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Lan Q, Li S, Zhang J, Guo H, Yan L, Tang F. Reliable prediction of implant size and axial alignment in AI-based 3D preoperative planning for total knee arthroplasty. Sci Rep 2024; 14:16971. [PMID: 39043748 PMCID: PMC11266554 DOI: 10.1038/s41598-024-67276-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 07/09/2024] [Indexed: 07/25/2024] Open
Abstract
The size and axial alignment of prostheses, when planned during total knee replacement (TKA) are critical for recovery of knee function and improvement of knee pain symptoms. This research aims to study the effect of artificial intelligence (AI)-based preoperative three dimensional (3D) planning technology on prosthesis size and axial alignment planning in TKA, and to compare its advantages with two dimensional (2D) X-ray template measurement technology. A total of 60 patients with knee osteoarthritis (KOA) who underwent TKA for the first time were included in the AI (n = 30) and 2D (n = 30) groups. The preoperative and postoperative prosthesis size, femoral valgus correction angle (VCA) and hip-knee-ankle angle (HKA) were recorded and compared between the two groups. The results of the University of Western Ontario and McMaster University Osteoarthritis Index (WOMAC) and the American Knee Association Score (AKS) were evaluated before surgery, 3 months, 6 months, and 12 months after surgery. The accuracy of prosthesis size, VCA and HKA prediction in AI group was significantly higher than that in 2D group (P < 0.05). The WOMAC and AKS scores in AI group at 3 months, 6 months and 12 months after surgery were better than those in 2D group (P < 0.05). Both groups showed significant improvement in WOMAC and AKS scores at 12 months follow-up. AI-based preoperative 3D planning technique has more reliable planning effect for prosthesis size and axial alignment in TKA.
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Affiliation(s)
- Qing Lan
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
- Department of Orthopedics, Fujian Provincial Hospital, Fuzhou, China
- Fuzhou University Affiliated Provincial Hospital, Fuzhou, China
| | - Shulin Li
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
- Department of Orthopedics, Fujian Provincial Hospital, Fuzhou, China
- Fuzhou University Affiliated Provincial Hospital, Fuzhou, China
| | - Jiahao Zhang
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
| | - Huiling Guo
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
- Department of Orthopedics, Fujian Provincial Hospital, Fuzhou, China
- Fuzhou University Affiliated Provincial Hospital, Fuzhou, China
| | - Laipeng Yan
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
- Department of Orthopedics, Fujian Provincial Hospital, Fuzhou, China
- Fuzhou University Affiliated Provincial Hospital, Fuzhou, China
| | - Faqiang Tang
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China.
- Department of Orthopedics, Fujian Provincial Hospital, Fuzhou, China.
- Fuzhou University Affiliated Provincial Hospital, Fuzhou, China.
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Longo UG, De Salvatore S, Valente F, Villa Corta M, Violante B, Samuelsson K. Artificial intelligence in total and unicompartmental knee arthroplasty. BMC Musculoskelet Disord 2024; 25:571. [PMID: 39034416 PMCID: PMC11265144 DOI: 10.1186/s12891-024-07516-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 05/13/2024] [Indexed: 07/23/2024] Open
Abstract
The application of Artificial intelligence (AI) and machine learning (ML) tools in total (TKA) and unicompartmental knee arthroplasty (UKA) emerges with the potential to improve patient-centered decision-making and outcome prediction in orthopedics, as ML algorithms can generate patient-specific risk models. This review aims to evaluate the potential of the application of AI/ML models in the prediction of TKA outcomes and the identification of populations at risk.An extensive search in the following databases: MEDLINE, Scopus, Cinahl, Google Scholar, and EMBASE was conducted using the PIOS approach to formulate the research question. The PRISMA guideline was used for reporting the evidence of the data extracted. A modified eight-item MINORS checklist was employed for the quality assessment. The databases were screened from the inception to June 2022.Forty-four out of the 542 initially selected articles were eligible for the data analysis; 5 further articles were identified and added to the review from the PUBMED database, for a total of 49 articles included. A total of 2,595,780 patients were identified, with an overall average age of the patients of 70.2 years ± 7.9 years old. The five most common AI/ML models identified in the selected articles were: RF, in 38.77% of studies; GBM, in 36.73% of studies; ANN in 34.7% of articles; LR, in 32.65%; SVM in 26.53% of articles.This systematic review evaluated the possible uses of AI/ML models in TKA, highlighting their potential to lead to more accurate predictions, less time-consuming data processing, and improved decision-making, all while minimizing user input bias to provide risk-based patient-specific care.
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Affiliation(s)
- Umile Giuseppe Longo
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, Rome, 200 - 00128, Italy.
- Department of Medicine and Surgery, Research Unit of Orthopaedic and Trauma Surgery, Università Campus Bio-Medico Di Roma, Via Alvaro del Portillo, Rome, 21 - 00128, Italy.
| | - Sergio De Salvatore
- IRCCS Ospedale Pediatrico Bambino Gesù, Rome, Italy
- Orthopedic Unit, Department of Surgery, Bambino Gesù Children's Hospital, Rome, Italy
| | - Federica Valente
- Department of Medicine and Surgery, Research Unit of Orthopaedic and Trauma Surgery, Università Campus Bio-Medico Di Roma, Via Alvaro del Portillo, Rome, 21 - 00128, Italy
| | - Mariajose Villa Corta
- Department of Medicine and Surgery, Research Unit of Orthopaedic and Trauma Surgery, Università Campus Bio-Medico Di Roma, Via Alvaro del Portillo, Rome, 21 - 00128, Italy
| | - Bruno Violante
- Orthopaedic Department, Clinical Institute Sant'Ambrogio, IRCCS - Galeazzi, Milan, Italy
| | - Kristian Samuelsson
- Department of Medicine and Surgery, Research Unit of Orthopaedic and Trauma Surgery, Università Campus Bio-Medico Di Roma, Via Alvaro del Portillo, Rome, 21 - 00128, Italy
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Gutierrez-Naranjo JM, Moreira A, Valero-Moreno E, Bullock TS, Ogden LA, Zelle BA. -A machine learning model to predict surgical site infection after surgery of lower extremity fractures. INTERNATIONAL ORTHOPAEDICS 2024; 48:1887-1896. [PMID: 38700699 DOI: 10.1007/s00264-024-06194-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 04/22/2024] [Indexed: 06/14/2024]
Abstract
PURPOSE This study aimed to develop machine learning algorithms for identifying predictive factors associated with the risk of postoperative surgical site infection in patients with lower extremity fractures. METHODS A machine learning analysis was conducted on a dataset comprising 1,579 patients who underwent surgical fixation for lower extremity fractures to create a predictive model for risk stratification of postoperative surgical site infection. We evaluated different clinical and demographic variables to train four machine learning models (neural networks, boosted generalised linear model, naïve bayes, and penalised discriminant analysis). Performance was measured by the area under the curve score, Youdon's index and Brier score. A multivariate adaptive regression splines (MARS) was used to optimise predictor selection. RESULTS The final model consisted of five predictors. (1) Operating room time, (2) ankle region, (3) open injury, (4) body mass index, and (5) age. The best-performing machine learning algorithm demonstrated a promising predictive performance, with an area under the ROC curve, Youdon's index, and Brier score of 77.8%, 62.5%, and 5.1%-5.6%, respectively. CONCLUSION The proposed predictive model not only assists surgeons in determining high-risk factors for surgical site infections but also empowers patients to closely monitor these factors and take proactive measures to prevent complications. Furthermore, by considering the identified predictors, this model can serve as a reference for implementing preventive measures and reducing postoperative complications, ultimately enhancing patient outcomes. However, further investigations involving larger datasets and external validations are required to confirm the reliability and applicability of our model.
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Affiliation(s)
| | - Alvaro Moreira
- Department of Pediatrics, UT Health San Antonio, San Antonio, TX, USA.
| | | | - Travis S Bullock
- Department of Orthopaedics, UT Health San Antonio, San Antonio, TX, 78229-3900, USA
| | - Liliana A Ogden
- Department of Orthopaedics, UT Health San Antonio, San Antonio, TX, 78229-3900, USA
| | - Boris A Zelle
- Department of Orthopaedics, UT Health San Antonio, San Antonio, TX, 78229-3900, USA.
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Corsi MP, Nham FH, Kassis E, El-Othmani MM. Bibliometric analysis of machine learning trends and hotspots in arthroplasty literature over 31 years. J Orthop 2024; 51:142-156. [PMID: 38405126 PMCID: PMC10891287 DOI: 10.1016/j.jor.2024.01.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Revised: 01/27/2024] [Accepted: 01/29/2024] [Indexed: 02/27/2024] Open
Abstract
Background Artificial intelligence has demonstrated utility in orthopedic research. Algorithmic models derived from machine learning have demonstrated adaptive learning with predictive application towards outcomes, leading to increased traction in the literature. This study aims to identify machine learning arthroplasty research trends and anticipate emerging key terms. Methods Published literature focused on machine learning in arthroplasty from 1992 to 2023 was selected through the Web of Science Core Collection of Clarivate Analytics. Following that, bibliometric indicators were attained and brought in to perform an additional examination using Bibliometrix and VOSviewer to identify historical and present patterns within the literature. Results A total of 235 documents were obtained through bibliometric sourcing based on machine learning applications within the arthroplasty literature. Thirty-four countries published articles on the topic, and the United States was demonstrated to be the largest global contributor. Four hundred-five institutions internationally contributed articles, with Harvard Medical School and the University of California system as the most relevant institutes, with 75 and 44 articles produced, respectively. Kwon YM was the most productive author, while Haeberle HS and Ramkumar PN were the most impactful based on h-index. The Thematic map and Co-occurrence visualization helped identify both major and niche themes present in the scientific databases. Conclusions Machine learning in arthroplasty research continues to gain traction with a growing annual production rate and contributions from international authors and institutions. Institutions and authors based in the United States are the leading contributors to machine learning applications within arthroplasty research. This research discerns trends that have occurred, are presently ongoing, and are emerging within this field, aiming to inform future hotspot development.
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Affiliation(s)
- Matthew P. Corsi
- Wayne State University School of Medicine, 540 E. Canfield St, Detroit, MI, 48201, USA
| | - Fong H. Nham
- Department of Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI, 48201, USA
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Mitterer JA, Huber S, Schwarz GM, Simon S, Pallamar M, Kissler F, Frank BJH, Hofstaetter JG. Fully automated assessment of the knee alignment on long leg radiographs following corrective knee osteotomies in patients with valgus or varus deformities. Arch Orthop Trauma Surg 2024; 144:1029-1038. [PMID: 38091069 DOI: 10.1007/s00402-023-05151-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 11/20/2023] [Indexed: 02/28/2024]
Abstract
INTRODUCTION The assessment of the knee alignment on long leg radiographs (LLR) postoperative to corrective knee osteotomies (CKOs) is highly dependent on the reader's expertise. Artificial Intelligence (AI) algorithms may help automate and standardise this process. The study aimed to analyse the reliability of an AI-algorithm for the evaluation of LLRs following CKOs. MATERIALS AND METHODS In this study, we analysed a validation cohort of 110 postoperative LLRs from 102 patients. All patients underwent CKO, including distal femoral (DFO), high tibial (HTO) and bilevel osteotomies. The agreement between manual measurements and the AI-algorithm was assessed for the mechanical axis deviation (MAD), hip knee ankle angle (HKA), anatomical-mechanical-axis-angle (AMA), joint line convergence angle (JLCA), mechanical lateral proximal femur angle (mLPFA), mechanical lateral distal femoral angle (mLDFA), mechanical medial proximal tibia angle (mMPTA) and mechanical lateral distal tibia angle (mLDTA), using the intra-class-correlation (ICC) coefficient between the readers, each reader and the AI and the mean of the manual reads and the AI-algorithm and Bland-Altman Plots between the manual reads and the AI software for the MAD, HKA, mLDFA and mMPTA. RESULTS In the validation cohort, the AI software showed excellent agreement with the manual reads (ICC: 0.81-0.99). The agreement between the readers (Inter-rater) showed excellent correlations (ICC: 0.95-0. The mean difference in the DFO group for the MAD, HKA, mLDFA and mMPTA were 0.50 mm, - 0.12°, 0.55° and 0.15°. In the HTO group the mean difference for the MAD, HKA, mLDFA and mMPTA were 0.36 mm, - 0.17°, 0.57° and 0.08°, respectively. Reliable outputs were generated in 95.4% of the validation cohort. CONCLUSION he application of AI-algorithms for the assessment of lower limb alignment on LLRs following CKOs shows reliable and accurate results. LEVEL OF EVIDENCE Diagnostic Level III.
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Affiliation(s)
- Jennyfer A Mitterer
- Michael Ogon Laboratory for Orthopaedic Research, Orthopaedic Hospital Vienna Speising, Speisinger Straße 109, 1130, Vienna, Austria
| | - Stephanie Huber
- Michael Ogon Laboratory for Orthopaedic Research, Orthopaedic Hospital Vienna Speising, Speisinger Straße 109, 1130, Vienna, Austria
- Center for Anatomy and Cell Biology, Medical University Vienna Speising, Währinger Straße 13, 1090, Vienna, Austria
| | - Gilbert M Schwarz
- Michael Ogon Laboratory for Orthopaedic Research, Orthopaedic Hospital Vienna Speising, Speisinger Straße 109, 1130, Vienna, Austria
- Center for Anatomy and Cell Biology, Medical University Vienna Speising, Währinger Straße 13, 1090, Vienna, Austria
- Department of Orthopaedic and Trauma-Surgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Sebastian Simon
- Michael Ogon Laboratory for Orthopaedic Research, Orthopaedic Hospital Vienna Speising, Speisinger Straße 109, 1130, Vienna, Austria
- 2nd Department, Orthopaedic Hospital Vienna Speising, Speisinger Straße 109, 1130, Vienna, Austria
| | - Matthias Pallamar
- Department of Pediatric Orthopaedics, Orthopaedic Hospital Vienna Speising, Speisinger Straße 109, 1130, Vienna, Austria
| | - Florian Kissler
- 1st Department, Orthopaedic Hospital Vienna Speising, Speisinger Straße 109, 1130, Vienna, Austria
| | - Bernhard J H Frank
- Michael Ogon Laboratory for Orthopaedic Research, Orthopaedic Hospital Vienna Speising, Speisinger Straße 109, 1130, Vienna, Austria
- 2nd Department, Orthopaedic Hospital Vienna Speising, Speisinger Straße 109, 1130, Vienna, Austria
| | - Jochen G Hofstaetter
- Michael Ogon Laboratory for Orthopaedic Research, Orthopaedic Hospital Vienna Speising, Speisinger Straße 109, 1130, Vienna, Austria.
- 2nd Department, Orthopaedic Hospital Vienna Speising, Speisinger Straße 109, 1130, Vienna, Austria.
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Salman LA, Khatkar H, Al-Ani A, Alzobi OZ, Abudalou A, Hatnouly AT, Ahmed G, Hameed S, AlAteeq Aldosari M. Reliability of artificial intelligence in predicting total knee arthroplasty component sizes: a systematic review. EUROPEAN JOURNAL OF ORTHOPAEDIC SURGERY & TRAUMATOLOGY : ORTHOPEDIE TRAUMATOLOGIE 2024; 34:747-756. [PMID: 38010443 PMCID: PMC10858112 DOI: 10.1007/s00590-023-03784-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 11/01/2023] [Indexed: 11/29/2023]
Abstract
PURPOSE This systematic review aimed to investigate the reliability of AI predictive models of intraoperative implant sizing in total knee arthroplasty (TKA). METHODS Four databases were searched from inception till July 2023 for original studies that studied the reliability of AI prediction in TKA. The primary outcome was the accuracy ± 1 size. This review was conducted per PRISMA guidelines, and the risk of bias was assessed using the MINORS criteria. RESULTS A total of four observational studies comprised of at least 34,547 patients were included in this review. A mean MINORS score of 11 out of 16 was assigned to the review. All included studies were published between 2021 and 2022, with a total of nine different AI algorithms reported. Among these AI models, the accuracy of TKA femoral component sizing prediction ranged from 88.3 to 99.7% within a deviation of one size, while tibial component sizing exhibited an accuracy ranging from 90 to 99.9% ± 1 size. CONCLUSION This study demonstrated the potential of AI as a valuable complement for planning TKA, exhibiting a satisfactory level of reliability in predicting TKA implant sizes. This predictive accuracy is comparable to that of the manual and digital templating techniques currently documented in the literature. However, future research is imperative to assess the impact of AI on patient care and cost-effectiveness. LEVEL OF EVIDENCE III PROSPERO registration number: CRD42023446868.
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Affiliation(s)
- Loay A Salman
- Department of Orthopaedic Surgery, Surgical Specialty Center, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar.
| | | | - Abdallah Al-Ani
- Office of Scientific Affairs and Research, King Hussein Cancer Center, Amman, Jordan
| | - Osama Z Alzobi
- Department of Orthopaedic Surgery, Surgical Specialty Center, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Abedallah Abudalou
- Department of Orthopaedic Surgery, Surgical Specialty Center, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Ashraf T Hatnouly
- Department of Orthopaedic Surgery, Surgical Specialty Center, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Ghalib Ahmed
- Department of Orthopaedic Surgery, Surgical Specialty Center, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Shamsi Hameed
- Department of Orthopaedic Surgery, Surgical Specialty Center, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Mohamed AlAteeq Aldosari
- Department of Orthopaedic Surgery, Surgical Specialty Center, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
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Chan VWK, Chan PK, Fu H, Cheung MH, Cheung A, Tang TCM, Chiu KY. Prediction of Total Knee Arthroplasty Sizes with Demographics, including Hand and Foot Sizes. J Knee Surg 2023. [PMID: 37879355 DOI: 10.1055/a-2198-7983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/27/2023]
Abstract
Anticipating implant sizes before total knee arthroplasty (TKA) allows the surgical team to streamline operations and prepare for potential difficulties. This study aims to determine the correlation and derive a regression model for predicting TKA sizes using patient-specific demographics without using radiographs. We reviewed the demographics, including hand and foot sizes, of 1,339 primary TKAs. To allow for comparison across different TKA designs, we converted the femur and tibia sizes into their anteroposterior (AP) and mediolateral (ML) dimensions. Stepwise multivariate regressions were performed to analyze the data. Regarding the femur component, the patient's foot, gender, height, hand circumference, body mass index, and age was the significant demographic factors in the regression analysis (R-square 0.541, p < 0.05). For the tibia component, the significant factors in the regression analysis were the patient's foot size, gender, height, hand circumference, and age (R-square 0.608, p < 0.05). The patient's foot size had the highest correlation coefficient for both femur (0.670) and tibia (0.697) implant sizes (p < 0.05). We accurately predicted the femur component size exactly, within one and two sizes in 49.5, 94.2, and 99.9% of cases, respectively. Regarding the tibia, the prediction was exact, within one and two sizes in 53.0, 96.0, and 100% of cases, respectively. The regression model, utilizing patient-specific characteristics, such as foot size and hand circumference, accurately predicted TKA femur and tibia sizes within one component size. This provides a more efficient alternative for preoperative planning.
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Affiliation(s)
- Vincent W K Chan
- Department of Orthopaedics and Traumatology, Division of Joint Replacement Surgery, The University of Hong Kong, Queen Mary Hospital, Hong Kong SAR, China
| | - Ping Keung Chan
- Department of Orthopaedics and Traumatology, Division of Joint Replacement Surgery, The University of Hong Kong, Queen Mary Hospital, Hong Kong SAR, China
| | - Henry Fu
- Department of Orthopaedics and Traumatology, Division of Joint Replacement Surgery, The University of Hong Kong, Queen Mary Hospital, Hong Kong SAR, China
| | - Man Hong Cheung
- Department of Orthopaedics and Traumatology, Division of Joint Replacement Surgery, The University of Hong Kong, Queen Mary Hospital, Hong Kong SAR, China
| | - Amy Cheung
- Department of Orthopaedics and Traumatology, Division of Joint Replacement Surgery, The University of Hong Kong, Queen Mary Hospital, Hong Kong SAR, China
| | - Thomas C M Tang
- Department of Orthopaedics and Traumatology, Division of Joint Replacement Surgery, The University of Hong Kong, Queen Mary Hospital, Hong Kong SAR, China
| | - Kwong Yuen Chiu
- Department of Orthopaedics and Traumatology, Division of Joint Replacement Surgery, The University of Hong Kong, Queen Mary Hospital, Hong Kong SAR, China
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Kim MS, Kim JJ, Kang KH, Lee JH, In Y. Detection of Prosthetic Loosening in Hip and Knee Arthroplasty Using Machine Learning: A Systematic Review and Meta-Analysis. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:medicina59040782. [PMID: 37109740 PMCID: PMC10141023 DOI: 10.3390/medicina59040782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Revised: 04/02/2023] [Accepted: 04/11/2023] [Indexed: 04/29/2023]
Abstract
Background: prosthetic loosening after hip and knee arthroplasty is one of the most common causes of joint arthroplasty failure and revision surgery. Diagnosis of prosthetic loosening is a difficult problem and, in many cases, loosening is not clearly diagnosed until accurately confirmed during surgery. The purpose of this study is to conduct a systematic review and meta-analysis to demonstrate the analysis and performance of machine learning in diagnosing prosthetic loosening after total hip arthroplasty (THA) and total knee arthroplasty (TKA). Materials and Methods: three comprehensive databases, including MEDLINE, EMBASE, and the Cochrane Library, were searched for studies that evaluated the detection accuracy of loosening around arthroplasty implants using machine learning. Data extraction, risk of bias assessment, and meta-analysis were performed. Results: five studies were included in the meta-analysis. All studies were retrospective studies. In total, data from 2013 patients with 3236 images were assessed; these data involved 2442 cases (75.5%) with THAs and 794 cases (24.5%) with TKAs. The most common and best-performing machine learning algorithm was DenseNet. In one study, a novel stacking approach using a random forest showed similar performance to DenseNet. The pooled sensitivity across studies was 0.92 (95% CI 0.84-0.97), the pooled specificity was 0.95 (95% CI 0.93-0.96), and the pooled diagnostic odds ratio was 194.09 (95% CI 61.60-611.57). The I2 statistics for sensitivity and specificity were 96% and 62%, respectively, showing that there was significant heterogeneity. The summary receiver operating characteristics curve indicated the sensitivity and specificity, as did the prediction regions, with an AUC of 0.9853. Conclusions: the performance of machine learning using plain radiography showed promising results with good accuracy, sensitivity, and specificity in the detection of loosening around THAs and TKAs. Machine learning can be incorporated into prosthetic loosening screening programs.
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Affiliation(s)
- Man-Soo Kim
- Department of Orthopaedic Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| | - Jae-Jung Kim
- Department of Orthopaedic Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| | - Ki-Ho Kang
- Department of Orthopaedic Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| | - Jeong-Han Lee
- Department of Orthopaedic Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| | - Yong In
- Department of Orthopaedic Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
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Kunze KN, Karhade AV, Polce EM, Schwab JH, Levine BR. Development and internal validation of machine learning algorithms for predicting complications after primary total hip arthroplasty. Arch Orthop Trauma Surg 2023; 143:2181-2188. [PMID: 35508549 DOI: 10.1007/s00402-022-04452-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 04/15/2022] [Indexed: 12/23/2022]
Abstract
INTRODUCTION Complications after total hip arthroplasty (THA) may result in readmission or reoperation and impose a significant cost on the healthcare system. Understanding which patients are at-risk for complications can potentially allow for targeted interventions to decrease complication rates through pursuing preoperative health optimization. The purpose of the current was to develop and internally validate machine learning (ML) algorithms capable of performing patient-specific predictions of all-cause complications within two years of primary THA. METHODS This was a retrospective case-control study of clinical registry data from 616 primary THA patients from one large academic and two community hospitals. The primary outcome was all-cause complications at a minimum of 2-years after primary THA. Recursive feature elimination was applied to identify preoperative variables with the greatest predictive value. Five ML algorithms were developed on the training set using tenfold cross-validation and internally validated on the independent testing set of patients. Algorithms were assessed by discrimination, calibration, Brier score, and decision curve analysis to quantify performance. RESULTS The observed complication rate was 16.6%. The stochastic gradient boosting model achieved the best performance with an AUC = 0.88, calibration intercept = 0.1, calibration slope = 1.22, and Brier score = 0.09. The most important factors for predicting complications were age, drug allergies, prior hip surgery, smoking, and opioid use. Individual patient-level explanations were provided for the algorithm predictions and incorporated into an open access digital application: https://sorg-apps.shinyapps.io/tha_complication/ CONCLUSIONS: The stochastic boosting gradient algorithm demonstrated good discriminatory capacity for identifying patients at high-risk of experiencing a postoperative complication and proof-of-concept for creating office-based applications from ML that can perform real-time prediction. However, this clinical utility of the current algorithm is unknown and definitions of complications broad. Further investigation on larger data sets and rigorous external validation is necessary prior to the assessment of clinical utility with respect to risk-stratification of patients undergoing primary THA. LEVEL OF EVIDENCE III, therapeutic study.
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Affiliation(s)
- Kyle N Kunze
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA.
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA.
| | - Aditya V Karhade
- Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Evan M Polce
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Joseph H Schwab
- Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Brett R Levine
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
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Polisetty TS, Jain S, Pang M, Karnuta JM, Vigdorchik JM, Nawabi DH, Wyles CC, Ramkumar PN. Concerns surrounding application of artificial intelligence in hip and knee arthroplasty. Bone Joint J 2022; 104-B:1292-1303. [DOI: 10.1302/0301-620x.104b12.bjj-2022-0922.r1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular (“AI/machine learning”), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered. Cite this article: Bone Joint J 2022;104-B(12):1292–1303.
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Affiliation(s)
- Teja S. Polisetty
- Department of Orthopaedic Surgery, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Samagra Jain
- Department of Orthopaedic Surgery, Baylor College of Medicine, Houston, Texas, USA
| | - Michael Pang
- Department of Orthopaedic Surgery, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Jaret M. Karnuta
- Department of Orthopaedic Surgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | - Danyal H. Nawabi
- Sports Medicine Institute, Hospital for Special Surgery, New York, New York, USA
| | - Cody C. Wyles
- Department of Orthopaedic Surgery, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
| | - Prem N. Ramkumar
- Department of Orthopaedic Surgery, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Sports Medicine Institute, Hospital for Special Surgery, New York, New York, USA
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Rodríguez-Merchán EC. The current role of the virtual elements of artificial intelligence in total knee arthroplasty. EFORT Open Rev 2022; 7:491-497. [PMID: 35900206 PMCID: PMC9297054 DOI: 10.1530/eor-21-0107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
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.
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Affiliation(s)
- E Carlos Rodríguez-Merchán
- Department of Orthopaedic Surgery, La Paz University Hospital, Madrid, Spain
- Osteoarticular Surgery Research, Hospital La Paz Institute for Health Research – IdiPAZ (La Paz University Hospital – Autonomous University of Madrid), Madrid, Spain
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Batailler C, Shatrov J, Sappey-Marinier E, Servien E, Parratte S, Lustig S. Artificial intelligence in knee arthroplasty: current concept of the available clinical applications. ARTHROPLASTY 2022; 4:17. [PMID: 35491420 PMCID: PMC9059406 DOI: 10.1186/s42836-022-00119-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Accepted: 02/24/2022] [Indexed: 11/30/2022] Open
Abstract
Background Artificial intelligence (AI) is defined as the study of algorithms that allow machines to reason and perform cognitive functions such as problem-solving, objects, images, word recognition, and decision-making. This study aimed to review the published articles and the comprehensive clinical relevance of AI-based tools used before, during, and after knee arthroplasty. Methods The search was conducted through PubMed, EMBASE, and MEDLINE databases from 2000 to 2021 using the 2009 Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol (PRISMA). Results A total of 731 potential articles were reviewed, and 132 were included based on the inclusion criteria and exclusion criteria. Some steps of the knee arthroplasty procedure were assisted and improved by using AI-based tools. Before surgery, machine learning was used to aid surgeons in optimizing decision-making. During surgery, the robotic-assisted systems improved the accuracy of knee alignment, implant positioning, and ligamentous balance. After surgery, remote patient monitoring platforms helped to capture patients’ functional data. Conclusion In knee arthroplasty, the AI-based tools improve the decision-making process, surgical planning, accuracy, and repeatability of surgical procedures.
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