Artificial intelligence and machine learning: an introduction for orthopaedic surgeons

Martin, R. Kyle; Ley, Christophe; Pareek, Ayoosh; Groll, Andreas; Tischer, Thomas; Seil, Romain

doi:10.1007/s00167-021-06741-2

Cited by 47 publications

(36 citation statements)

References 23 publications

(27 reference statements)

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“…Furthermore, in an effort to comprehensively analyze the success of conservative treatment modalities—primarily tendinopathies—the development of non-surgical treatment modules is currently in progress. Lastly, artificial intelligence and machine learning, as they already have been proven to be valuable tools in orthopaedic research for several different pathologies, are planned to be utilized in future DART-related research [ 3 , 15 , 19 , 21 , 24 , 25 ].…”

Section: Discussionmentioning

confidence: 99%

The German Arthroscopy Registry DART: what has happened after 5 years?

Hinz

Lutter

Mueller-Rath

et al. 2022

Knee Surg Sports Traumatol Arthrosc

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Purpose The German Arthroscopy Registry (DART) has been initiated in 2017 with the aim to collect real-life data of patients undergoing knee, shoulder, hip or ankle surgery. The purpose of this study was to present an overview of the current status and the collected data thus far. Methods Data entered between 11/2017 and 01/2022 were analyzed. The number of cases (each case is defined as a single operation with or without concomitant procedures) entered for each joint, follow-up rates and trends between different age groups (18–29 years, 30–44 years, 45–64 years, ≥ 65 years) and across genders, and quality of life improvement (pre- vs. 1 year postoperative EQ visual analogue scale [EQ-VAS]) for frequently performed procedures (medial meniscus repair [MMR] vs. rotator cuff repair [RCR] vs. microfracturing of the talus [MFX-T]) were investigated. Results Overall, 6651 cases were entered into DART, forming three distinct modules classified by joint (5370 knee, 1053 shoulder and 228 ankle cases). The most commonly entered procedures were: knee: partial medial meniscectomy (n = 2089), chondroplasty (n = 1389), anterior cruicate ligament reconstruction with hamstring autograft (n = 880); shoulder: sub acromial decompression (n = 631), bursectomy (n = 385), RCR (n = 359); ankle: partial synovectomy (n = 117), tibial osteophyte resection (n = 72), loose body removal (n = 48). In the knee and shoulder modules, middle-aged patients were the predominant age group, whereas in the ankle module, the youngest age group was the most frequent one. The two oldest age groups had the highest 1-year follow-up rates across all modules. In the knee and shoulder module, 1-year follow-up rates were higher in female patients, whereas follow-up rates were higher in male patients in the ankle module. From pre- to 1-year postoperative, MFX-T (EQ-VAS: 50.0 [25–75% interquartile range: 31.8–71.5] to 75.0 [54.3–84.3]; ∆ + 25.0) led to a comparably larger improvement in quality of life than did MMR (EQ-VAS: 70.0 [50.0–80.0] to 85.0 [70.0–94.0]; ∆ + 15.0) or RCR (EQ-VAS: 67.0 [50.0–80.0] to 85.0 [70.0–95.0]; ∆ + 18.0). Conclusion DART has been sufficiently established and collects high-quality patient-related data with satisfactory follow-up allowing for a comprehensive analysis of the collected data. The current focus lies on improving patient enrolment and follow-up rates as well as initiating the hip module.

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Section: Discussionmentioning

confidence: 99%

The German Arthroscopy Registry DART: what has happened after 5 years?

Hinz

Lutter

Mueller-Rath

et al. 2022

Knee Surg Sports Traumatol Arthrosc

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“…The term 'artificial intelligence' (AI) was first introduced in 1956 by John Mccarthy. 8,9 Machine learning (ML) is a subset of ai, 10 and deep learning (DL), the process used in image analysis, is a subset of ML. Through the use of convolutional neural networks (CNNs), DL algorithms are able to simultaneously accept multiple datapoints and evaluate them.…”

Section: Introductionmentioning

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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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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“…Machine Learning (ML) is a subset of artificial intelligence related to “advanced statistical techniques that use computer algorithms to model complex relationships between variables”, with these computer algorithms learning automatically from experience , i.e. data, without direct human intervention [ 20 ]. These algorithms rely on data analysis models to uncover hidden patterns and other meaningful insights from large datasets [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Pre-injury performance is most important for predicting the level of match participation after Achilles tendon ruptures in elite soccer players: a study using a machine learning classifier

Diniz

Abreu

Lacerda

et al. 2022

Knee Surg Sports Traumatol Arthrosc

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Purpose Achilles tendon ruptures (ATR) are career-threatening injuries in elite soccer players due to the decreased sports performance they commonly inflict. This study presents an exploratory data analysis of match participation before and after ATRs and an evaluation of the performance of a machine learning (ML) model based on pre-injury features to predict whether a player will return to a previous level of match participation. Methods The website transfermarkt.com was mined, between January and March of 2021, for relevant entries regarding soccer players who suffered an ATR while playing in first or second leagues. The difference between average minutes played per match (MPM) 1 year before injury and between 1 and 2 years after the injury was used to identify patterns in match participation after injury. Clustering analysis was performed using k -means clustering. Predictions of post-injury match participation were made using the XGBoost classification algorithm. The performance of this model was evaluated using the area under the receiver operating characteristic curve (AUROC) and Brier score loss (BSL). Results Two hundred and nine players were included in the study. Data from 32,853 matches was analysed. Exploratory data analysis revealed that forwards, midfielders and defenders increased match participation during the first year after injury, with goalkeepers still improving at 2 years. Players were grouped into four clusters regarding the difference between MPMs 1 year before injury and between 1 and 2 years after the injury. These groups ranged between a severe decrease ( n = 34; − 59 ± 13 MPM), moderate decrease ( n = 75; − 25 ± 8 MPM), maintenance ( n = 70; 0 ± 8 MPM), or increase ( n = 30; 32 ± 13 MPM). Regarding the predictive model, the average AUROC after cross-validation was 0.81 ± 0.10, and the BSL was 0.12, with the most important features relating to pre-injury match participation. Conclusion Most players take 1 year to reach peak match participation after an ATR. Good performance was attained using a ML classifier to predict the level of match participation following an ATR, with features related to pre-injury match participation displaying the highest importance. Level of evidence I.

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Artificial intelligence and machine learning: an introduction for orthopaedic surgeons

Cited by 47 publications

References 23 publications

The German Arthroscopy Registry DART: what has happened after 5 years?

The German Arthroscopy Registry DART: what has happened after 5 years?

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

Pre-injury performance is most important for predicting the level of match participation after Achilles tendon ruptures in elite soccer players: a study using a machine learning classifier

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