications inside and outside of the operating theatre: a review of contemporary use associated with total knee arthroplasty

“…Given the inherent reliance on information garnered from diagnostic imaging studies in many areas of medicine, including surgery, there is a natural opportunity to test and apply AI’s interpretive capabilities in this context. When looking specifically at knee arthroplasty, the majority of reported work to date has been related to either the diagnosis of OA [ 1 , 15 , 32 , 33 ] and/or the grading of OA [ 1 ] from plain radiographs [ 34 , 35 ], or the identification of in situ implant components [ 34 , 36 – 38 ]. Relatively simple diagnostic analyses based on single AP X-rays have been reported [ 32 , 34 ], as well as more sophisticated works using customized, multi-planar, imaging protocols [ 39 ].…”

“…Modern AI applications have already been reported to be accurate in identifying [ 15 , 33 ] both the manufacturer and model [ 34 ] of in situ TKA components. Using human observers as the comparative standard [ 36 ], most AI algorithms report > 90% accuracy [ 36 ] and consistently outperform senior orthopaedic specialists in this regard [ 38 ].…”

“…Other currently novel/less extensively studied imaging-based AI applications in knee arthroplasty include the accurate diagnosis of component loosening from plain film X-rays [ 33 , 36 ], prediction of postoperative hip-knee-ankle axis (HKAA) [ 31 ], determination of mechanical and implant alignment from standing long leg images [ 10 ], and the semi-automated 3D reconstruction of sectional CT images to inform subsequent surgical robotic planning [ 44 ]. While certainly tantalizing extensions of image-based AI work in knee arthroplasty, further confirmatory evidence is required to support consideration of wider adoption [ 12 ].…”

Predictive Tool Use and Willingness for Surgery in Patients With Knee Osteoarthritis

“…Rapid advancements in artificial intelligence technologies have led to the development of numerous clinical predictive tools, including those for patients with knee osteoarthritis (OA) considering total knee arthroplasty (TKA). 1 , 2 , 3 , 4 , 5 While the integration of predictive tools into clinical practice is progressing quickly, few of these tools have undergone rigorous evaluation through randomized clinical trials (RCTs). 6 , 7 Consequently, the effectiveness of these tools in surgical decision-making for both patients and clinicians remains uncertain.…”