1
|
Chavosh Nejad M, Vestergaard Matthiesen R, Dukovska-Popovska I, Jakobsen T, Johansen J. Machine learning for predicting duration of surgery and length of stay: A literature review on joint arthroplasty. Int J Med Inform 2024; 192:105631. [PMID: 39293161 DOI: 10.1016/j.ijmedinf.2024.105631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 08/15/2024] [Accepted: 09/13/2024] [Indexed: 09/20/2024]
Abstract
INTRODUCTION In recent years, different factors such as population aging have caused escalating demand for hip and knee arthroplasty straining already limited hospitals' resources. To address this challenge, focus is put on medical and operational efficiency improvements. This includes an increased use of machine learning (ML) to predict duration of surgery (DOS) and length of stay (LOS) for total knee and total hip arthroplasty, which can be utilized for optimizing resource allocation to satisfy medical and operational limitations. This paper explores the development and performance of ML models in predicting DOS and LOS. METHODS A systematic search of publications between 2010-2023 was conducted following PRISMA guidelines. Considering the inclusion and exclusion criteria, 28 out of 722 gathered papers from PubMed, Web of Science, and manual search were included in the study. Descriptive statistics was used to analyze the extracted data regarding data preprocessing, model development, and model performance assessment. RESULTS Most of the papers work on LOS as a binary variable. Patient's age was identified as the most frequently used and reported as important variable for predicting DOS and LOS. Investigations also illustrated that within the resulting 28 papers, more than 71% of models reached good to perfect performance based on the area under the receiver operating characteristic curve (AUC), where artificial neural networks and ensemble learning models had the biggest share among the best-performing models. CONCLUSION The utilization of ML models is increasing in the literature. The current performance level indicates that ML can potentially turn to powerful tools in predicting DOS and LOS for different purposes. Meanwhile, the literature is not matured yet in reporting real-life application. Future studies can focus on model specification and validation by considering empirical application.
Collapse
Affiliation(s)
- Mohammad Chavosh Nejad
- Department of Materials and Production, Aalborg University, Fibigerstræde 16, 2-109, Aalborg Ø 9220, Danmark.
| | | | - Iskra Dukovska-Popovska
- Department of Materials and Production, Aalborg University, Fibigerstræde 16, 2-107, Aalborg Ø 9220, Danmark.
| | - Thomas Jakobsen
- Department of Orthopaedics, Aalborg University Hospital, Hobrovej 18-22, Aalborg Universitetshospital, Aalborg Syd 9000, Danmark.
| | - John Johansen
- Department of Materials and Production, Aalborg University, Fibigerstræde 16, 2-114, Aalborg Ø 9220, Danmark.
| |
Collapse
|
2
|
Archer H, Reine S, Xia S, Vazquez LC, Ashikyan O, Pezeshk P, Kohli A, Xi Y, Wells JE, Hummer A, Difranco M, Chhabra A. Reliability assessment of leg length and angular alignment on manual reads versus artificial intelligence-generated lower extremity radiographic measurements. Clin Imaging 2024; 113:110233. [PMID: 39029361 DOI: 10.1016/j.clinimag.2024.110233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 06/25/2024] [Accepted: 07/08/2024] [Indexed: 07/21/2024]
Abstract
PURPOSE Leg length discrepancy (LLD) and lower extremity malalignment can lead to pain and osteoarthritis. A variety of radiographic parameters are used to assess LLD and alignment. A 510(k) FDA approved artificial intelligence (AI) software locates landmarks on full leg standing radiographs and performs several measurements. The objective of this study was to assess the reliability of this AI tool compared to three manual readers. METHODS A sample of 320 legs was used. Three readers' measurements were compared to AI output for hip-knee-angle (HKA), anatomical-tibiofemoral angle (aTFA), anatomical-mechanical-axis angle (AMA), joint-line-convergence angle (JLCA), mechanical-lateral-proximal-femur-angle (mLPFA), mechanical-lateral-distal-femur-angle (mLDFA), mechanical-medial-proximal-tibia-angle (mMPTA), mechanical-lateral-distal-tibia- angle (mLDTA), femur length, tibia length, full leg length, leg-length-discrepancy (LLD), and mechanical-axis-deviation (MAD). Intraclass correlation coefficients (ICCs) and Bland-Altman analysis were used to track performance. RESULTS AI output was successfully produced for 272/320 legs in the study. The reader versus AI pairwise ICCs were mostly in the excellent range: 12/13, 12/13, and 9/13 variables were in the excellent range (ICC > 0.75) for readers 1, 2, and 3, respectively. There was better agreement for leg length, femur length, tibia length, LLD, and HKA than for other variables. The median reading times for the three readers and AI were 250, 282, 236, and 38 s, respectively. CONCLUSION This study showed that AI-based software provides reliable assessment of LLD and lower extremity alignment with substantial time savings.
Collapse
Affiliation(s)
- Holden Archer
- University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, USA. https://twitter.com/@HoldenArcher
| | - Seth Reine
- University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, USA
| | - Shuda Xia
- University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, USA
| | - Louis Camilo Vazquez
- University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, USA
| | - Oganes Ashikyan
- University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, USA
| | - Parham Pezeshk
- University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, USA
| | - Ajay Kohli
- University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, USA
| | - Yin Xi
- University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, USA
| | - Joel E Wells
- Baylor Scott & White, 5220 W University Dr, McKinney, TX 75071, USA. https://twitter.com/@Joelwellsmd
| | - Allan Hummer
- IB Lab GmbH, Zehetnergasse 6/2/2, 1140 Vienna, Austria
| | | | - Avneesh Chhabra
- University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, USA.
| |
Collapse
|
3
|
Di Matteo V, Tommasini T, Morandini P, Savevski V, Grappiolo G, Loppini M. Machine Learning Prediction Model to Predict Length of Stay of Patients Undergoing Hip or Knee Arthroplasties: Results from a High-Volume Single-Center Multivariate Analysis. J Clin Med 2024; 13:5180. [PMID: 39274393 PMCID: PMC11395981 DOI: 10.3390/jcm13175180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2024] [Revised: 08/27/2024] [Accepted: 08/29/2024] [Indexed: 09/16/2024] Open
Abstract
Background: The growth of arthroplasty procedures requires innovative strategies to reduce inpatients' hospital length of stay (LOS). This study aims to develop a machine learning prediction model that may aid in predicting LOS after hip or knee arthroplasties. Methods: A collection of all the clinical notes of patients who underwent elective primary or revision arthroplasty from 1 January 2019 to 31 December 2019 was performed. The hospitalization was classified as "short LOS" if it was less than or equal to 6 days and "long LOS" if it was greater than 7 days. Clinical data from pre-operative laboratory analysis, vital parameters, and demographic characteristics of patients were screened. Final data were used to train a logistic regression model with the aim of predicting short or long LOS. Results: The final dataset was composed of 1517 patients (795 "long LOS", 722 "short LOS", p = 0.3196) with a total of 1541 hospital admissions (729 "long LOS", 812 "short LOS", p < 0.001). The complete model had a prediction efficacy of 78.99% (AUC 0.7899). Conclusions: Machine learning may facilitate day-by-day clinical practice determination of which patients are suitable for a shorter LOS and which for a longer LOS, in which a cautious approach could be recommended.
Collapse
Affiliation(s)
- Vincenzo Di Matteo
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090 Milan, Italy
- Orthopedics and Trauma Surgery Unit, Department of Aging, Orthopedic and Rheumatologic Sciences, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
- IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
| | - Tobia Tommasini
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Pierandrea Morandini
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Victor Savevski
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Guido Grappiolo
- IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
- Fondazione Livio Sciutto Onlus, Campus Savona, Università Degli Studi di Genova, 17100 Savona, Italy
| | - Mattia Loppini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090 Milan, Italy
- IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
- Fondazione Livio Sciutto Onlus, Campus Savona, Università Degli Studi di Genova, 17100 Savona, Italy
| |
Collapse
|
4
|
Jeon YD, Jung KH, Kim MS, Kim H, Yoon DK, Park KB. Clinical validation of artificial intelligence-based preoperative virtual reduction for Neer 3- or 4-part proximal humerus fractures. BMC Musculoskelet Disord 2024; 25:669. [PMID: 39192203 DOI: 10.1186/s12891-024-07798-z] [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: 01/19/2024] [Accepted: 08/19/2024] [Indexed: 08/29/2024] Open
Abstract
BACKGROUND If reduction images of fractures can be provided in advance with artificial-intelligence (AI)-based technology, it can assist with preoperative surgical planning. Recently, we developed the AI-based preoperative virtual reduction model for orthopedic trauma, which can provide an automatic segmentation and reduction of fractured fragments. The purpose of this study was to validate a quality of reduction model of Neer 3- or 4-part proximal humerus fractures established by AI-based technology. METHODS To develop the AI-based preoperative virtual reduction model, deep learning performed the segmentation of fracture fragments, and a Monte Carlo simulation completed the virtual reduction to determine the best model. A total of 20 pre/postoperative three-dimensional computed tomography (CT) scans of proximal humerus fracture were prepared. The preoperative CT scans were employed as the input of AI-based automated reduction (AI-R) to deduce the reduction models of fracture fragments, meanwhile, the manual reduction (MR) was conducted using the same CT images. Dice similarity coefficient (DSC) and intersection over union (IoU) between the reduction model from the AI-R/MR and postoperative CT scans were evaluated. Working times were compared between the two groups. Clinical validity agreement (CVA) and reduction quality score (RQS) were investigated for clinical validation outcomes by 20 orthopedic surgeons. RESULTS The mean DSC and IoU were better when using AI-R that when using MR (0.78 ± 0.13 vs. 0.69 ± 0.16, p < 0.001 and 0.65 ± 0.16 vs. 0.55 ± 0.18, p < 0.001, respectively). The working time of AI-R was, on average, 1.41% of that of MR. The mean CVA of all cases was 81%±14.7% (AI-R, 82.25%±14.27%; MR, 76.75%±14.17%, p = 0.06). The mean RQS was significantly higher when AI-R compared with MR was used (91.47 ± 1.12 vs. 89.30 ± 1.62, p = 0.045). CONCLUSION The AI-based preoperative virtual reduction model showed good performance in the reduction model in proximal humerus fractures with faster working times. Beyond diagnosis, classification, and outcome prediction, the AI-based technology can change the paradigm of preoperative surgical planning in orthopedic surgery. LEVEL OF EVIDENCE Level IV.
Collapse
Affiliation(s)
- Young Dae Jeon
- Department of Orthopaedic Surgery, University of Ulsan College of Medicine, Ulsan University Hospital, 25, Daehakbyeongwon-ro, Dong-Gu, Ulsan, 44033, Republic of Korea
| | - Kwang-Hwan Jung
- Department of Orthopaedic Surgery, University of Ulsan College of Medicine, Ulsan University Hospital, 25, Daehakbyeongwon-ro, Dong-Gu, Ulsan, 44033, Republic of Korea
| | - Moo-Sub Kim
- Industrial R&D Center, KAVILAB Co. Ltd, Seoul, Republic of Korea
| | - Hyeonjoo Kim
- Industrial R&D Center, KAVILAB Co. Ltd, Seoul, Republic of Korea
| | - Do-Kun Yoon
- Industrial R&D Center, KAVILAB Co. Ltd, Seoul, Republic of Korea
| | - Ki-Bong Park
- Department of Orthopaedic Surgery, University of Ulsan College of Medicine, Ulsan University Hospital, 25, Daehakbyeongwon-ro, Dong-Gu, Ulsan, 44033, Republic of Korea.
| |
Collapse
|
5
|
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.
Collapse
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
| |
Collapse
|
6
|
Inabathula A, Semerdzhiev DI, Srinivasan A, Amirouche F, Puri L, Piponov H. Robots on the Stage: A Snapshot of the American Robotic Total Knee Arthroplasty Market. JB JS Open Access 2024; 9:e24.00063. [PMID: 39238881 PMCID: PMC11368221 DOI: 10.2106/jbjs.oa.24.00063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/07/2024] Open
Abstract
» Computer-assisted robots aid orthopaedic surgeons in implant positioning and bony resection. Surgeons selecting a robot for their practice are faced with numerous options. This study aims to make the choice less daunting by reviewing the most commonly used Food and Drug Administration-approved robotic total knee arthroplasty platforms in the American arthroplasty market.» Modern total knee arthroplasty (TKA) robots use computer guidance to create a virtual knee model that serves as the surgeon's canvas for resection planning.» Most available robotic TKA (rTKA) systems are closed semiactive systems that restrict implant use to those of the manufacturer.» Each system has distinct imaging requirements, safety features, resection methods, and operating room footprints that will affect a surgeon's technique and practice.» Robots carry different purchase, maintenance, and equipment costs that will influence patient access across different socioeconomic groups.» Some studies show improved early patient-reported outcomes with rTKA, but long-term studies have yet to show clinical superiority over manual TKA.
Collapse
Affiliation(s)
| | | | | | | | - Lalit Puri
- Northshore University Health System, Evanston, Illinois
| | | |
Collapse
|
7
|
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.
Collapse
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
| |
Collapse
|
8
|
Chen JB, Borsinger TM, Chalmers BP, Debbi EM. Joint Replacement Technology in the Ambulatory Surgery Center: Current and Future Applications. HSS J 2024; 20:18-21. [PMID: 38356759 PMCID: PMC10863591 DOI: 10.1177/15563316231209500] [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: 06/15/2023] [Accepted: 06/21/2023] [Indexed: 02/16/2024]
Abstract
Total joint replacement (TJR) surgery in the ambulatory surgery centers (ASCs) has grown significantly over the past several years, along with the ability to improve the value of care. Standardization of high-quality, perioperative care is pivotal to the success of a TJR ASC program. As surgeons are experiencing increasing overhead with decreasing reimbursement, technology integration can provide major advantages. In this article, we will therefore highlight several examples of technologies that are changing the field and improving care in the preoperative, intraoperative, and postoperative settings.
Collapse
Affiliation(s)
- James B Chen
- Mission Orthopedic Institute, Providence Mission Hospital, Mission Viejo, CA, USA
| | - Tracy M Borsinger
- Adult Reconstruction and Joint Replacement, Hospital for Special Surgery, New York, NY, USA
| | - Brian P Chalmers
- Adult Reconstruction and Joint Replacement, Hospital for Special Surgery, New York, NY, USA
| | - Eytan M Debbi
- Adult Reconstruction and Joint Replacement, Hospital for Special Surgery, New York, NY, USA
| |
Collapse
|
9
|
Klemt C, Yeo I, Harvey M, Burns JC, Melnic C, Uzosike AC, Kwon YM. The Use of Artificial Intelligence for the Prediction of Periprosthetic Joint Infection Following Aseptic Revision Total Knee Arthroplasty. J Knee Surg 2024; 37:158-166. [PMID: 36731501 DOI: 10.1055/s-0043-1761259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Periprosthetic joint infection (PJI) following revision total knee arthroplasty (TKA) for aseptic failure is associated with poor outcomes, patient morbidity, and high health care expenditures. The aim of this study was to develop novel machine learning algorithms for the prediction of PJI following revision TKA for patients with aseptic indications for revision surgery. A single-institution database consisting of 1,432 consecutive revision TKA patients with aseptic etiologies was retrospectively identified. The patient cohort included 208 patients (14.5%) who underwent re-revision surgery for PJI. Three machine learning algorithms (artificial neural networks, support vector machines, k-nearest neighbors) were developed to predict this outcome and these models were assessed by discrimination, calibration, and decision curve analysis. This is a retrospective study. Among the three machine learning models, the neural network model achieved the best performance across discrimination (area under the receiver operating characteristic curve = 0.78), calibration, and decision curve analysis. The strongest predictors for PJI following revision TKA for aseptic reasons were prior open procedure prior to revision surgery, drug abuse, obesity, and diabetes. This study utilized machine learning as a tool for the prediction of PJI following revision TKA for aseptic failure with excellent performance. The validated machine learning models can aid surgeons in patient-specific risk stratifying to assist in preoperative counseling and clinical decision making for patients undergoing aseptic revision TKA.
Collapse
Affiliation(s)
- Christian Klemt
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Ingwon Yeo
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Michael Harvey
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Jillian C Burns
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Christopher Melnic
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Akachimere Cosmas Uzosike
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Young-Min Kwon
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| |
Collapse
|
10
|
Chen TLW, Buddhiraju A, Seo HH, Shimizu MR, Bacevich BM, Kwon YM. Can machine learning models predict prolonged length of hospital stay following primary total knee arthroplasty based on a national patient cohort data? Arch Orthop Trauma Surg 2023; 143:7185-7193. [PMID: 37592158 DOI: 10.1007/s00402-023-05013-7] [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: 06/06/2023] [Accepted: 07/23/2023] [Indexed: 08/19/2023]
Abstract
INTRODUCTION The total length of stay (LOS) is one of the biggest determinators of overall care costs associated with total knee arthroplasty (TKA). An accurate prediction of LOS could aid in optimizing discharge strategy for patients in need and diminishing healthcare expenditure. The aim of this study was to predict LOS following TKA using machine learning models developed on a national-scale patient cohort. METHODS The ACS-NSQIP database was queried to acquire 267,966 TKA cases from 2013 to 2020. Four machine learning models-artificial neural network (ANN), random forest, histogram-based gradient boosting, and k-nearest neighbor were trained and tested on the dataset for the prediction of prolonged LOS (LOS exceeded the 75th of all values in the cohort). The model performance was assessed by discrimination (area under the receiver operating characteristic curve [AUC]), calibration, and clinical utility. RESULTS ANN delivered the best performance among the four models. ANN distinguished prolonged LOS in the study cohort with an AUC of 0.71 and accurately predicted the probability of prolonged LOS for individual patients (calibration slope: 0.82; calibration intercept: 0.03; Brier score: 0.089). All models demonstrated clinical utility by generating positive net benefits in decision curve analyses. Operation time, pre-operative transfusion, pre-operative laboratory tests (hematocrit, platelet count, and white blood cell count), and BMI were the strongest predictors of prolonged LOS. CONCLUSION ANN demonstrated modest discrimination capacity and excellent performance in calibration and clinical utility for the prediction of prolonged LOS following TKA. Clinical application of the machine learning models has the potential to improve care coordination and discharge planning for patients at high risk of extended hospitalization after surgery. Incorporating more relevant patient factors may further increase the models' prediction strength.
Collapse
Affiliation(s)
- Tony Lin-Wei Chen
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Anirudh Buddhiraju
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Henry Hojoon Seo
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Michelle Riyo Shimizu
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Blake M Bacevich
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Young-Min Kwon
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
11
|
Zhang J, Santos C, Park C, Mazurowski MA, Colglazier R. Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approach. J Digit Imaging 2023; 36:2402-2410. [PMID: 37620710 PMCID: PMC10584746 DOI: 10.1007/s10278-023-00894-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 07/28/2023] [Accepted: 07/31/2023] [Indexed: 08/26/2023] Open
Abstract
Large numbers of radiographic images are available in musculoskeletal radiology practices which could be used for training of deep learning models for diagnosis of knee abnormalities. However, those images do not typically contain readily available labels due to limitations of human annotations. The purpose of our study was to develop an automated labeling approach that improves the image classification model to distinguish normal knee images from those with abnormalities or prior arthroplasty. The automated labeler was trained on a small set of labeled data to automatically label a much larger set of unlabeled data, further improving the image classification performance for knee radiographic diagnosis. We used BioBERT and EfficientNet as the feature extraction backbone of the labeler and imaging model, respectively. We developed our approach using 7382 patients and validated it on a separate set of 637 patients. The final image classification model, trained using both manually labeled and pseudo-labeled data, had the higher weighted average AUC (WA-AUC 0.903) value and higher AUC values among all classes (normal AUC 0.894; abnormal AUC 0.896, arthroplasty AUC 0.990) compared to the baseline model (WA-AUC = 0.857; normal AUC 0.842; abnormal AUC 0.848, arthroplasty AUC 0.987), trained using only manually labeled data. Statistical tests show that the improvement is significant on normal (p value < 0.002), abnormal (p value < 0.001), and WA-AUC (p value = 0.001). Our findings demonstrated that the proposed automated labeling approach significantly improves the performance of image classification for radiographic knee diagnosis, allowing for facilitating patient care and curation of large knee datasets.
Collapse
Affiliation(s)
- Jikai Zhang
- Department of Electrical and Computer Engineering, Duke University, Room 10070, 2424 Erwin Road, Durham, NC, 27705, USA.
| | - Carlos Santos
- Wake Forest University, Winston-Salem, NC, 27109, USA
| | - Christine Park
- Department of Radiology, Duke University Medical Center, Durham, NC, USA
| | - Maciej A Mazurowski
- Department of Radiology, Duke University Medical Center, Durham, NC, USA
- Department of Electrical and Computer Engineering, Department of Biostatistics and Bioinformatics, Department of Computer Science, Duke University, Durham, NC, USA
| | - Roy Colglazier
- Department of Radiology, Duke University Medical Center, Durham, NC, USA
| |
Collapse
|
12
|
Michelsen C, Jørgensen CC, Heltberg M, Jensen MH, Lucchetti A, Petersen PB, Petersen T, Kehlet H, Madsen F, Hansen TB, Gromov K, Jakobsen T, Varnum C, Overgaard S, Rathsach M, Hansen L. Machine-learning vs. logistic regression for preoperative prediction of medical morbidity after fast-track hip and knee arthroplasty-a comparative study. BMC Anesthesiol 2023; 23:391. [PMID: 38030979 PMCID: PMC10685559 DOI: 10.1186/s12871-023-02354-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 11/21/2023] [Indexed: 12/01/2023] Open
Abstract
BACKGROUND Machine-learning models may improve prediction of length of stay (LOS) and morbidity after surgery. However, few studies include fast-track programs, and most rely on administrative coding with limited follow-up and information on perioperative care. This study investigates potential benefits of a machine-learning model for prediction of postoperative morbidity in fast-track total hip (THA) and knee arthroplasty (TKA). METHODS Cohort study in consecutive unselected primary THA/TKA between 2014-2017 from seven Danish centers with established fast-track protocols. Preoperative comorbidity and prescribed medication were recorded prospectively and information on length of stay and readmissions was obtained through the Danish National Patient Registry and medical records. We used a machine-learning model (Boosted Decision Trees) based on boosted decision trees with 33 preoperative variables for predicting "medical" morbidity leading to LOS > 4 days or 90-days readmissions and compared to a logistical regression model based on the same variables. We also evaluated two parsimonious models, using the ten most important variables in the full machine-learning and logistic regression models. Data collected between 2014-2016 (n:18,013) was used for model training and data from 2017 (n:3913) was used for testing. Model performances were analyzed using precision, area under receiver operating (AUROC) and precision recall curves (AUPRC), as well as the Mathews Correlation Coefficient. Variable importance was analyzed using Shapley Additive Explanations values. RESULTS Using a threshold of 20% "risk-patients" (n:782), precision, AUROC and AUPRC were 13.6%, 76.3% and 15.5% vs. 12.4%, 74.7% and 15.6% for the machine-learning and logistic regression model, respectively. The parsimonious machine-learning model performed better than the full logistic regression model. Of the top ten variables, eight were shared between the machine-learning and logistic regression models, but with a considerable age-related variation in importance of specific types of medication. CONCLUSION A machine-learning model using preoperative characteristics and prescriptions slightly improved identification of patients in high-risk of "medical" complications after fast-track THA and TKA compared to a logistic regression model. Such algorithms could help find a manageable population of patients who may benefit most from intensified perioperative care.
Collapse
Affiliation(s)
- Christian Michelsen
- The Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100, Copenhagen, Denmark
| | - Christoffer C Jørgensen
- Department of Anesthesia and Intensive Care, Hospital of Northern Zealand, Dyrehavevej 29 3400, Hillerød, Denmark.
- The Centre for Fast-Track Hip and Knee Replacement, 7621, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark.
| | - Mathias Heltberg
- The Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100, Copenhagen, Denmark
| | - Mogens H Jensen
- The Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100, Copenhagen, Denmark
| | - Alessandra Lucchetti
- The Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100, Copenhagen, Denmark
| | - Pelle B Petersen
- Department of Anesthesia and Intensive Care, Hospital of Northern Zealand, Dyrehavevej 29 3400, Hillerød, Denmark
- The Centre for Fast-Track Hip and Knee Replacement, 7621, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Troels Petersen
- The Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100, Copenhagen, Denmark
| | - Henrik Kehlet
- The Centre for Fast-Track Hip and Knee Replacement, 7621, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark
- Section of Surgical Pathophysiology, 7621, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | | | | | | | | | | | | | | | | |
Collapse
|
13
|
Zhang Z, Luo Y, Zhang C, Wang X, Zhang T, Zhang G. Prediction of gap balancing based on 2-D radiography in total knee arthroplasty for knee osteoarthritis patients. ARTHROPLASTY 2023; 5:60. [PMID: 37968740 PMCID: PMC10652581 DOI: 10.1186/s42836-023-00218-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 10/13/2023] [Indexed: 11/17/2023] Open
Abstract
BACKGROUND To investigate the influence of osteophytes on postoperative gap balancing, and to work out a predictive model of the relationship between osteophyte size and gap gaining in primary total knee replacement. METHODS One hundred and ten patients were enrolled in the study. Pre- and postoperative radiographs were collected and analyzed. They were assigned to the training dataset and test dataset randomly at a ratio of 9:1 by using the statistical package R (version 4.0.5). Size and marginal distances of osteophytes, planned bone cut planes, predicted bone cuts and joint gaps were labeled on the preoperative standing anteroposterior and lateral views, while actual bone cuts and joint gaps were recorded on the postoperative plain films, respectively. Statistical analysis was performed. RESULTS Actual joint gaps were significantly related to the distances of medial and lateral predictive bone cutting lines, bone cut thickness on tibial side and posterior condylar, as well as size and marginal distances of osteophytes (P < 0.05). A predictive equation was generated, with a root mean square error (RMSE) of 3.4761 in validation. A 2-D planning system with adjustable input parameters and dim predictive outputs on joint gap was developed. The equation is [Formula: see text] CONCLUSION: Postoperative joint gap can be predicted on the basis of preoperative measurements on 2-D plain films. Larger sample size may help improve the effectiveness and accuracy of the predictive equation.
Collapse
Affiliation(s)
- Zhuo Zhang
- Department of Adult Reconstruction and Joint Replacement, Senior Orthopedic Department, Fourth Medical Center, Chinese PLA General Hospital, No. 51, Fucheng Road, Beijing, 100048, China.
| | - Yang Luo
- Department of Orthopedics, First Medical Center, Chinese PLA General Hospital, No.28, Fuxing Road, Beijing, 100853, China
| | - Chong Zhang
- Yunnan Baiyao Group Medicine Electronic Commerce Co., Ltd, No. 3686 Yunnan Baiyao Street, Chenggong District, Kunming, 650500, Yunnan, China
| | - Xin Wang
- Yunnan Baiyao Group Medicine Electronic Commerce Co., Ltd, No. 3686 Yunnan Baiyao Street, Chenggong District, Kunming, 650500, Yunnan, China
| | - Tianwei Zhang
- Department of Adult Reconstruction and Joint Replacement, Senior Orthopedic Department, Fourth Medical Center, Chinese PLA General Hospital, No. 51, Fucheng Road, Beijing, 100048, China
| | - Guoqiang Zhang
- Department of Adult Reconstruction and Joint Replacement, Senior Orthopedic Department, Fourth Medical Center, Chinese PLA General Hospital, No. 51, Fucheng Road, Beijing, 100048, China
| |
Collapse
|
14
|
Karlin EA, Lin CC, Meftah M, Slover JD, Schwarzkopf R. The Impact of Machine Learning on Total Joint Arthroplasty Patient Outcomes: A Systemic Review. J Arthroplasty 2023; 38:2085-2095. [PMID: 36441039 DOI: 10.1016/j.arth.2022.10.039] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 10/19/2022] [Accepted: 10/24/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Supervised machine learning techniques have been increasingly applied to predict patient outcomes after hip and knee arthroplasty procedures. The purpose of this study was to systematically review the applications of supervised machine learning techniques to predict patient outcomes after primary total hip and knee arthroplasty. METHODS A comprehensive literature search using the electronic databases MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and Cochrane Database of Systematic Reviews was conducted in July of 2021. The inclusion criteria were studies that utilized supervised machine learning techniques to predict patient outcomes after primary total hip or knee arthroplasty. RESULTS Search criteria yielded n = 30 relevant studies. Topics of study included patient complications (n = 6), readmissions (n = 1), revision (n = 2), patient-reported outcome measures (n = 4), patient satisfaction (n = 4), inpatient status and length of stay (LOS) (n = 9), opioid usage (n = 3), and patient function (n = 1). Studies involved TKA (n = 12), THA (n = 11), or a combination (n = 7). Less than 35% of predictive outcomes had an area under the receiver operating characteristic curve (AUC) in the excellent or outstanding range. Additionally, only 9 of the studies found improvement over logistic regression, and only 9 studies were externally validated. CONCLUSION Supervised machine learning algorithms are powerful tools that have been increasingly applied to predict patient outcomes after total hip and knee arthroplasty. However, these algorithms should be evaluated in the context of prognostic accuracy, comparison to traditional statistical techniques for outcome prediction, and application to populations outside the training set. While machine learning algorithms have been received with considerable interest, they should be critically assessed and validated prior to clinical adoption.
Collapse
Affiliation(s)
- Elan A Karlin
- MedStar Georgetown University Hospital, Washington, District of Columbia
| | - Charles C Lin
- Department of Orthopedic Surgery, NYU Langone Health, New York, New York
| | - Morteza Meftah
- Department of Orthopedic Surgery, NYU Langone Health, New York, New York
| | - James D Slover
- Department of Orthopedic Surgery, NYU Langone Health, New York, New York
| | - Ran Schwarzkopf
- Department of Orthopedic Surgery, NYU Langone Health, New York, New York
| |
Collapse
|
15
|
Karnuta JM, Shaikh HJF, Murphy MP, Brown NM, Pearle AD, Nawabi DH, Chen AF, Ramkumar PN. Artificial Intelligence for Automated Implant Identification in Knee Arthroplasty: A Multicenter External Validation Study Exceeding 3.5 Million Plain Radiographs. J Arthroplasty 2023; 38:2004-2008. [PMID: 36940755 DOI: 10.1016/j.arth.2023.03.039] [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: 11/14/2022] [Revised: 03/13/2023] [Accepted: 03/14/2023] [Indexed: 03/23/2023] Open
Abstract
BACKGROUND Surgical management of complications following knee arthroplasty demands accurate and timely identification of implant manufacturer and model. Automated image processing using deep machine learning has been previously developed and internally validated; however, external validation is essential prior to scaling clinical implementation for generalizability. METHODS We trained, validated, and externally tested a deep learning system to classify knee arthroplasty systems as one of the 9 models from 4 manufacturers derived from 4,724 original, retrospectively collected anteroposterior plain knee radiographs across 3 academic referral centers. From these radiographs, 3,568 were used for training, 412 for validation, and 744 for external testing. Augmentation was applied to the training set (n = 3,568,000) to increase model robustness. Performance was determined by the area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy. Implant identification processing speed was calculated. The training and testing sets were drawn from statistically different populations of implants (P < .001). RESULTS After 1,000 training epochs by the deep learning system, the system discriminated 9 implant models with a mean area under the receiver operating characteristic curve of 0.989, accuracy of 97.4%, sensitivity of 89.2%, and specificity of 99.0% in the external testing dataset of 744 anteroposterior radiographs. The software classified implants at a mean speed of 0.02 seconds per image. CONCLUSION An artificial intelligence-based software for identifying knee arthroplasty implants demonstrated excellent internal and external validation. Although continued surveillance is necessary with implant library expansion, this software represents a responsible and meaningful clinical application of artificial intelligence with immediate potential to globally scale and assist in preoperative planning prior to revision knee arthroplasty.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | - Prem N Ramkumar
- Hospital for Special Surgery, New York, New York; Long Beach Orthopaedic Institute, Long Beach, California
| |
Collapse
|
16
|
Chen TLW, Buddhiraju A, Seo HH, Subih MA, Tuchinda P, Kwon YM. Internal and External Validation of the Generalizability of Machine Learning Algorithms in Predicting Non-home Discharge Disposition Following Primary Total Knee Joint Arthroplasty. J Arthroplasty 2023; 38:1973-1981. [PMID: 36764409 DOI: 10.1016/j.arth.2023.01.065] [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: 11/24/2022] [Revised: 01/24/2023] [Accepted: 01/31/2023] [Indexed: 02/12/2023] Open
Abstract
BACKGROUND Nonhome discharge disposition following primary total knee arthroplasty (TKA) is associated with a higher rate of complications and constitutes a socioeconomic burden on the health care system. While existing algorithms predicting nonhome discharge disposition varied in degrees of mathematical complexity and prediction power, their capacity to generalize predictions beyond the development dataset remains limited. Therefore, this study aimed to establish the machine learning model generalizability by performing internal and external validations using nation-scale and institutional cohorts, respectively. METHODS Four machine learning models were trained using the national cohort. Recursive feature elimination and hyper-parameter tuning were applied. Internal validation was achieved through five-fold cross-validation during model training. The trained models' performance was externally validated using the institutional cohort and assessed by discrimination, calibration, and clinical utility. RESULTS The national (424,354 patients) and institutional (10,196 patients) cohorts had non-home discharge rates of 19.4 and 36.4%, respectively. The areas under the receiver operating curve of the model predictions were 0.83 to 0.84 during internal validation and increased to 0.88 to 0.89 during external validation. Artificial neural network and histogram-based gradient boosting elicited the best performance with a mean area under the receiver operating curve of 0.89, calibration slope of 1.39, and Brier score of 0.14, which indicated that the two models were robust in distinguishing non-home discharge and well-calibrated with accurate predictions of the probabilities. The low inter-dataset similarity indicated reliable external validation. Length of stay, age, body mass index, and sex were the strongest predictors of discharge destination after primary TKA. CONCLUSION The machine learning models demonstrated excellent predictive performance during both internal and external validations, supporting their generalizability across different patient cohorts and potential applicability in the clinical workflow.
Collapse
Affiliation(s)
- Tony Lin-Wei Chen
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Anirudh Buddhiraju
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Henry Hojoon Seo
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Murad Abdullah Subih
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Pete Tuchinda
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Young-Min Kwon
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| |
Collapse
|
17
|
Karnuta JM, Murphy MP, Luu BC, Ryan MJ, Haeberle HS, Brown NM, Iorio R, Chen AF, Ramkumar PN. Artificial Intelligence for Automated Implant Identification in Total Hip Arthroplasty: A Multicenter External Validation Study Exceeding Two Million Plain Radiographs. J Arthroplasty 2023; 38:1998-2003.e1. [PMID: 35271974 DOI: 10.1016/j.arth.2022.03.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 02/23/2022] [Accepted: 03/01/2022] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND The surgical management of complications after total hip arthroplasty (THA) necessitates accurate identification of the femoral implant manufacturer and model. Automated image processing using deep learning has been previously developed and internally validated; however, external validation is necessary prior to responsible application of artificial intelligence (AI)-based technologies. METHODS We trained, validated, and externally tested a deep learning system to classify femoral-sided THA implants as one of the 8 models from 2 manufacturers derived from 2,954 original, deidentified, retrospectively collected anteroposterior plain radiographs across 3 academic referral centers and 13 surgeons. From these radiographs, 2,117 were used for training, 249 for validation, and 588 for external testing. Augmentation was applied to the training set (n = 2,117,000) to increase model robustness. Performance was evaluated by area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy. Implant identification processing speed was calculated. RESULTS The training and testing sets were drawn from statistically different populations of implants (P < .001). After 1,000 training epochs by the deep learning system, the system discriminated 8 implant models with a mean area under the receiver operating characteristic curve of 0.991, accuracy of 97.9%, sensitivity of 88.6%, and specificity of 98.9% in the external testing dataset of 588 anteroposterior radiographs. The software classified implants at a mean speed of 0.02 seconds per image. CONCLUSION An AI-based software demonstrated excellent internal and external validation. Although continued surveillance is necessary with implant library expansion, this software represents responsible and meaningful clinical application of AI with immediate potential to globally scale and assist in preoperative planning prior to revision THA.
Collapse
Affiliation(s)
- Jaret M Karnuta
- Orthopaedic Machine Learning Laboratory, Orthopaedic Intelligence LLC, Cleveland Heights, OH; Department of Orthopaedic Surgery, University of Pennsylvania, Philadelphia, PA
| | - Michael P Murphy
- Department of Orthopaedic Surgery & Rehabilitation, Loyola University Medical Center, Chicago, IL
| | - Bryan C Luu
- Orthopaedic Machine Learning Laboratory, Orthopaedic Intelligence LLC, Cleveland Heights, OH; Department of Orthopaedic Surgery, Baylor College of Medicine, Houston, TX
| | - Michael J Ryan
- Orthopaedic Machine Learning Laboratory, Orthopaedic Intelligence LLC, Cleveland Heights, OH
| | - Heather S Haeberle
- Orthopaedic Machine Learning Laboratory, Orthopaedic Intelligence LLC, Cleveland Heights, OH; Sports Medicine Institute, Hospital for Special Surgery, New York, NY
| | - Nicholas M Brown
- Department of Orthopaedic Surgery & Rehabilitation, Loyola University Medical Center, Chicago, IL
| | - Richard Iorio
- Department of Orthopaedic Surgery, Brigham & Women's Hospital, Boston, MA
| | - Antonia F Chen
- Department of Orthopaedic Surgery, Brigham & Women's Hospital, Boston, MA
| | - Prem N Ramkumar
- Orthopaedic Machine Learning Laboratory, Orthopaedic Intelligence LLC, Cleveland Heights, OH; Sports Medicine Institute, Hospital for Special Surgery, New York, NY; Department of Orthopaedic Surgery, Brigham & Women's Hospital, Boston, MA
| |
Collapse
|
18
|
Kurmis AP. A role for artificial intelligence applications inside and outside of the operating theatre: a review of contemporary use associated with total knee arthroplasty. ARTHROPLASTY 2023; 5:40. [PMID: 37400876 DOI: 10.1186/s42836-023-00189-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 04/19/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) has become involved in many aspects of everyday life, from voice-activated virtual assistants built into smartphones to global online search engines. Similarly, many areas of modern medicine have found ways to incorporate such technologies into mainstream practice. Despite the enthusiasm, robust evidence to support the utility of AI in contemporary total knee arthroplasty (TKA) remains limited. The purpose of this review was to provide an up-to-date summary of the use of AI in TKA and to explore its current and future value. METHODS Initially, a structured systematic review of the literature was carried out, following PRISMA search principles, with the aim of summarising the understanding of the field and identifying clinical and knowledge gaps. RESULTS A limited body of published work exists in this area. Much of the available literature is of poor methodological quality and many published studies could be best described as "demonstration of concepts" rather than "proof of concepts". There exists almost no independent validation of reported findings away from designer/host sites, and the extrapolation of key results to general orthopaedic sites is limited. CONCLUSION While AI has certainly shown value in a small number of specific TKA-associated applications, the majority to date have focused on risk, cost and outcome prediction, rather than surgical care, per se. Extensive future work is needed to demonstrate external validity and reliability in non-designer settings. Well-performed studies are warranted to ensure that the scientific evidence base supporting the use of AI in knee arthroplasty matches the global hype.
Collapse
Affiliation(s)
- Andrew P Kurmis
- Discipline of Medical Specialties, University of Adelaide, Adelaide, SA, 5005, Australia.
- Department of Orthopaedic Surgery, Lyell McEwin Hospital, Haydown Road, Elizabeth Vale, SA, 5112, Australia.
- College of Medicine & Public Health, Flinders University, Bedford Park, SA, 5042, Australia.
| |
Collapse
|
19
|
Nham FH, Court T, Zalikha AK, El-Othmani MM, Shah RP. Assessing the predictive capacity of machine learning models using patient-specific variables in determining in-hospital outcomes after THA. J Orthop 2023; 41:39-46. [PMID: 37304653 PMCID: PMC10248727 DOI: 10.1016/j.jor.2023.05.012] [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] [Received: 05/16/2023] [Revised: 05/25/2023] [Accepted: 05/29/2023] [Indexed: 06/13/2023] Open
Abstract
Background Machine learning is a subset of artificial intelligence using algorithmic modeling to progressively learn and create predictive models. Clinical application of machine learning can aid physicians through identification of risk factors and implications of predicted patient outcomes. Aims The aim of this study was to compare patient-specific and situation perioperative variables through optimized machine learning models to predict postoperative outcomes. Methods Data from 2016 to 2017 from the National Inpatient Sample was used to identify 177,442 discharges undergoing primary total hip arthroplasty, which were included in the training, testing, and validation of 10 machine learning models. 15 predictive variables consisting of 8 patient-specific and 7 situational specific variables were utilized to predict 3 outcome variables: length of stay, discharge, and mortality. The machine learning models were assessed in responsiveness via area under the curve and reliability. Results For all outcomes, Linear Support Vector Machine had the highest responsiveness among all models when using all variables. When utilizing patient-specific variables only, responsiveness of the top 3 models ranged between 0.639 and 0.717 for length of stay, 0.703-0.786 for discharge disposition, and 0.887-0.952 for mortality. The top 3 models utilizing situational variables only produced responsiveness of 0.552-0.589 for length of stay, 0.543-0.574 for discharge disposition, and 0.469-0.536 for mortality. Conclusions Linear Support Vector Machine was the most responsive machine learning model of the 10 algorithms trained, while decision list was most reliable. Responsiveness was observed to be consistently higher with patient-specific variables than situational variables, emphasizing the predictive capacity and value of patient-specific variables. The current practice in machine learning literature generally deploys a single model, it is suboptimal to develop optimized models for application into clinical practice. The limitation of other algorithms may prohibit potential more reliable and responsive models.Level of Evidence III.
Collapse
Affiliation(s)
- Fong H. Nham
- Department of Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI, 48201, USA
| | - Tannor Court
- Department of Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI, 48201, USA
| | - Abdul K. Zalikha
- Department of Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI, 48201, USA
| | - Mouhanad M. El-Othmani
- Department of Orthopaedic Surgery, Columbia University Medical Center, New York, NY, 10032, USA
| | - Roshan P. Shah
- Department of Orthopaedic Surgery, Columbia University Medical Center, New York, NY, 10032, USA
| |
Collapse
|
20
|
Niculescu M, Honțaru OS, Popescu G, Sterian AG, Dobra M. Challenges of Integrating New Technologies for Orthopedic Doctors to Face up to Difficulties during the Pandemic Era. Healthcare (Basel) 2023; 11:1524. [PMID: 37297666 PMCID: PMC10288938 DOI: 10.3390/healthcare11111524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 05/19/2023] [Accepted: 05/21/2023] [Indexed: 06/12/2023] Open
Abstract
In the field of orthopedics, competitive progress is growing faster because new technologies used to facilitate the work of physicians are continuously developing. Based on the issues generated in the pandemic era in this field, a research study was developed to identify the intention of orthopedic doctors to integrate new medical technologies. The survey was based on a questionnaire that was used for data collection. The quantitative study registered a sample of 145 orthopedic doctors. The data analysis was performed based on the IBM SPSS program. A multiple linear regression model was applied, which analyzed how the independent variables can influence the dependent variables. After analyzing the data, it was observed that the intention of orthopedic doctors to use new medical technologies is influenced by the advantages and disadvantages perceived by them, the perceived risks, the quality of the medical technologies, the experience of physicians in their use, and their receptivity to other digital tools. The obtained results are highly important both for hospital managers and authorities, illustrating the main factors that influence doctors to use emergent technologies in their clinical work.
Collapse
Affiliation(s)
- Marius Niculescu
- Faculty of Medicine, “Titu Maiorescu” University of Bucharest, 031593 Bucharest, Romania;
- Colentina Hospital, Șoseaua Ștefan cel Mare 19-21, 020125 Bucharest, Romania
| | - Octavia-Sorina Honțaru
- Faculty of Sciences, Physical Education and Informatics, University of Pitesti, Târgul din Vale 1, 110040 Arges, Romania
- Department of Public Health Arges, Exercitiu 39 bis, 110438 Arges, Romania
| | - George Popescu
- Emergency Clinical Hospital Dr. Bagdasar-Arseni, Șoseaua Berceni 12, 041915 Bucharest, Romania
| | - Alin Gabriel Sterian
- Emergency Hospital for Children Grigore Alexandrescu, 30-32 Iancu de Hunedoara Boulevard, 011743 Bucharest, Romania;
- Department of Pediatric Surgery and Orthopedics, University of Medicine and Pharmacy “Carol Davila” Bucharest, 020021 Bucharest, Romania
| | - Mihai Dobra
- Center of Uronephrology and Renal Transplant Fundeni, University of Medicine and Pharmacy “Carol Davila” Bucharest, 020021 Bucharest, Romania;
| |
Collapse
|
21
|
Gupta P, Haeberle HS, Zimmer ZR, Levine WN, Williams RJ, Ramkumar PN. Artificial intelligence-based applications in shoulder surgery leaves much to be desired: a systematic review. JSES REVIEWS, REPORTS, AND TECHNIQUES 2023; 3:189-200. [PMID: 37588443 PMCID: PMC10426484 DOI: 10.1016/j.xrrt.2022.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Background Artificial intelligence (AI) aims to simulate human intelligence using automated computer algorithms. There has been a rapid increase in research applying AI to various subspecialties of orthopedic surgery, including shoulder surgery. The purpose of this review is to assess the scope and validity of current clinical AI applications in shoulder surgery literature. Methods A systematic literature review was conducted using PubMed for all articles published between January 1, 2010 and June 10, 2022. The search query used the terms as follows: (artificial intelligence OR machine learning OR deep learning) AND (shoulder OR shoulder surgery OR rotator cuff). All studies that examined AI application models in shoulder surgery were included and evaluated for model performance and validation (internal, external, or both). Results A total of 45 studies were included in the final analysis. Eighteen studies involved shoulder arthroplasty, 13 rotator cuff, and 14 other areas. Studies applying AI to shoulder surgery primarily involved (1) automated imaging analysis including identifying rotator cuff tears and shoulder implants (2) risk prediction analyses including perioperative complications, functional outcomes, and patient satisfaction. Highest model performance area under the curve ranged from 0.681 (poor) to 1.00 (perfect). Only 2 studies reported external validation. Conclusion Applications of AI in the field of shoulder surgery are expanding rapidly and offer patient-specific risk stratification for shared decision-making and process automation for resource preservation. However, model performance is modest and external validation remains to be demonstrated, suggesting increased scientific rigor is warranted prior to deploying AI-based clinical applications.
Collapse
Affiliation(s)
- Puneet Gupta
- Department of Orthopaedic Surgery, George Washington University School of Medicine and Health Sciences, Washington, DC, USA
- Department of Orthopaedic Surgery, Columbia University Irving Medical Center, New York, NY, USA
| | - Heather S. Haeberle
- Department of Orthopaedic Surgery, Columbia University Irving Medical Center, New York, NY, USA
| | - Zachary R. Zimmer
- Department of Orthopaedic Surgery, George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - William N. Levine
- Department of Orthopaedic Surgery, Columbia University Irving Medical Center, New York, NY, USA
| | - Riley J. Williams
- Institute for Cartilage Repair, Hospital for Special Surgery, New York, NY, USA
| | - Prem N. Ramkumar
- Institute for Cartilage Repair, Hospital for Special Surgery, New York, NY, USA
- Long Beach Orthopaedic Institute, Long Beach, CA, USA
| |
Collapse
|
22
|
Jang SJ, Fontana MA, Kunze KN, Anderson CG, Sculco TP, Mayman DJ, Jerabek SA, Vigdorchik JM, Sculco PK. An Interpretable Machine Learning Model for Predicting 10-Year Total Hip Arthroplasty Risk. J Arthroplasty 2023:S0883-5403(23)00336-4. [PMID: 37019312 DOI: 10.1016/j.arth.2023.03.087] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 03/20/2023] [Accepted: 03/25/2023] [Indexed: 04/07/2023] Open
Abstract
BACKGROUND As the demand for total hip arthroplasty (THA) rises, a predictive model for THA risk may aid patients and clinicians in augmenting shared decision-making. We aimed to develop and validate a model predicting THA within 10 years in patients using demographic, clinical, and deep learning (DL)-automated radiographic measurements. METHODS Patients enrolled in the Osteoarthritis Initiative were included. DL algorithms measuring osteoarthritis- and dysplasia-relevant parameters on baseline pelvis radiographs were developed. Demographic, clinical, and radiographic measurement variables were then used to train generalized additive models to predict THA within 10 years from baseline. A total of 4,796 patients were included (9,592 hips; 58% female; 230 THAs (2.4%)). Model performance using 1) baseline demographic and clinical variables 2) radiographic variables, and 3) all variables were compared. RESULTS Using 110 demographic and clinical variables, the model had a baseline area under the receiver operating curve (AUROC) of 0.68 and area under the precision recall curve (AUPRC) of 0.08. Using 26 DL-automated hip measurements, the AUROC was 0.77 and AUPRC was 0.22. Combining all variables, the model improved to an AUROC of 0.81 and AUPRC of 0.28. Three of the top five predictive features in the combined model were radiographic variables including minimum joint space along with hip pain and analgesic use. Partial dependency plots revealed predictive discontinuities for radiographic measurements consistent with literature thresholds of osteoarthritis progression and hip dysplasia. CONCLUSION A machine learning model predicting 10-year THA performed more accurately with DL radiographic measurements. The model weighted predictive variables in concordance with clinical THA-pathology assessments.
Collapse
Affiliation(s)
- Seong Jun Jang
- Weill Cornell College of Medicine, New York, NY, USA; Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA.
| | - Mark A Fontana
- Weill Cornell College of Medicine, New York, NY, USA; Center for Analytics, Modeling, and Performance, Hospital for Special Surgery, New York, NY, USA
| | - Kyle N Kunze
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA
| | | | - Thomas P Sculco
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA; Adult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, New York, NY, USA
| | - David J Mayman
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA; Adult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, New York, NY, USA
| | - Seth A Jerabek
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA; Adult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, New York, NY, USA
| | - Jonathan M Vigdorchik
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA; Adult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, New York, NY, USA
| | - Peter K Sculco
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA; Adult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, New York, NY, USA
| |
Collapse
|
23
|
Nham FH, Patel I, Zalikha AK, El-Othmani MM. Epidemiology of primary and revision total knee arthroplasty: analysis of demographics, comorbidities and outcomes from the national inpatient sample. ARTHROPLASTY 2023; 5:18. [PMID: 37004093 PMCID: PMC10068145 DOI: 10.1186/s42836-023-00175-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 02/22/2023] [Indexed: 04/03/2023] Open
Abstract
INTRODUCTION Primary total knee arthroplasty (TKA) is a preferred treatment for end-stage knee osteoarthritis. In the setting of a failed TKA, revision total knee arthroplasty (rTKA) acts as a salvage procedure and carries a higher risk compared to primary TKA. Given increased interest in postoperative outcomes from these procedures, a thorough understanding of the demographics, comorbidities, and inpatient outcomes is warranted. This study aimed to report the epidemiological data of demographics, comorbidity profiles and outcomes of patients undergoing TKA and rTKA. METHODS A retrospective review of NIS registry discharge data from 2006 to 2015 third quarter was performed. This study included adults aged 40 and older who underwent TKA or rTKA. A total of 5,901,057 TKA patients and 465,968 rTKA patients were included in this study. Simple descriptive statistics were used to present variables on demographics, medical comorbidities, and postoperative complications. RESULTS A total of 5,901,057 TKA and 465,968 rTKA discharges were included in this study, with an average age of 66.30 and 66.56 years, and the major payor being Medicare, accounting for 55.34% and 59.88% of TKA and rTKA cases, respectively. Infection (24.62%) was the most frequent reason for rTKA, and was followed by mechanical complications (18.62%) and dislocation (7.67%). The most common medical comorbidities for both groups were hypertension, obesity, and diabetes. All types of inpatient complications were reported in 22.21% TKA and 28.78% of rTKA cases. Postoperative anemia was the most common complication in both groups (20.34% vs. 25.05%). CONCLUSIONS Our data demonstrated a 41.9% increase in patients receiving TKA and 28.8% increase in rTKA from the years 2006 to 2014. The data showed a 22.21% and a 28.78% "complication" rate with TKA and rTKA, with postoperative anemia being the most common complication. The top 3 medical comorbidities were hypertension, obesity, and diabetes for both groups and with increased focus on perioperative optimization, future analyses into preoperative medical optimization, and improved primary arthroplasty protocol may result in improved postoperative outcomes.
Collapse
Affiliation(s)
- Fong H Nham
- DMC Orthopaedics & Sports Medicine, 3990 John R Street, Detroit, MI, 48201, USA.
| | - Ishan Patel
- DMC Orthopaedics & Sports Medicine, 3990 John R Street, Detroit, MI, 48201, USA
| | - Abdul K Zalikha
- DMC Orthopaedics & Sports Medicine, 3990 John R Street, Detroit, MI, 48201, USA
| | - Mouhanad M El-Othmani
- Department of Orthopaedic Surgery, Columbia University Medical Center, 622 W 168Th Street, New York, NY, 10032, USA
| |
Collapse
|
24
|
Entezari B, Koucheki R, Abbas A, Toor J, Wolfstadt JI, Ravi B, Whyne C, Lex JR. Improving Resource Utilization for Arthroplasty Care by Leveraging Machine Learning and Optimization: A Systematic Review. Arthroplast Today 2023; 20:101116. [PMID: 36938350 PMCID: PMC10014272 DOI: 10.1016/j.artd.2023.101116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 01/28/2023] [Indexed: 03/21/2023] Open
Abstract
Background There is a growing demand for total joint arthroplasty (TJA) surgery. The applications of machine learning (ML), mathematical optimization, and computer simulation have the potential to improve efficiency of TJA care delivery through outcome prediction and surgical scheduling optimization, easing the burden on health-care systems. The purpose of this study was to evaluate strategies using advances in analytics and computational modeling that may improve planning and the overall efficiency of TJA care. Methods A systematic review including MEDLINE, Embase, and IEEE Xplore databases was completed from inception to October 3, 2022, for identification of studies generating ML models for TJA length of stay, duration of surgery, and hospital readmission prediction. A scoping review of optimization strategies in elective surgical scheduling was also conducted. Results Twenty studies were included for evaluating ML predictions and 17 in the scoping review of scheduling optimization. Among studies generating linear or logistic control models alongside ML models, only 1 found a control model to outperform its ML counterpart. Furthermore, neural networks performed superior to or at the same level as conventional ML models in all but 1 study. Implementation of mathematical and simulation strategies improved the optimization efficiency when compared to traditional scheduling methods at the operational level. Conclusions High-performing predictive ML-based models have been developed for TJA, as have mathematical strategies for elective surgical scheduling optimization. By leveraging artificial intelligence for outcome prediction and surgical optimization, there exist greater opportunities for improved resource utilization and cost-savings in TJA than when using traditional modeling and scheduling methods.
Collapse
Affiliation(s)
- Bahar Entezari
- Granovsky Gluskin Division of Orthopaedics, Mount Sinai Hospital, Toronto, Ontario, Canada
- Queen’s University School of Medicine, Kingston, Ontario, Canada
- Corresponding author. Mount Sinai Hospital, 15 Arch Street, Kingston, Ontario, Canada K7L 3N6. Tel.: +1 647 866 8729.
| | - Robert Koucheki
- Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Aazad Abbas
- Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Orthopaedic Biomechanics Lab, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Jay Toor
- Division of Orthopaedic Surgery, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Jesse I. Wolfstadt
- Granovsky Gluskin Division of Orthopaedics, Mount Sinai Hospital, Toronto, Ontario, Canada
- Division of Orthopaedic Surgery, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Bheeshma Ravi
- Division of Orthopaedic Surgery, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Division of Orthopaedic Surgery, Holland Bone and Joint Program, Sunnybrook Health Science Centre, Toronto, Ontario, Canada
| | - Cari Whyne
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
- Orthopaedic Biomechanics Lab, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Division of Orthopaedic Surgery, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Division of Orthopaedic Surgery, Holland Bone and Joint Program, Sunnybrook Health Science Centre, Toronto, Ontario, Canada
| | - Johnathan R. Lex
- Orthopaedic Biomechanics Lab, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Division of Orthopaedic Surgery, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| |
Collapse
|
25
|
How to Create an Orthopaedic Arthroplasty Database Project: A Step-by-Step Guide Part II: Study Execution. J Arthroplasty 2023; 38:414-418. [PMID: 36243277 DOI: 10.1016/j.arth.2022.10.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 09/28/2022] [Accepted: 10/01/2022] [Indexed: 12/14/2022] Open
Abstract
In recent years, the use of national databases in orthopaedic surgery research has grown substantially with database studies comprising an estimated ∼10% of all published lower extremity arthroplasty research. The aim of this review is to serve as a guide on how to: (1) design; (2) execute; and (3) publish an orthopaedic database arthroplasty project. In part II, we discuss how to collect data, propose a novel checklist/standards for presenting orthopaedic database information (SOPOD), discuss methods for appropriate data interpretation/analysis, and summarize how to convert findings to a manuscript (providing a previously published example study). Data collection can be divided into two stages: baseline patient demographics and primary/secondary outcomes of interest. Our proposed SOPOD is more orthopaedic-centered and builds upon previous standards for observational studies from the EQUATOR network. There are a host of statistical methods available to analyze data to compare baseline demographics, primary/secondary outcomes, and reduce type 1 errors seen in large datasets. When drafting a manuscript, it is important to consider and discuss the limitations of database studies, including their retrospective nature, issues with coding/billing, differences in statistical versus clinical significance (or relevance), lack of surgery details (approach, laterality, and implants), and limited sampling or follow-up. We hope this paper will serve as a starting point for those interested in conducting lower extremity arthroplasty database studies.
Collapse
|
26
|
Patel I, Nham F, Zalikha AK, El-Othmani MM. Epidemiology of total hip arthroplasty: demographics, comorbidities and outcomes. ARTHROPLASTY (LONDON, ENGLAND) 2023; 5:2. [PMID: 36593482 PMCID: PMC9808997 DOI: 10.1186/s42836-022-00156-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 11/22/2022] [Indexed: 01/04/2023]
Abstract
BACKGROUND Primary THA (THA) is a successful procedure for end-stage hip osteoarthritis. In the setting of a failed THA, revision total hip arthroplasty (rTHA) acts as a salvage procedure. This procedure has increased risks, including sepsis, infection, prolonged surgery time, blood loss, and increased length of stay. Increasing focus on understanding of demographics, comorbidities, and inpatient outcomes can lead to better perioperative optimization and post-operative outcomes. This epidemiological registry study aimed to compare the demographics, comorbidity profiles, and outcomes of patients undergoing THA and rTHA. METHODS A retrospective review of discharge data reported from 2006 to the third quarter of 2015 using the National Inpatient Sample registry was performed. The study included adult patients aged 40 and older who underwent either THA or rTHA. A total of 2,838,742 THA patients and 400,974 rTHA patients were identified. RESULTS The primary reimbursement for both THA and rTHA was dispensed by Medicare at 53.51% and 65.36% of cases respectively. Complications arose in 27.32% of THA and 39.46% of rTHA cases. Postoperative anemia was the most common complication in groups (25.20% and 35.69%). Common comorbidities in both groups were hypertension and chronic pulmonary disease. rTHA indications included dislocation/instability (21.85%) followed by mechanical loosening (19.74%), other mechanical complications (17.38%), and infection (15.10%). CONCLUSION Our data demonstrated a 69.50% increase in patients receiving THA and a 28.50% increase in rTHA from the years 2006 to 2014. The data demonstrated 27.32% and 39.46% complication rate with THA and rTHA, with postoperative anemia as the most common cause. Common comorbidities were hypertension and chronic pulmonary disease. Future analyses into preoperative optimizations, such as prior consultation with medical specialists or improved primary hip protocol, should be considered to prevent/reduce postoperative complications amongst a progressive expansion in patients receiving both THA and rTHA.
Collapse
Affiliation(s)
- Ishan Patel
- grid.413184.b0000 0001 0088 6903DMC Orthopaedics & Sports Medicine, 3990 John R Street, Detroit, MI 48201 USA
| | - Fong Nham
- grid.413184.b0000 0001 0088 6903DMC Orthopaedics & Sports Medicine, 3990 John R Street, Detroit, MI 48201 USA
| | - Abdul K. Zalikha
- grid.413184.b0000 0001 0088 6903DMC Orthopaedics & Sports Medicine, 3990 John R Street, Detroit, MI 48201 USA
| | - Mouhanad M. El-Othmani
- grid.239585.00000 0001 2285 2675Department of Orthopaedic Surgery, Columbia University Medical Center, 622 W 168th Street, New York, NY 10032 USA
| |
Collapse
|
27
|
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.
Collapse
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
| |
Collapse
|
28
|
Hui AT, Alvandi LM, Eleswarapu AS, Fornari ED. Artificial Intelligence in Modern Orthopaedics: Current and Future Applications. JBJS Rev 2022; 10:01874474-202210000-00003. [PMID: 36191085 DOI: 10.2106/jbjs.rvw.22.00086] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
➢ With increasing computing power, artificial intelligence (AI) has gained traction in all aspects of health care delivery. Orthopaedics is no exception because the influence of AI technology has become intricately linked with its advancement as evidenced by increasing interest and research. ➢ This review is written for the orthopaedic surgeon to develop a better understanding of the main clinical applications and potential benefits of AI within their day-to-day practice. ➢ A brief and easy-to-understand foundation for what AI is and the different terminology used within the literature is first provided, followed by a summary of the newest research on AI applications demonstrating increased accuracy and convenience in risk stratification, clinical decision-making support, and robotically assisted surgery.
Collapse
Affiliation(s)
- Aaron T Hui
- Albert Einstein College of Medicine, Bronx, New York
- Department of Orthopaedic Surgery, Montefiore Medical Center, Bronx, New York
| | - Leila M Alvandi
- Albert Einstein College of Medicine, Bronx, New York
- Department of Orthopaedic Surgery, Montefiore Medical Center, Bronx, New York
| | - Ananth S Eleswarapu
- Albert Einstein College of Medicine, Bronx, New York
- Department of Orthopaedic Surgery, Montefiore Medical Center, Bronx, New York
| | - Eric D Fornari
- Albert Einstein College of Medicine, Bronx, New York
- Department of Orthopaedic Surgery, Montefiore Medical Center, Bronx, New York
| |
Collapse
|
29
|
Ramkumar PN, Pang M, Polisetty T, Helm JM, Karnuta JM. Meaningless Applications and Misguided Methodologies in Artificial Intelligence-Related Orthopaedic Research Propagates Hype Over Hope. Arthroscopy 2022; 38:2761-2766. [PMID: 35550419 DOI: 10.1016/j.arthro.2022.04.014] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 04/07/2022] [Accepted: 04/12/2022] [Indexed: 02/02/2023]
Abstract
There exists great hope and hype in the literature surrounding applications of artificial intelligence (AI) to orthopaedic surgery. Between 2018 and 2021, a total of 178 AI-related articles were published in orthopaedics. However, for every 2 original research papers that apply AI to orthopaedics, a commentary or review is published (30.3%). AI-related research in orthopaedics frequently fails to provide use cases that offer the uninitiated an opportunity to appraise the importance of AI by studying meaningful questions, evaluating unknown hypotheses, or analyzing quality data. The hype perpetuates a feed-forward cycle that relegates AI to a meaningless buzzword by rewarding those with nascent understanding and rudimentary technical knowhow into committing several basic errors: (1) inappropriately conflating vernacular ("AI/machine learning"), (2) repackaging registry data, (3) prematurely releasing internally validated algorithms, (4) overstating the "black box phenomenon" by failing to provide weighted analysis, (5) claiming to evaluate AI rather than the data itself, and (6) withholding full model architecture code. Relevant AI-specific guidelines are forthcoming, but forced application of the original Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guidelines designed for regression analyses is irrelevant and misleading. To safeguard meaningful use, AI-related research efforts in orthopaedics should be (1) directed toward administrative support over clinical evaluation and management, (2) require the use of the advanced model, and (3) answer a question that was previously unknown, unanswered, or unquantifiable.
Collapse
Affiliation(s)
- Prem N Ramkumar
- Orthopaedic Machine Learning Laboratory, Brigham & Women's Hospital, Boston, Massachusetts, U.S.A; Sports Medicine Service, Hospital for Special Surgery, New York, New York, U.S.A; Department of Orthopaedic Surgery, UTHealth McGovern Medical School, Houston, Texas, U.S.A.
| | - Michael Pang
- Orthopaedic Machine Learning Laboratory, Brigham & Women's Hospital, Boston, Massachusetts, U.S.A
| | - Teja Polisetty
- Orthopaedic Machine Learning Laboratory, Brigham & Women's Hospital, Boston, Massachusetts, U.S.A
| | - J Matthew Helm
- Orthopaedic Machine Learning Laboratory, Brigham & Women's Hospital, Boston, Massachusetts, U.S.A
| | - Jaret M Karnuta
- Orthopaedic Machine Learning Laboratory, Brigham & Women's Hospital, Boston, Massachusetts, U.S.A; Department of Orthopaedic Surgery, University of Pennsylvania, Philadelphia, Pennsylvania, U.S.A
| |
Collapse
|
30
|
Yeo I, Klemt C, Melnic CM, Pattavina MH, De Oliveira BMC, Kwon YM. Predicting surgical operative time in primary total knee arthroplasty utilizing machine learning models. Arch Orthop Trauma Surg 2022; 143:3299-3307. [PMID: 35994094 DOI: 10.1007/s00402-022-04588-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 08/10/2022] [Indexed: 11/24/2022]
Abstract
BACKGROUND Prolonged surgical operative time is associated with postoperative adverse outcomes following total knee arthroplasty (TKA). Increasing operating room efficiency necessitates the accurate prediction of surgical operative time for each patient. One potential way to increase the accuracy of predictions is to use advanced predictive analytics, such as machine learning. The aim of this study is to use machine learning to develop an accurate predictive model for surgical operative time for patients undergoing primary total knee arthroplasty. METHODS A retrospective chart review of electronic medical records was conducted to identify patients who underwent primary total knee arthroplasty at a tertiary referral center. Three machine learning algorithms were developed to predict surgical operative time and were assessed by discrimination, calibration and decision curve analysis. Specifically, we used: (1) Artificial Neural Networks (ANNs), (2) Random Forest (RF), and (3) K-Nearest Neighbor (KNN). RESULTS We analyzed the surgical operative time for 10,021 consecutive patients who underwent primary total knee arthroplasty. The neural network model achieved the best performance across discrimination (AUC = 0.82), calibration and decision curve analysis for predicting surgical operative time. Based on this algorithm, younger age (< 45 years), tranexamic acid non-usage, and a high BMI (> 40 kg/m2) were the strongest predictors associated with surgical operative time. CONCLUSIONS This study shows excellent performance of machine learning models for predicting surgical operative time in primary total knee arthroplasty. The accurate estimation of surgical duration is important in enhancing OR efficiency and identifying patients at risk for prolonged surgical operative time. LEVEL OF EVIDENCE Level III, case control retrospective analysis.
Collapse
Affiliation(s)
- Ingwon Yeo
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
| | - Christian Klemt
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
| | - Christopher M Melnic
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
| | - Meghan H Pattavina
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
| | - Bruna M Castro De Oliveira
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
| | - Young-Min Kwon
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA.
| |
Collapse
|
31
|
Klemt C, Uzosike AC, Harvey MJ, Laurencin S, Habibi Y, Kwon YM. Neural network models accurately predict discharge disposition after revision total knee arthroplasty? Knee Surg Sports Traumatol Arthrosc 2022; 30:2591-2599. [PMID: 34716766 DOI: 10.1007/s00167-021-06778-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 10/15/2021] [Indexed: 01/18/2023]
Abstract
PURPOSE Based on the rising incidence of revision total knee arthroplasty (TKA), bundled payment models may be applied to revision TKA in the near future. Facility discharge represents a significant cost factor for those bundled payment models; however, accurately predicting discharge disposition remains a clinical challenge. The purpose of this study was to develop and validate artificial intelligence algorithms to predict discharge disposition following revision total knee arthroplasty. METHODS A retrospective review of electronic patient records was conducted to identify patients who underwent revision total knee arthroplasty. Discharge disposition was defined as either home discharge or non-home discharge, which included rehabilitation and skilled nursing facilities. Four artificial intelligence algorithms were developed to predict this outcome and were assessed by discrimination, calibration and decision curve analysis. RESULTS A total of 2228 patients underwent revision TKA, of which 1405 patients (63.1%) were discharged home, whereas 823 patients (36.9%) were discharged to a non-home facility. The strongest predictors for non-home discharge following revision TKA were American Society of Anesthesiologist (ASA) score, Medicare insurance type and revision surgery for peri-prosthetic joint infection, non-white ethnicity and social status (living alone). The best performing artificial intelligence algorithm was the neural network model which achieved excellent performance across discrimination (AUC = 0.87), calibration and decision curve analysis. CONCLUSION This study developed four artificial intelligence algorithms for the prediction of non-home discharge disposition for patients following revision total knee arthroplasty. The study findings show excellent performance on discrimination, calibration and decision curve analysis for all four candidate algorithms. Therefore, these models have the potential to guide preoperative patient counselling and improve the value (clinical and functional outcomes divided by costs) of revision total knee arthroplasty patients. LEVEL OF EVIDENCE IV.
Collapse
Affiliation(s)
- Christian Klemt
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
| | - Akachimere Cosmas Uzosike
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
| | - Michael Joseph Harvey
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
| | - Samuel Laurencin
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
| | - Yasamin Habibi
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
| | - Young-Min Kwon
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA.
| |
Collapse
|
32
|
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: 8] [Impact Index Per Article: 4.0] [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.
Collapse
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
| |
Collapse
|
33
|
Fully automated deep learning for knee alignment assessment in lower extremity radiographs: a cross-sectional diagnostic study. Skeletal Radiol 2022; 51:1249-1259. [PMID: 34773485 DOI: 10.1007/s00256-021-03948-9] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 10/27/2021] [Accepted: 10/27/2021] [Indexed: 02/02/2023]
Abstract
OBJECTIVES Accurate assessment of knee alignment and leg length discrepancy is currently measured manually from standing long-leg radiographs (LLR), a process that is both time consuming and poorly reproducible. The aim was to assess the performance of a commercial available AI software by comparing its outputs with manually performed measurements. MATERIALS AND METHODS The AI was trained on over 15,000 radiographs to measure various clinical angles and lengths from LLRs. We performed a retrospective single-center analysis on 295 LLRs obtained between 2015 and 2020 from male and female patients over 18 years. AI and expert measurements were performed independently. Kellgren-Lawrence score and reading time were assessed. All measurements were compared and non-inferiority, mean-absolute-deviation (sMAD), and intra-class-correlation (ICC) were calculated. RESULTS A total of 295 LLRs from 284 patients (mean age, 65 years (18; 90); 97 (34.2%) men) were analyzed. The AI model produces outputs on 98.0% of the LLRs. Manually annotations were considered as 100% accurate. For each measurement, its divergence was calculated, resulting in an overall accuracy of 89.2% when comparing the AI outputs to the manually measured. AI vs. mean observer revealed an sMAD between 0.39 and 2.19° for angles and 1.45-5.00 mm for lengths. AI showed good reliability in all lengths and angles (ICC ≥ 0.87). Non-inferiority comparing AI to the mean observer revealed an equivalence-index (γ) between 0.54 and 3.03° for angles and - 0.70-1.95 mm for lengths. On average, AI was 130 s faster than clinicians. CONCLUSION Automated measurements of knee alignment and length measurements produced with an AI tool result in reproducible, accurate measures with a time savings compared to manually acquired measurements.
Collapse
|
34
|
Polce EM, Kunze KN, Dooley MS, Piuzzi NS, Boettner F, Sculco PK. Efficacy and Applications of Artificial Intelligence and Machine Learning Analyses in Total Joint Arthroplasty: A Call for Improved Reporting. J Bone Joint Surg Am 2022; 104:821-832. [PMID: 35045061 DOI: 10.2106/jbjs.21.00717] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
BACKGROUND There has been a considerable increase in total joint arthroplasty (TJA) research using machine learning (ML). Therefore, the purposes of this study were to synthesize the applications and efficacies of ML reported in the TJA literature, and to assess the methodological quality of these studies. METHODS PubMed, OVID/MEDLINE, and Cochrane libraries were queried in January 2021 for articles regarding the use of ML in TJA. Study demographics, topic, primary and secondary outcomes, ML model development and testing, and model presentation and validation were recorded. The TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) guidelines were used to assess the methodological quality. RESULTS Fifty-five studies were identified: 31 investigated clinical outcomes and resource utilization; 11, activity and motion surveillance; 10, imaging detection; and 3, natural language processing. For studies reporting the area under the receiver operating characteristic curve (AUC), the median AUC (and range) was 0.80 (0.60 to 0.97) among 26 clinical outcome studies, 0.99 (0.83 to 1.00) among 6 imaging-based studies, and 0.88 (0.76 to 0.98) among 3 activity and motion surveillance studies. Twelve studies compared ML to logistic regression, with 9 (75%) reporting that ML was superior. The average number of TRIPOD guidelines met was 11.5 (range: 5 to 18), with 38 (69%) meeting greater than half of the criteria. Presentation and explanation of the full model for individual predictions and assessments of model calibration were poorly reported (<30%). CONCLUSIONS The performance of ML models was good to excellent when applied to a wide variety of clinically relevant outcomes in TJA. However, reporting of certain key methodological and model presentation criteria was inadequate. Despite the recent surge in TJA literature utilizing ML, the lack of consistent adherence to reporting guidelines needs to be addressed to bridge the gap between model development and clinical implementation.
Collapse
Affiliation(s)
- Evan M Polce
- University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Kyle N Kunze
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY
| | - Matthew S Dooley
- University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Nicolas S Piuzzi
- Department of Orthopaedic Surgery, Cleveland Clinic, Cleveland, Ohio
| | - Friedrich Boettner
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY
| | - Peter K Sculco
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY
| |
Collapse
|
35
|
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.
Collapse
|
36
|
The Ability of Deep Learning Models to Identify Total Hip and Knee Arthroplasty Implant Design From Plain Radiographs. J Am Acad Orthop Surg 2022; 30:409-415. [PMID: 35139038 DOI: 10.5435/jaaos-d-21-00771] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 01/05/2022] [Indexed: 02/01/2023] Open
Abstract
INTRODUCTION The surgical management of patients with failed total hip or knee arthroplasty (THA and TKA) necessitates the identification of the implant manufacturer and model. Failure to accurately identify implant design leads to delays in care, increased morbidity, and healthcare costs. The automated identification of implant designs has the potential to assist in the surgical management of patients with failed arthroplasty. This study aimed to develop and validate a convolutional neural network deep learning model for the identification of primary and revision hip and knee total joint arthroplasty designs from plain radiographs. METHODS This study trained a convolutional neural network deep learning model to automatically identify 24 THA designs and 14 TKA designs from 11,204 anterior-posterior radiographs obtained from 8,763 patients. From these radiographs, 8,963 radiographs (80%) were used for model training and 2,241 radiographs (20%) were used for model validation. Model performance was assessed through receiver operating curve characteristics. RESULTS After 1,000 training epochs by the convolutional neural network deep learning model, the computational model discriminated 17 primary THA designs with an area under the receiver operating curve (AUC) of 0.98, sensitivity of 95.8%, and specificity of 98.6%. The deep learning model discriminated eight primary TKA designs with an AUC of 0.97, sensitivity of 94.9%, and specificity of 97.8%. The deep learning model demonstrated an AUC of 0.98 and 0.96 for the identification of seven revision THA and six revision TKA designs, respectively. DISCUSSION This study developed and validated a convolutional neural network deep learning model for the identification of hip and knee total joint arthroplasty designs from plain radiographs. The study findings demonstrate excellent accuracy of the deep learning model for the identification of 24 THA and 14 TKA designs, illustrating the great potential of the deep learning model to assist in preoperative surgical planning of failed arthroplasty patients.
Collapse
|
37
|
Zalikha AK, El-Othmani MM, Shah RP. Predictive capacity of four machine learning models for in-hospital postoperative outcomes following total knee arthroplasty. J Orthop 2022; 31:22-28. [PMID: 35345622 PMCID: PMC8956845 DOI: 10.1016/j.jor.2022.03.006] [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] [Received: 12/13/2021] [Revised: 02/13/2022] [Accepted: 03/17/2022] [Indexed: 11/26/2022] Open
Abstract
Background Machine learning (ML) methods have shown promise in the development of patient-specific predictive models prior to surgical interventions. The purpose of this study was to develop, test, and compare four distinct ML models to predict postoperative parameters following primary total knee arthroplasty (TKA). Methods Data from the Nationwide Inpatient Sample was used to identify patients undergoing TKA during 2016-2017. Four distinct ML models predictive of mortality, length of stay (LOS), and discharge disposition were developed and validated using 15 predictive patient and hospital-specific factors. Area under the curve of the receiver operating characteristic curve (AUCROC) and accuracy were used as validity metrics, and the strongest predictive variables under each model were assessed. Results A total of 305,577 patients were included. For mortality, the XGBoost, neural network (NN), and LSVM models all had excellent responsiveness during validation, while random forest (RF) had fair responsiveness. For predicting LOS, all four models had poor responsiveness. For the discharge disposition outcome, the LSVM, NN, and XGBoost models had good responsiveness, while the RF model had poor responsiveness. LSVM and XGBoost had the highest responsiveness for predicting discharge disposition with an AUCROC of 0.747. Discussion The ML models tested demonstrated a range of poor to excellent responsiveness and accuracy in the prediction of the assessed metrics, with considerable variability noted in the predictive precision between the models. The continued development of ML models should be encouraged, with eventual integration into clinical practice in order to inform patient discussions, management decision making, and health policy.
Collapse
|
38
|
Ricciardi C, Ponsiglione AM, Scala A, Borrelli A, Misasi M, Romano G, Russo G, Triassi M, Improta G. Machine Learning and Regression Analysis to Model the Length of Hospital Stay in Patients with Femur Fracture. Bioengineering (Basel) 2022; 9:bioengineering9040172. [PMID: 35447732 PMCID: PMC9029792 DOI: 10.3390/bioengineering9040172] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 04/08/2022] [Accepted: 04/11/2022] [Indexed: 12/27/2022] Open
Abstract
Fractures of the femur are a frequent problem in elderly people, and it has been demonstrated that treating them with a diagnostic–therapeutic–assistance path within 48 h of admission to the hospital reduces complications and shortens the length of the hospital stay (LOS). In this paper, the preoperative data of 1082 patients were used to further extend the previous research and to generate several models that are capable of predicting the overall LOS: First, the LOS, measured in days, was predicted through a regression analysis; then, it was grouped by weeks and was predicted with a classification analysis. The KNIME analytics platform was applied to divide the dataset for a hold-out cross-validation, perform a multiple linear regression and implement machine learning algorithms. The best coefficient of determination (R2) was achieved by the support vector machine (R2 = 0.617), while the mean absolute error was similar for all the algorithms, ranging between 2.00 and 2.11 days. With regard to the classification analysis, all the algorithms surpassed 80% accuracy, and the most accurate algorithm was the radial basis function network, at 83.5%. The use of these techniques could be a valuable support tool for doctors to better manage orthopaedic departments and all their resources, which would reduce both waste and costs in the context of healthcare.
Collapse
Affiliation(s)
- Carlo Ricciardi
- Department of Electrical Engineering and Information Technology, University of Naples “Federico II”, 80125 Naples, Italy;
| | - Alfonso Maria Ponsiglione
- Department of Electrical Engineering and Information Technology, University of Naples “Federico II”, 80125 Naples, Italy;
- Correspondence:
| | - Arianna Scala
- Department of Public Health, University Hospital of Naples “Federico II”, 80131 Naples, Italy; (A.S.); (M.T.); (G.I.)
| | - Anna Borrelli
- Health Department, University Hospital of Salerno “San Giovanni di Dio e Ruggi d′Aragona”, 84126 Salerno, Italy;
| | - Mario Misasi
- Department of the Orthopaedics, National Hospital (A.O.R.N.) Antonio Cardarelli, 80131 Naples, Italy; (M.M.); (G.R.)
| | - Gaetano Romano
- Department of the Orthopaedics, National Hospital (A.O.R.N.) Antonio Cardarelli, 80131 Naples, Italy; (M.M.); (G.R.)
| | - Giuseppe Russo
- National Hospital (A.O.R.N.) Antonio Cardarelli, 80131 Naples, Italy;
| | - Maria Triassi
- Department of Public Health, University Hospital of Naples “Federico II”, 80131 Naples, Italy; (A.S.); (M.T.); (G.I.)
- Interdepartmental Center for Research in Healthcare, Management and Innovation in Healthcare (CIRMIS), University of Study of Naples “Federico II”, 80131 Naples, Italy
| | - Giovanni Improta
- Department of Public Health, University Hospital of Naples “Federico II”, 80131 Naples, Italy; (A.S.); (M.T.); (G.I.)
- Interdepartmental Center for Research in Healthcare, Management and Innovation in Healthcare (CIRMIS), University of Study of Naples “Federico II”, 80131 Naples, Italy
| |
Collapse
|
39
|
St Mart JP, Goh EL, Liew I, Shah Z, Sinha J. Artificial intelligence in orthopaedic surgery: transforming technological innovation in patient care and surgical training. Postgrad Med J 2022:postgradmedj-2022-141596. [PMID: 35379754 DOI: 10.1136/postgradmedj-2022-141596] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 03/19/2022] [Indexed: 12/16/2022]
Abstract
Artificial intelligence (AI) is an exciting field combining computer science with robust data sets to facilitate problem-solving. It has the potential to transform education, practice and delivery of healthcare especially in orthopaedics. This review article outlines some of the already used AI pathways as well as recent technological advances in orthopaedics. Additionally, this article further explains how potentially these two entities could be combined in the future to improve surgical education, training and ultimately patient care and outcomes.
Collapse
Affiliation(s)
- Jean-Pierre St Mart
- Trauma and Orthopaedics, North West Anglia NHS Foundation Trust, Peterborough, UK
| | - En Lin Goh
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Oxford Trauma, Kadoorie Centre, University of Oxford, Oxford, UK
| | - Ignatius Liew
- Trauma and Orthopaedics, North West Anglia NHS Foundation Trust, Peterborough, UK
| | - Zameer Shah
- Trauma and Orthopaedic Surgery, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Joydeep Sinha
- Trauma and Orthopaedic Surgery, King's College Hospital NHS Foundation Trust, London, UK
| |
Collapse
|
40
|
Lee LS, Chan PK, Wen C, Fung WC, Cheung A, Chan VWK, Cheung MH, Fu H, Yan CH, Chiu KY. Artificial intelligence in diagnosis of knee osteoarthritis and prediction of arthroplasty outcomes: a review. ARTHROPLASTY 2022; 4:16. [PMID: 35246270 PMCID: PMC8897859 DOI: 10.1186/s42836-022-00118-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 02/17/2022] [Indexed: 11/15/2022] Open
Abstract
Background Artificial intelligence is an emerging technology with rapid growth and increasing applications in orthopaedics. This study aimed to summarize the existing evidence and recent developments of artificial intelligence in diagnosing knee osteoarthritis and predicting outcomes of total knee arthroplasty. Methods PubMed and EMBASE databases were searched for articles published in peer-reviewed journals between January 1, 2010 and May 31, 2021. The terms included: ‘artificial intelligence’, ‘machine learning’, ‘knee’, ‘osteoarthritis’, and ‘arthroplasty’. We selected studies focusing on the use of AI in diagnosis of knee osteoarthritis, prediction of the need for total knee arthroplasty, and prediction of outcomes of total knee arthroplasty. Non-English language articles and articles with no English translation were excluded. A reviewer screened the articles for the relevance to the research questions and strength of evidence. Results Machine learning models demonstrated promising results for automatic grading of knee radiographs and predicting the need for total knee arthroplasty. The artificial intelligence algorithms could predict postoperative outcomes regarding patient-reported outcome measures, patient satisfaction and short-term complications. Important weaknesses of current artificial intelligence algorithms included the lack of external validation, the limitations of inherent biases in clinical data, the requirement of large datasets in training, and significant research gaps in the literature. Conclusions Artificial intelligence offers a promising solution to improve detection and management of knee osteoarthritis. Further research to overcome the weaknesses of machine learning models may enhance reliability and allow for future use in routine healthcare settings.
Collapse
Affiliation(s)
- Lok Sze Lee
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong, China
| | - Ping Keung Chan
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong, China.
| | - Chunyi Wen
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, China
| | - Wing Chiu Fung
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong, China
| | - Amy Cheung
- Department of Orthopaedics and Traumatology, Queen Mary Hospital, Hong Kong, China
| | | | - Man Hong Cheung
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong, China
| | - Henry Fu
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong, China
| | - Chun Hoi Yan
- Department of Orthopaedics and Traumatology, Gleneagles Hospital Hong Kong, Hong Kong, China
| | - Kwong Yuen Chiu
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong, China
| |
Collapse
|
41
|
Kurmis AP, Ianunzio JR. Artificial intelligence in orthopedic surgery: evolution, current state and future directions. ARTHROPLASTY 2022; 4:9. [PMID: 35232490 PMCID: PMC8889658 DOI: 10.1186/s42836-022-00112-z] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 12/31/2021] [Indexed: 12/14/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Andrew P Kurmis
- Discipline of Medical Specialties, University of Adelaide, Adelaide, SA, Australia. .,Department of Orthopaedic Surgery, Lyell McEwin Hospital, Vale, Elizabeth, SA, Australia.
| | - Jamie R Ianunzio
- Department of Orthopaedic Surgery, Lyell McEwin Hospital, Vale, Elizabeth, SA, Australia.,School of Medicine, University of Adelaide, Adelaide, SA, Australia
| |
Collapse
|
42
|
Ramkumar PN, Luu BC, Haeberle HS, Karnuta JM, Nwachukwu BU, Williams RJ. Sports Medicine and Artificial Intelligence: A Primer. Am J Sports Med 2022; 50:1166-1174. [PMID: 33900125 DOI: 10.1177/03635465211008648] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Artificial intelligence (AI) represents the fourth industrial revolution and the next frontier in medicine poised to transform the field of orthopaedics and sports medicine, though widespread understanding of the fundamental principles and adoption of applications remain nascent. Recent research efforts into implementation of AI in the field of orthopaedic surgery and sports medicine have demonstrated great promise in predicting athlete injury risk, interpreting advanced imaging, evaluating patient-reported outcomes, reporting value-based metrics, and augmenting the patient experience. Not unlike the recent emphasis thrust upon physicians to understand the business of medicine, the future practice of sports medicine specialists will require a fundamental working knowledge of the strengths, limitations, and applications of AI-based tools. With appreciation, caution, and experience applying AI to sports medicine, the potential to automate tasks and improve data-driven insights may be realized to fundamentally improve patient care. In this Current Concepts review, we discuss the definitions, strengths, limitations, and applications of AI from the current literature as it relates to orthopaedic sports medicine.
Collapse
Affiliation(s)
- Prem N Ramkumar
- Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, Ohio, USA
- Department of Orthopaedic Surgery, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Bryan C Luu
- Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, Ohio, USA
- Department of Orthopaedic Surgery, Baylor College of Medicine, Houston, Texas, USA
| | - Heather S Haeberle
- Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, Ohio, USA
- Department of Orthopedic Surgery, Hospital for Special Surgery, New York, New York, USA
| | - Jaret M Karnuta
- Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, Ohio, USA
| | - Benedict U Nwachukwu
- Department of Orthopedic Surgery, Hospital for Special Surgery, New York, New York, USA
| | - Riley J Williams
- Department of Orthopedic Surgery, Hospital for Special Surgery, New York, New York, USA
| |
Collapse
|
43
|
Hinterwimmer F, Lazic I, Suren C, Hirschmann MT, Pohlig F, Rueckert D, Burgkart R, von Eisenhart-Rothe R. Machine learning in knee arthroplasty: specific data are key-a systematic review. Knee Surg Sports Traumatol Arthrosc 2022; 30:376-388. [PMID: 35006281 PMCID: PMC8866371 DOI: 10.1007/s00167-021-06848-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 12/16/2021] [Indexed: 12/16/2022]
Abstract
PURPOSE Artificial intelligence (AI) in healthcare is rapidly growing and offers novel options of data analysis. Machine learning (ML) represents a distinct application of AI, which is capable of generating predictions and has already been tested in different medical specialties with various approaches such as diagnostic applications, cost predictions or identification of risk factors. In orthopaedics, this technology has only recently been introduced and the literature on ML in knee arthroplasty is scarce. In this review, we aim to investigate which predictions are already feasible using ML models in knee arthroplasty to identify prerequisites for the effective use of this novel approach. For this reason, we conducted a systematic review of ML algorithms for outcome prediction in knee arthroplasty. METHODS A comprehensive search of PubMed, Medline database and the Cochrane Library was conducted to find ML applications for knee arthroplasty. All relevant articles were systematically retrieved and evaluated by an orthopaedic surgeon and a data scientist on the basis of the PRISMA statement. The search strategy yielded 225 articles of which 19 were finally assessed as eligible. A modified Coleman Methodology Score (mCMS) was applied to account for a methodological evaluation. RESULTS The studies presented in this review demonstrated fair to good results (AUC median 0.76/range 0.57-0.98), while heterogeneous prediction models were analysed: complications (6), costs (4), functional outcome (3), revision (2), postoperative satisfaction (2), surgical technique (1) and biomechanical properties (1) were investigated. The median mCMS was 65 (range 40-80) points. CONCLUSION The prediction of distinct outcomes with ML models applying specific data is already feasible; however, the prediction of more complex outcomes is still inaccurate. Registry data on knee arthroplasty have not been fully analysed yet so that specific parameters have not been sufficiently evaluated. The inclusion of specific input data as well as the collaboration of orthopaedic surgeons and data scientists are essential prerequisites to fully utilize the capacity of ML in knee arthroplasty. Future studies should investigate prospective data with specific and longitudinally recorded parameters. LEVEL OF EVIDENCE III.
Collapse
Affiliation(s)
- Florian Hinterwimmer
- Department of Orthopaedics and Sports Orthopaedics, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675, München, Germany. .,Institute for AI and Informatics in Medicine, Technical University of Munich, Munich, Germany.
| | - Igor Lazic
- Department of Orthopaedics and Sports Orthopaedics, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675, München, Germany.
| | - Christian Suren
- Department of Orthopaedics and Sports Orthopaedics, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 München, Germany
| | - Michael T. Hirschmann
- Department of Orthopaedic Surgery and Traumatology-Liestal, Kantonsspital Baselland, Bruderholz, Laufen, Switzerland
| | - Florian Pohlig
- Department of Orthopaedics and Sports Orthopaedics, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 München, Germany
| | - Daniel Rueckert
- Institute for AI and Informatics in Medicine, Technical University of Munich, Munich, Germany
| | - Rainer Burgkart
- Department of Orthopaedics and Sports Orthopaedics, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 München, Germany
| | - Rüdiger von Eisenhart-Rothe
- Department of Orthopaedics and Sports Orthopaedics, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 München, Germany
| |
Collapse
|
44
|
Lopez CD, Ding J, Trofa DP, Cooper HJ, Geller JA, Hickernell TR. Machine Learning Model Developed to Aid in Patient Selection for Outpatient Total Joint Arthroplasty. Arthroplast Today 2021; 13:13-23. [PMID: 34917716 PMCID: PMC8666332 DOI: 10.1016/j.artd.2021.11.001] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 10/12/2021] [Accepted: 11/03/2021] [Indexed: 12/13/2022] Open
Abstract
Background Patient selection for outpatient total joint arthroplasty (TJA) is important for optimizing patient outcomes. This study develops machine learning models that may aid in patient selection for outpatient TJA based on medical comorbidities and demographic factors. Methods This study queried elective total knee arthroplasty (TKA) and total hip arthroplasty (THA) cases during 2010-2018 in the American College of Surgeons National Surgical Quality Improvement Program. Artificial neural network models predicted same-day discharge and length of stay (LOS) fewer than 2 days (short LOS). Multiple linear and logistic regression analyses were used to identify variables significantly associated with predicted outcomes. Results A total of 284,731 TKA cases and 153,053 THA cases met inclusion criteria. For TKA, prediction of short LOS had an area under the receiver operating characteristic curve (AUC) of 0.767 and accuracy of 84.1%; prediction of same-day discharge had an AUC of 0.802 and accuracy of 89.2%. For THA, prediction of short LOS had an AUC of 0.757 and accuracy of 70.6%; prediction of same-day discharge had an AUC of 0.814 and accuracy of 78.8%. Conclusion This study developed machine learning models for aiding patient selection for outpatient TJA, through accurately predicting short LOS or outpatient vs inpatient cases. As outpatient TJA expands, it will be important to optimize preoperative patient selection and effectively screen surgical candidates from a broader patient population. Incorporating models such as these into electronic medical records could aid in decision-making and resource planning in real time.
Collapse
Affiliation(s)
- Cesar D Lopez
- New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Jessica Ding
- New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - David P Trofa
- New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - H John Cooper
- New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Jeffrey A Geller
- New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Thomas R Hickernell
- New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| |
Collapse
|
45
|
Federer SJ, Jones GG. Artificial intelligence in orthopaedics: A scoping review. PLoS One 2021; 16:e0260471. [PMID: 34813611 PMCID: PMC8610245 DOI: 10.1371/journal.pone.0260471] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 11/11/2021] [Indexed: 11/19/2022] Open
Abstract
There is a growing interest in the application of artificial intelligence (AI) to orthopaedic surgery. This review aims to identify and characterise research in this field, in order to understand the extent, range and nature of this work, and act as springboard to stimulate future studies. A scoping review, a form of structured evidence synthesis, was conducted to summarise the use of AI in orthopaedics. A literature search (1946-2019) identified 222 studies eligible for inclusion. These studies were predominantly small and retrospective. There has been significant growth in the number of papers published in the last three years, mainly from the USA (37%). The majority of research used AI for image interpretation (45%) or as a clinical decision tool (25%). Spine (43%), knee (23%) and hip (14%) were the regions of the body most commonly studied. The application of artificial intelligence to orthopaedics is growing. However, the scope of its use so far remains limited, both in terms of its possible clinical applications, and the sub-specialty areas of the body which have been studied. A standardized method of reporting AI studies would allow direct assessment and comparison. Prospective studies are required to validate AI tools for clinical use.
Collapse
Affiliation(s)
- Simon J. Federer
- MSk Lab, Sir Michael Uren Hub, Imperial College London, London, United Kingdom
- * E-mail:
| | - Gareth G. Jones
- MSk Lab, Sir Michael Uren Hub, Imperial College London, London, United Kingdom
| |
Collapse
|
46
|
Bishop JA, Javed HA, el-Bouri R, Zhu T, Taylor T, Peto T, Watkinson P, Eyre DW, Clifton DA. Improving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge. PLoS One 2021; 16:e0260476. [PMID: 34813632 PMCID: PMC8610279 DOI: 10.1371/journal.pone.0260476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 11/10/2021] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Delays in patient flow and a shortage of hospital beds are commonplace in hospitals during periods of increased infection incidence, such as seasonal influenza and the COVID-19 pandemic. The objective of this study was to develop and evaluate the efficacy of machine learning methods at identifying and ranking the real-time readiness of individual patients for discharge, with the goal of improving patient flow within hospitals during periods of crisis. METHODS AND PERFORMANCE Electronic Health Record data from Oxford University Hospitals was used to train independent models to classify and rank patients' real-time readiness for discharge within 24 hours, for patient subsets according to the nature of their admission (planned or emergency) and the number of days elapsed since their admission. A strategy for the use of the models' inference is proposed, by which the model makes predictions for all patients in hospital and ranks them in order of likelihood of discharge within the following 24 hours. The 20% of patients with the highest ranking are considered as candidates for discharge and would therefore expect to have a further screening by a clinician to confirm whether they are ready for discharge or not. Performance was evaluated in terms of positive predictive value (PPV), i.e., the proportion of these patients who would have been correctly deemed as 'ready for discharge' after having the second screening by a clinician. Performance was high for patients on their first day of admission (PPV = 0.96/0.94 for planned/emergency patients respectively) but dropped for patients further into a longer admission (PPV = 0.66/0.71 for planned/emergency patients still in hospital after 7 days). CONCLUSION We demonstrate the efficacy of machine learning methods at making operationally focused, next-day discharge readiness predictions for all individual patients in hospital at any given moment and propose a strategy for their use within a decision-support tool during crisis periods.
Collapse
Affiliation(s)
- Jennifer A. Bishop
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Hamza A. Javed
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Rasheed el-Bouri
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Tingting Zhu
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Thomas Taylor
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Tim Peto
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Peter Watkinson
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - David W. Eyre
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - David A. Clifton
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
47
|
Purnomo G, Yeo SJ, Liow MHL. Artificial intelligence in arthroplasty. ARTHROPLASTY 2021; 3:37. [PMID: 35236494 PMCID: PMC8796516 DOI: 10.1186/s42836-021-00095-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 08/31/2021] [Indexed: 01/10/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Glen Purnomo
- St. Vincentius a Paulo Catholic Hospital, Surabaya, Indonesia.
- Adult Reconstruction Service, Department of Orthopaedic Surgery, Singapore General Hospital, Singapore, Singapore.
| | - Seng-Jin Yeo
- Adult Reconstruction Service, Department of Orthopaedic Surgery, Singapore General Hospital, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Ming Han Lincoln Liow
- Adult Reconstruction Service, Department of Orthopaedic Surgery, Singapore General Hospital, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| |
Collapse
|
48
|
Lopez CD, Gazgalis A, Boddapati V, Shah RP, Cooper HJ, Geller JA. Artificial Learning and Machine Learning Decision Guidance Applications in Total Hip and Knee Arthroplasty: A Systematic Review. Arthroplast Today 2021; 11:103-112. [PMID: 34522738 PMCID: PMC8426157 DOI: 10.1016/j.artd.2021.07.012] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 07/17/2021] [Accepted: 07/26/2021] [Indexed: 12/14/2022] Open
Abstract
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 patient-reported outcomes and were less accurate in predicting hospital readmissions and reoperations.
Collapse
Affiliation(s)
- Cesar D. Lopez
- New York-Presbyterian/Columbia University Irving Medical Center, New York, NY
| | - Anastasia Gazgalis
- New York-Presbyterian/Columbia University Irving Medical Center, New York, NY
| | - Venkat Boddapati
- New York-Presbyterian/Columbia University Irving Medical Center, New York, NY
| | - Roshan P. Shah
- New York-Presbyterian/Columbia University Irving Medical Center, New York, NY
| | - H. John Cooper
- New York-Presbyterian/Columbia University Irving Medical Center, New York, NY
| | - Jeffrey A. Geller
- New York-Presbyterian/Columbia University Irving Medical Center, New York, NY
| |
Collapse
|
49
|
Wei C, Quan T, Wang KY, Gu A, Fassihi SC, Kahlenberg CA, Malahias MA, Liu J, Thakkar S, Gonzalez Della Valle A, Sculco PK. Artificial neural network prediction of same-day discharge following primary total knee arthroplasty based on preoperative and intraoperative variables. Bone Joint J 2021; 103-B:1358-1366. [PMID: 34334050 DOI: 10.1302/0301-620x.103b8.bjj-2020-1013.r2] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
AIMS This study used an artificial neural network (ANN) model to determine the most important pre- and perioperative variables to predict same-day discharge in patients undergoing total knee arthroplasty (TKA). METHODS Data for this study were collected from the National Surgery Quality Improvement Program (NSQIP) database from the year 2018. Patients who received a primary, elective, unilateral TKA with a diagnosis of primary osteoarthritis were included. Demographic, preoperative, and intraoperative variables were analyzed. The ANN model was compared to a logistic regression model, which is a conventional machine-learning algorithm. Variables collected from 28,742 patients were analyzed based on their contribution to hospital length of stay. RESULTS The predictability of the ANN model, area under the curve (AUC) = 0.801, was similar to the logistic regression model (AUC = 0.796) and identified certain variables as important factors to predict same-day discharge. The ten most important factors favouring same-day discharge in the ANN model include preoperative sodium, preoperative international normalized ratio, BMI, age, anaesthesia type, operating time, dyspnoea status, functional status, race, anaemia status, and chronic obstructive pulmonary disease (COPD). Six of these variables were also found to be significant on logistic regression analysis. CONCLUSION Both ANN modelling and logistic regression analysis revealed clinically important factors in predicting patients who can undergo safely undergo same-day discharge from an outpatient TKA. The ANN model provides a beneficial approach to help determine which perioperative factors can predict same-day discharge as of 2018 perioperative recovery protocols. Cite this article: Bone Joint J 2021;103-B(8):1358-1366.
Collapse
Affiliation(s)
- Chapman Wei
- Department of Orthopaedic Surgery, The George Washington University School of Medicine and Health Sciences, Washington, District of Columbia, USA
| | - Theodore Quan
- Department of Orthopaedic Surgery, The George Washington University School of Medicine and Health Sciences, Washington, District of Columbia, USA
| | - Kevin Y Wang
- Johns Hopkins Department of Orthopaedic Surgery, Adult Reconstruction Division, John Hopkins Medicine, Baltimore, Maryland, USA
| | - Alex Gu
- Department of Orthopaedic Surgery, The George Washington University School of Medicine and Health Sciences, Washington, District of Columbia, USA.,The Stavros Niarchos Foundation Complex Joint Reconstruction Center, Department of Orthopaedic Surgery, Hospital for Special Surgery, Washington, District of Columbia, USA
| | - Safa C Fassihi
- Department of Orthopaedic Surgery, The George Washington University School of Medicine and Health Sciences, Washington, District of Columbia, USA
| | - Cynthia A Kahlenberg
- Adult Reconstruction and Joint Replacement Division, Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, USA
| | - Michael-Alexander Malahias
- Adult Reconstruction and Joint Replacement Division, Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, USA
| | - Jiabin Liu
- Department of Anesthesiology, Critical Care & Pain Management, Hospital for Special Surgery, New York, New York, USA
| | - Savyasachi Thakkar
- Johns Hopkins Department of Orthopaedic Surgery, Adult Reconstruction Division, John Hopkins Medicine, Baltimore, Maryland, USA
| | - Alejandro Gonzalez Della Valle
- The Stavros Niarchos Foundation Complex Joint Reconstruction Center, Department of Orthopaedic Surgery, Hospital for Special Surgery, Washington, District of Columbia, USA
| | - Peter K Sculco
- The Stavros Niarchos Foundation Complex Joint Reconstruction Center, Department of Orthopaedic Surgery, Hospital for Special Surgery, Washington, District of Columbia, USA
| |
Collapse
|
50
|
Groot OQ, Bindels BJJ, Ogink PT, Kapoor ND, Twining PK, Collins AK, Bongers MER, Lans A, Oosterhoff JHF, Karhade AV, Verlaan JJ, Schwab JH. Availability and reporting quality of external validations of machine-learning prediction models with orthopedic surgical outcomes: a systematic review. Acta Orthop 2021; 92:385-393. [PMID: 33870837 PMCID: PMC8436968 DOI: 10.1080/17453674.2021.1910448] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/31/2023] Open
Abstract
Background and purpose - External validation of machine learning (ML) prediction models is an essential step before clinical application. We assessed the proportion, performance, and transparent reporting of externally validated ML prediction models in orthopedic surgery, using the Transparent Reporting for Individual Prognosis or Diagnosis (TRIPOD) guidelines.Material and methods - We performed a systematic search using synonyms for every orthopedic specialty, ML, and external validation. The proportion was determined by using 59 ML prediction models with only internal validation in orthopedic surgical outcome published up until June 18, 2020, previously identified by our group. Model performance was evaluated using discrimination, calibration, and decision-curve analysis. The TRIPOD guidelines assessed transparent reporting.Results - We included 18 studies externally validating 10 different ML prediction models of the 59 available ML models after screening 4,682 studies. All external validations identified in this review retained good discrimination. Other key performance measures were provided in only 3 studies, rendering overall performance evaluation difficult. The overall median TRIPOD completeness was 61% (IQR 43-89), with 6 items being reported in less than 4/18 of the studies.Interpretation - Most current predictive ML models are not externally validated. The 18 available external validation studies were characterized by incomplete reporting of performance measures, limiting a transparent examination of model performance. Further prospective studies are needed to validate or refute the myriad of predictive ML models in orthopedics while adhering to existing guidelines. This ensures clinicians can take full advantage of validated and clinically implementable ML decision tools.
Collapse
Affiliation(s)
- Olivier Q Groot
- Orthopedic Oncology Service, Massachusetts General Hospital, Harvard Medical School, Boston, USA;;
- Department of Orthopedic Surgery, University Medical Center Utrecht, Utrecht University, The Netherlands
| | - Bas J J Bindels
- Department of Orthopedic Surgery, University Medical Center Utrecht, Utrecht University, The Netherlands
| | - Paul T Ogink
- Department of Orthopedic Surgery, University Medical Center Utrecht, Utrecht University, The Netherlands
| | - Neal D Kapoor
- Orthopedic Oncology Service, Massachusetts General Hospital, Harvard Medical School, Boston, USA;;
| | - Peter K Twining
- Orthopedic Oncology Service, Massachusetts General Hospital, Harvard Medical School, Boston, USA;;
| | - Austin K Collins
- Orthopedic Oncology Service, Massachusetts General Hospital, Harvard Medical School, Boston, USA;;
| | - Michiel E R Bongers
- Orthopedic Oncology Service, Massachusetts General Hospital, Harvard Medical School, Boston, USA;;
| | - Amanda Lans
- Orthopedic Oncology Service, Massachusetts General Hospital, Harvard Medical School, Boston, USA;;
- Department of Orthopedic Surgery, University Medical Center Utrecht, Utrecht University, The Netherlands
| | - Jacobien H F Oosterhoff
- Orthopedic Oncology Service, Massachusetts General Hospital, Harvard Medical School, Boston, USA;;
| | - Aditya V Karhade
- Orthopedic Oncology Service, Massachusetts General Hospital, Harvard Medical School, Boston, USA;;
| | - Jorrit-Jan Verlaan
- Department of Orthopedic Surgery, University Medical Center Utrecht, Utrecht University, The Netherlands
| | - Joseph H Schwab
- Orthopedic Oncology Service, Massachusetts General Hospital, Harvard Medical School, Boston, USA;;
| |
Collapse
|