Deep Learning Artificial Intelligence Model for Assessment of Hip Dislocation Risk Following Primary Total Hip Arthroplasty From Postoperative Radiographs

Rouzrokh, Pouria; Ramazanian, Taghi; Wyles, Cody C.; Philbrick, Kenneth A.; Cai, Jason; Taunton, Michael J.; Kremers, Hilal Maradit; Lewallen, David G.; Erickson, Bradley J.

doi:10.1016/j.arth.2021.02.028

Cited by 51 publications

(44 citation statements)

References 28 publications

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“…25 The remaining three studies reported the CNNs’ ability to predict the postoperative risk of implant failure. 22,24,26…”

Section: Resultsmentioning

confidence: 99%

“…Nine studies described automated classification of hip and knee implant brands with good precision (AUC 0.992 to 1) and accuracy (> 90% for most models). 15-21,23,25 The remaining three studies described CNNs trained to detect implant malposition by measuring acetabular angles, 24 predict the risk of dislocation following a THA, 22 and identify prosthetic loosening. 26 CNN measurement of acetabular angle on post-THA radiographs achieved similar accuracy to that of orthopaedic surgeons (mean absolute difference 1.35° (AP) 1.39° (lateral)).…”

Section: Discussionmentioning

confidence: 99%

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Artificial intelligence for image analysis in total hip and total knee arthroplasty

Gurung

Liu

Harris

et al. 2022

The Bone & Joint Journal

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Aims Total hip arthroplasty (THA) and total knee arthroplasty (TKA) are common orthopaedic procedures requiring postoperative radiographs to confirm implant positioning and identify complications. Artificial intelligence (AI)-based image analysis has the potential to automate this postoperative surveillance. The aim of this study was to prepare a scoping review to investigate how AI is being used in the analysis of radiographs following THA and TKA, and how accurate these tools are. Methods The Embase, MEDLINE, and PubMed libraries were systematically searched to identify relevant articles. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews and Arksey and O’Malley framework were followed. Study quality was assessed using a modified Methodological Index for Non-Randomized Studies tool. AI performance was reported using either the area under the curve (AUC) or accuracy. Results Of the 455 studies identified, only 12 were suitable for inclusion. Nine reported implant identification and three described predicting risk of implant failure. Of the 12, three studies compared AI performance with orthopaedic surgeons. AI-based implant identification achieved AUC 0.992 to 1, and most algorithms reported an accuracy > 90%, using 550 to 320,000 training radiographs. AI prediction of dislocation risk post-THA, determined after five-year follow-up, was satisfactory (AUC 76.67; 8,500 training radiographs). Diagnosis of hip implant loosening was good (accuracy 88.3%; 420 training radiographs) and measurement of postoperative acetabular angles was comparable to humans (mean absolute difference 1.35° to 1.39°). However, 11 of the 12 studies had several methodological limitations introducing a high risk of bias. None of the studies were externally validated. Conclusion These studies show that AI is promising. While it already has the ability to analyze images with significant precision, there is currently insufficient high-level evidence to support its widespread clinical use. Further research to design robust studies that follow standard reporting guidelines should be encouraged to develop AI models that could be easily translated into real-world conditions. Cite this article: Bone Joint J 2022;104-B(8):929–937.

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“…25 The remaining three studies reported the CNNs’ ability to predict the postoperative risk of implant failure. 22,24,26…”

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Artificial intelligence for image analysis in total hip and total knee arthroplasty

Gurung

Liu

Harris

et al. 2022

The Bone & Joint Journal

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“…Postoperatively, the overall prospective determination of the likelihood of future need for TKA revision has also been demonstrated with high utility in a large cohort of 25,104 patients after the primary procedure [ 7 ], as ha the value in automated postoperative monitoring and outcome assessment [ 13 ], risk prediction [ 8 ], including the likelihood of dislocation after primary THA [ 26 ]. Ultimately, applications already in research or early clinical use have been shown to predict [ 40 , 43 , 44 ] and/or improve patient satisfaction [ 45 ] and overall outcomes [ 19 ].…”

Section: Methodsmentioning

confidence: 99%

Artificial intelligence in orthopedic surgery: evolution, current state and future directions

Kurmis

Ianunzio

2022

Arthroplasty

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Technological advances continue to evolve at a breath-taking pace. Computer-navigation, robot-assistance and three-dimensional digital planning have become commonplace in many parts of the world. With near exponential advances in computer processing capacity, and the advent, progressive understanding and refinement of software algorithms, medicine and orthopaedic surgery have begun to delve into artificial intelligence (AI) systems. While for some, such applications still seem in the realm of science fiction, these technologies are already in selective clinical use and are likely to soon see wider uptake. The purpose of this structured review was to provide an understandable summary to non-academic orthopaedic surgeons, exploring key definitions and basic development principles of AI technology as it currently stands. To ensure content validity and representativeness, a structured, systematic review was performed following the accepted PRISMA principles. The paper concludes with a forward-look into heralded and potential applications of AI technology in orthopedic surgery.While not intended to be a detailed technical description of the complex processing that underpins AI applications, this work will take a small step forward in demystifying some of the commonly-held misconceptions regarding AI and its potential benefits to patients and surgeons. With evidence-supported broader awareness, we aim to foster an open-mindedness among clinicians toward such technologies in the future.

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“…Our results are in line with Vena ¨la ¨inen et al, since even though the discrimination in both of our models was higher compared with the AUC in their study, the overall predictive ability in our models was low. Rouzrokh et al predicted dislocation after primary THA based on the cup position measured from 92 584 postoperative radiographs from 13 970 operations using convolutional neural networks [32]. Their model reached 89% sensitivity, 49% specificity, and an AUC of 0.77.…”

Section: Plos Onementioning

confidence: 99%

Prediction model for an early revision for dislocation after primary total hip arthroplasty

et al. 2022

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Dislocation is one of the most common complications after primary total hip arthroplasty (THA). Several patient-related risk factors for dislocation have been reported in the previous literature, but only few prediction models for dislocation have been made. Our aim was to build a prediction model for an early (within the first 2 years) revision for dislocation after primary THA using two different statistical methods. The study data constituted of 37 pre- or perioperative variables and postoperative follow-up data of 16 454 primary THAs performed at our institution in 2008–2021. Model I was a traditional logistic regression model and Model II was based on the elastic net method that utilizes machine learning. The models’ overall performance was measured using the pseudo R2 values. The discrimination of the models was measured using C-index in Model I and Area Under the Curve (AUC) in Model II. Calibration curves were made for both models. At 2 years postoperatively, 95 hips (0.6% prevalence) had been revised for dislocation. The pseudo R2 values were 0.04 in Model I and 0.02 in Model II indicating low predictive capability in both models. The C-index in Model I was 0.67 and the AUC in Model II was 0.73 indicating modest discrimination. The prediction of an early revision for dislocation after primary THA is difficult even in a large cohort of patients with detailed data available because of the reasonably low prevalence and multifactorial nature of dislocation. Therefore, the risk of dislocation should be kept in mind in every primary THA, whether the patient has predisposing factors for dislocation or not. Further, when conducting a prediction model, sophisticated methods that utilize machine learning may not necessarily offer significant advantage over traditional statistical methods in clinical setup.

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Deep Learning Artificial Intelligence Model for Assessment of Hip Dislocation Risk Following Primary Total Hip Arthroplasty From Postoperative Radiographs

Cited by 51 publications

References 28 publications

Artificial intelligence for image analysis in total hip and total knee arthroplasty

Artificial intelligence for image analysis in total hip and total knee arthroplasty

Artificial intelligence in orthopedic surgery: evolution, current state and future directions

Prediction model for an early revision for dislocation after primary total hip arthroplasty

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