Machine learning–based identification of hip arthroplasty designs

Kang, Yang-Jae; Yoo, Jun‐Il; Cha, Yonghan; Park, Chan H.; Kim, Jung-Taek

doi:10.1016/j.jot.2019.11.004

Cited by 40 publications

(46 citation statements)

References 11 publications

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“…Kang et al also developed a CNN model trained on 170 plain hip X-rays containing 29 different implant designs. This model also showed a high level of performance, with an AUC of 0.99 [35]. In summary, as in the case of using deep learning for fractures, binary classification of osteoarthritis has a higher accuracy than multiclass classification.…”

Section: Deep Learning For Osteoarthritis and Prediction Of Arthroplasty Implantsmentioning

confidence: 77%

Deep Learning for Orthopedic Disease Based on Medical Image Analysis: Present and Future

Lee

Chung

2022

Applied Sciences

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Since its development, deep learning has been quickly incorporated into the field of medicine and has had a profound impact. Since 2017, many studies applying deep learning-based diagnostics in the field of orthopedics have demonstrated outstanding performance. However, most published papers have focused on disease detection or classification, leaving some unsatisfactory reports in areas such as segmentation and prediction. This review introduces research published in the field of orthopedics classified according to disease from the perspective of orthopedic surgeons, and areas of future research are discussed. This paper provides orthopedic surgeons with an overall understanding of artificial intelligence-based image analysis and the information that medical data should be treated with low prejudice, providing developers and researchers with insight into the real-world context in which clinicians are embracing medical artificial intelligence.

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Section: Deep Learning For Osteoarthritis and Prediction Of Arthroplasty Implantsmentioning

confidence: 77%

Deep Learning for Orthopedic Disease Based on Medical Image Analysis: Present and Future

Lee

Chung

2022

Applied Sciences

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“…Sixteen studies (32.7%) evaluated AI/ML applications to accurately predict patient reoperations, operating time, hospital LOS, discharges, readmissions, or surgical and inpatient costs [ [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] ]. In addition, 16 studies (32.7%) used patients’ preoperative risk factors and other patient-specific variables to optimize the patient selection and surgical planning process through the use of AI/ML-based predictions of surgical outcomes and postoperative complications [ [30] , [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] ]. The majority of the decision support studies evaluated AI/ML model performance using receiver operating characteristic/AUC, accuracy, sensitivity, and specificity.…”

Section: Resultsmentioning

confidence: 99%

“…Hip and knee arthroplasty typically involve an older and highly comorbid patient population, and these tools can be especially helpful in identifying patient-specific needs and risks within this population. Examples of how these models can enable providers to create and optimize personalized treatment plans include accurate identification of an implant from a previous surgery for revision procedures and classifying total knee arthroplasty (TKA) surgical candidates based on patient-specific risk factors [29][30][31][33][34][35][37][38][39][40][41][42]44,62]. Hyer et al demonstrated an AI/ML model which classified TKA and total hip arthroplasty patients based on surgical complexity scores [19].…”

Section: Discussionmentioning

confidence: 99%

Artificial Learning and Machine Learning Decision Guidance Applications in Total Hip and Knee Arthroplasty: A Systematic Review

et al. 2021

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Background: Artificial intelligence (AI) and machine learning (ML) modeling in hip and knee arthroplasty (total joint arthroplasty [TJA]) is becoming more commonplace. This systematic review aims to quantify the accuracy of current AI-and ML-based application for cognitive support and decision-making in TJA. Methods: A comprehensive search of publications was conducted through the EMBASE, Medline, and PubMed databases using relevant keywords to maximize the sensitivity of the search. No limits were placed on level of evidence or timing of the study. Findings were reported according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Analysis of variance testing with post-hoc Tukey test was applied to compare the area under the curve (AUC) of the models. Results: After application of inclusion and exclusion criteria, 49 studies were included in this review. The application of AI/ML-based models and average AUC is as follows: cost prediction-0.77, LOS and discharges-0.78, readmissions and reoperations-0.66, preoperative patient selection/planning-0.79, adverse events and other postoperative complications-0.84, postoperative pain-0.83, postoperative patient-reported outcomes measures and functional outcome-0.81. Significant variability in model AUC across the different decision support applications was found (P < .001) with the AUC for readmission and reoperation models being significantly lower than that of the other decision support categories. Conclusions: AI/ML-based applications in TJA continue to expand and have the potential to optimize patient selection and accurately predict postoperative outcomes, complications, and associated costs. On average, the AI/ML models performed best in predicting postoperative complications, pain, and patientreported outcomes and were less accurate in predicting hospital readmissions and reoperations.

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“…In hip arthroplasty, deep learning algorithms are also capable of recognizing THA implants based on AP radiograph. Although the classification is based solely on identifying the design characteristics of the femoral stem, studies have found the results to be very accurate, even reaching an accuracy rate of 99.6 to 100% [45,47,49,50].…”

Section: Preoperative Evaluationmentioning

confidence: 99%

Artificial intelligence in arthroplasty

2021

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Artificial intelligence (AI) is altering the world of medicine. Given the rapid advances in technology, computers are now able to learn and improve, imitating humanoid cognitive function. AI applications currently exist in various medical specialties, some of which are already in clinical use. This review presents the potential uses and limitations of AI in arthroplasty to provide a better understanding of the existing technology and future direction of this field.Recent literature demonstrates that the utilization of AI in the field of arthroplasty has the potential to improve patient care through better diagnosis, screening, planning, monitoring, and prediction. The implementation of AI technology will enable arthroplasty surgeons to provide patient-specific management in clinical decision making, preoperative health optimization, resource allocation, decision support, and early intervention. While this technology presents a variety of exciting opportunities, it also has several limitations and challenges that need to be overcome to ensure its safety and effectiveness.

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Machine learning–based identification of hip arthroplasty designs

Cited by 40 publications

References 11 publications

Deep Learning for Orthopedic Disease Based on Medical Image Analysis: Present and Future

Deep Learning for Orthopedic Disease Based on Medical Image Analysis: Present and Future

Artificial Learning and Machine Learning Decision Guidance Applications in Total Hip and Knee Arthroplasty: A Systematic Review

Artificial intelligence in arthroplasty

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