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Rodriguez HC, Rust BD, Roche MW, Gupta A. Artificial intelligence and machine learning in knee arthroplasty. Knee 2025; 54:28-49. [PMID: 40022960 DOI: 10.1016/j.knee.2025.02.014] [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: 05/23/2024] [Revised: 10/09/2024] [Accepted: 02/07/2025] [Indexed: 03/04/2025]
Abstract
BACKGROUND Artificial intelligence (AI) and its subset, machine learning (ML), have significantly impacted clinical medicine, particularly in knee arthroplasty (KA). These technologies utilize algorithms for tasks such as predictive analytics and image recognition, improving preoperative planning, intraoperative navigation, and postoperative complication anticipation. This systematic review presents AI-driven tools' clinical implications in total and unicompartmental KA, focusing on enhancing patient outcomes and operational efficiency. METHODS A systematic search was conducted across multiple databases including Cochrane Central Register of Controlled Trials, Embase, OVID Medline, PubMed, and Web of Science, following the PRISMA guidelines for studies published in the English language till March 2024. Inclusion criteria targeted adult human models without geographical restrictions, specifically related to total or unicompartmental KA. RESULTS A total of 153 relevant studies were identified, covering various aspects of ML application for KA. Topics of studies included imaging modalities (n = 28), postoperative primary KA complications (n = 26), inpatient status (length of stay, readmissions, and cost) (n = 24), implant configuration (n = 14), revision (n = 12), patient-reported outcome measures (PROMs) (n = 11), function (n = 11), procedural communication (n = 8), total knee arthroplasty/unicompartmental knee arthroplasty prediction (n = 6), outpatient status (n = 4), perioperative efficiency (n = 4), patient satisfaction (n = 3), opioid usage (n = 3). A total of 66 ML models were described, with 48.7% of studies using multiple approaches. CONCLUSION This review assesses ML applications in knee arthroplasty, highlighting their potential to improve patient outcomes. While current algorithms and AI show promise, our findings suggest areas for enhancement in predictive performance before widespread clinical adoption.
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Affiliation(s)
- Hugo C Rodriguez
- Larkin Community Hospital, Department of Orthopaedic Surgery, South Miami, FL, USA; Hospital for Special Surgery, West Palm Beach, FL, USA
| | - Brandon D Rust
- Nova Southeastern University, Dr. Kiran C. Patel College of Osteopathic Medicine, Fort Lauderdale, FL, USA
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Longo UG, Marino M, Nicodemi G, Pisani MG, Oeding JF, Ley C, Papalia R, Samuelsson K. Artificial intelligence applications in the management of musculoskeletal disorders of the shoulder: A systematic review. J Exp Orthop 2025; 12:e70248. [PMID: 40303836 PMCID: PMC12038175 DOI: 10.1002/jeo2.70248] [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: 07/20/2024] [Revised: 03/06/2025] [Accepted: 03/11/2025] [Indexed: 05/02/2025] Open
Abstract
Purpose The aim of the present review is to evaluate and report on the available literature discussing artificial intelligence (AI) applications to the diagnosis of shoulder conditions, outcome prediction of shoulder interventions, and the possible application of such algorithms directly to surgical procedures. Methods In February 2024, a search of PubMed, Cochrane and Scopus databases was performed. Studies had to evaluate AI model effectiveness for inclusion. Research on healthcare cost predictions, deterministic algorithms, patient satisfaction, protocol studies and upper-extremity fractures not involving the shoulder were excluded. The Joanna Briggs Institute Critical Appraisal tool and the Risk of Bias in Non-randomised Studies of Interventions tools were used to assess bias. Results Thirty-three studies were included in the analysis. Seven studies analysed the detection of rotator cuff tears (RCTs) in magnetic resonance imaging and found area under the curve (AUC) values ranged from 0.812 to 0.94 for the detection of RCTs. One study reported Area Under the Receiver Operating Characteristics values ranging from 0.79 to 0.97 for the prediction of clinical outcomes following reverse total shoulder arthroplasty. In terms of outcomes of rotator cuff repair, an AUC value ranging from 0.58 to 0.68 was reported for prediction of patient-reported outcome measures, and an AUC range of 0.87-0.92 was found for prediction of retear rate. Five studies evaluated the identification of shoulder implant models following TSA from radiographs, with reported accuracy ranging from 89.90% to 97.20%. Conclusion AI application enables forecasting of clinical outcomes, permits refined diagnostic evaluation and increases surgical accuracy. While promising, the translation of these technologies into routine clinical practice requires careful consideration. Level of Evidence Level IV.
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Affiliation(s)
- Umile Giuseppe Longo
- Fondazione Policlinico Universitario Campus Bio‐MedicoRomaItaly
- Research Unit of Orthopaedic and Trauma Surgery, Department of Medicine and SurgeryUniversità Campus Bio‐Medico di RomaRomaItaly
| | - Martina Marino
- Fondazione Policlinico Universitario Campus Bio‐MedicoRomaItaly
- Research Unit of Orthopaedic and Trauma Surgery, Department of Medicine and SurgeryUniversità Campus Bio‐Medico di RomaRomaItaly
| | - Guido Nicodemi
- Fondazione Policlinico Universitario Campus Bio‐MedicoRomaItaly
- Research Unit of Orthopaedic and Trauma Surgery, Department of Medicine and SurgeryUniversità Campus Bio‐Medico di RomaRomaItaly
| | - Matteo Giuseppe Pisani
- Fondazione Policlinico Universitario Campus Bio‐MedicoRomaItaly
- Research Unit of Orthopaedic and Trauma Surgery, Department of Medicine and SurgeryUniversità Campus Bio‐Medico di RomaRomaItaly
| | - Jacob F. Oeding
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska AcademyUniversity of GothenburgGothenburgSweden
| | - Christophe Ley
- Department of MathematicsUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Rocco Papalia
- Fondazione Policlinico Universitario Campus Bio‐MedicoRomaItaly
- Research Unit of Orthopaedic and Trauma Surgery, Department of Medicine and SurgeryUniversità Campus Bio‐Medico di RomaRomaItaly
| | - Kristian Samuelsson
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska AcademyUniversity of GothenburgGothenburgSweden
- Sahlgrenska Sports Medicine CenterGothenburgSweden
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Henry JP, Paradis B, Qilleri A, Baichoo N, Reinhardt KR, Slover JD, Danoff JR, Germano JA. Size-Up, Size-Down: Accuracy of Component Sizing with Computerized Tomography and Robotic-Assisted Total Knee Arthroplasty. J Knee Surg 2025; 38:217-223. [PMID: 39689870 DOI: 10.1055/s-0044-1800976] [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: 12/19/2024]
Abstract
Templating prior to total knee arthroplasty (TKA) can help to improve surgical efficiency and potentially improve alignment and outcomes. The purpose of this article is to evaluate the ability of computed tomography (CT)-based preoperative templating to accurately predict implant sizes. A total of 724 Stryker MAKO robotic-assisted TKA cases were retrospectively evaluated from a prospectively collected database between January 2020 and October 2023. Cases were performed by one of three adult reconstruction fellowship-trained orthopaedic surgeons from a health system that includes an academic level one trauma center, an ambulatory surgery center, and a community hospital. Out of the 724 cases, 391 were preoperatively templated independently by the surgeon and the company representative (MAKO Product Specialist [MPS]). The remaining 333 cases were only templated prior to incision by the MPS. Final implant sizes of the tibial and femoral components were compared to preoperative templates. The MPS was able to preoperatively predict the final tibial and femoral implants within one size in 97.2 and 97.8% of cases, respectively. A surgeon and MPS combined preoperative templating increased accuracy to predict the final tibial and femoral implants within one size in 98.9 and 99.5% of cases, respectively. Height and weight were positively correlated with the final implant size (p < 0.001). Non-surgeons can reliably predict implanted components in CT-based preoperative templating in the majority of cases, which is further enhanced by surgeon review and adjustments. In no cases in our series were the final size components implanted greater than two sizes larger or smaller. Our findings suggest that there is opportunity to avoid waste by processing fewer trial implants and transporting fewer components. This would likely decrease overall case cost and improve efficiency in the operating room. Level of evidence: III (retrospective cohort).
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Affiliation(s)
- James P Henry
- Department of Orthopaedic Surgery, Huntington Hospital, Northwell Health, Huntington, New York
| | - Brienne Paradis
- University of New England College of Osteopathic Medicine, Biddeford, Maine
| | - Aleksandra Qilleri
- Department of Orthopaedic Surgery, Donald & Barbara Zucker School of Medicine, Hofstra University/Northwell Health, Hempstead, New York
| | - Nadia Baichoo
- Orlin & Cohen Orthopaedic Group, Rockville Center, New York
| | - Keith R Reinhardt
- Department of Orthopaedic Surgery, South Shore University Hospital, Northwell Health, Bay Shore, New York
| | - James D Slover
- Department of Orthopaedic Surgery, Lenox Hill Hospital, Northwell Health, New York, New York
| | - Jonathan R Danoff
- Department of Orthopaedic Surgery, Northshore University Hospital, Northwell Health, Manhasset, New York
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Park KB, Kim MS, Yoon DK, Jeon YD. Clinical validation of a deep learning-based approach for preoperative decision-making in implant size for total knee arthroplasty. J Orthop Surg Res 2024; 19:637. [PMID: 39380122 PMCID: PMC11463000 DOI: 10.1186/s13018-024-05128-6] [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: 08/27/2024] [Accepted: 09/28/2024] [Indexed: 10/10/2024] Open
Abstract
BACKGROUND Orthopedic surgeons use manual measurements, acetate templating, and dedicated software to determine the appropriate implant size for total knee arthroplasty (TKA). This study aimed to use deep learning (DL) to assist in deciding the femoral and tibial implant sizes without manual manipulation and to evaluate the clinical validity of the DL decision by comparing it with conventional manual procedures. METHODS Two types of DL were used to detect the femoral and tibial regions using the You Only Look Once algorithm model and to determine the implant size from the detected regions using convolutional neural network. An experienced surgeon predicted the implant size for 234 patient cases using manual procedures, and the DL model also predicted the implant sizes for the same cases. RESULTS The exact accuracies of the surgeon's template were 61.54% and 68.38% for predicting femoral and tibial implant sizes, respectively. Meanwhile, the proposed DL model reported exact accuracies of 89.32% and 90.60% for femoral and tibial implant sizes, respectively. The accuracy ± 1 levels of the surgeon and proposed DL model were 97.44% and 97.86%, respectively, for the femoral implant size and 98.72% for both the surgeon and proposed DL model for the tibial implant size. CONCLUSION The observed differences and higher agreement levels achieved by the proposed DL model demonstrate its potential as a valuable tool in preoperative decision-making for TKA. By providing accurate predictions of implant size, the proposed DL model has the potential to optimize implant selection, leading to improved surgical outcomes.
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Affiliation(s)
- Ki-Bong Park
- Department of Orthopaedic Surgery, University of Ulsan College of Medicine, Ulsan University Hospital, Ulsan, South Korea
| | - Moo-Sub Kim
- Industrial R&D Center, Kavilab Co., Ltd, Seoul, South Korea
| | - Do-Kun Yoon
- Industrial R&D Center, Kavilab Co., Ltd, Seoul, South Korea
- Department of Integrative Medicine, College of Medicine, Yonsei University, Seoul, South Korea
| | - Young Dae Jeon
- Department of Orthopaedic Surgery, University of Ulsan College of Medicine, Ulsan University Hospital, Ulsan, South Korea.
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5
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Corti A, Galante S, Rauch R, Chiappetta K, Corino V, Loppini M. Leveraging transfer learning for predicting total knee arthroplasty failure from post-operative radiographs. J Exp Orthop 2024; 11:e70097. [PMID: 39664926 PMCID: PMC11633713 DOI: 10.1002/jeo2.70097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2024] [Accepted: 10/15/2024] [Indexed: 12/13/2024] Open
Abstract
Purpose The incidence of both primary and revision total knee arthroplasty (TKA) is expected to rise, making early recognition of TKA failure crucial to prevent extensive revision surgeries. This study aims to develop a deep learning (DL) model to predict TKA failure using radiographic images. Methods Two patient cohorts who underwent primary TKA were retrospectively collected: one was used for the model development and the other for the external validation. Each cohort encompassed failed and non-failed subjects, according to the need for TKA revision surgery. Moreover, for each patient, one anteroposterior and one lateral radiographic view obtained during routine TKA follow-up, were considered. A transfer learning fine-tuning approach was employed. After pre-processing, the images were analyzed using a convolutional neuronal network (CNN) that was originally developed for predicting hip prosthesis failure and was based on the Densenet169 pre-trained on Imagenet. The model was tested on 20% of the images of the first cohort and externally validated on the images of the second cohort. Metrics, such as accuracy, sensitivity, specificity and area under the receiving operating characteristic curve (AUC), were calculated for the final assessment. Results The trained model correctly classified 108 out of 127 images in the test set, providing a classification accuracy of 0.85, sensitivity of 0.80, specificity of 0.89 and AUC of 0.86. Moreover, the model correctly classified 1547 out of 1937 in the external validation set, providing a balanced accuracy of 0.79, sensitivity of 0.80, specificity of 0.78 and AUC of 0.86. Conclusions The present DL model predicts TKA failure with moderate accuracy, regardless of the cause of revision surgery. Additionally, the effectiveness of the transfer learning fine-tuning approach, leveraging a previously developed DL model for hip prosthesis failure, has been successfully demonstrated. Level of Evidence Level III, diagnostic study.
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Affiliation(s)
- Anna Corti
- Department of Electronics, Information and BioengineeringPolitecnico di MilanoMilanMilanItaly
| | - Sarah Galante
- Department of Electronics, Information and BioengineeringPolitecnico di MilanoMilanMilanItaly
| | | | | | - Valentina Corino
- Department of Electronics, Information and BioengineeringPolitecnico di MilanoMilanMilanItaly
- Cardio Tech‐LabCentro Cardiologico Monzino IRCCSMilanMilanItaly
| | - Mattia Loppini
- IRCCS Humanitas Research HospitalRozzanoMilanItaly
- Department of Biomedical Sciences, Humanitas UniversityPieve EmanueleMilanItaly
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Yu Y, Cho YJ, Park S, Kim YH, Goh TS. Development of an artificial intelligence model for predicting implant size in total knee arthroplasty using simple X-ray images. J Orthop Surg Res 2024; 19:516. [PMID: 39192371 DOI: 10.1186/s13018-024-05013-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2024] [Accepted: 08/19/2024] [Indexed: 08/29/2024] Open
Abstract
BACKGROUND Accurate estimation of implant size before surgery is crucial in preparing for total knee arthroplasty. However, this task is time-consuming and labor-intensive. To alleviate this burden on surgeons, we developed a reliable artificial intelligence (AI) model to predict implant size. METHODS We enrolled 714 patients with knee osteoarthritis who underwent total knee arthroplasty from March 2010 to February 2014. All surgeries were performed by the same surgeon using implants from the same manufacturer. We collected 1412 knee anteroposterior (AP) and lateral view x-ray images and retrospectively investigated the implant size. We trained the AI model using both AP and lateral images without any clinical or demographic information and performed data augmentation to resolve issues of uneven distribution and insufficient data. Using data augmentation techniques, we generated 500 images for each size of the femur and tibia, which were then used to train the model. Using data augmentation techniques, we generated 500 images for each size of the femur and tibia, which were then used to train the model. We used ResNet-101 and optimized the model with the aim of minimizing the cross-entropy loss function using both the Stochastic Gradient Descent (SGD) and Adam optimizer. RESULTS The SGD optimizer achieved the best performance in internal validation. The model showed micro F1-score 0.91 for femur and 0.87 for tibia. For predicting within ± one size, micro F1-score was 0.99 for femur and 0.98 for tibia. CONCLUSION We developed a deep learning model with high predictive power for implant size using only simple x-ray images. This could help surgeons reduce the time and labor required for preoperative preparation in total knee arthroplasty. While similar studies have been conducted, our work is unique in its use of simple x-ray images without any other data, like demographic features, to achieve a model with strong predictive power.
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Affiliation(s)
- Yeuni Yu
- Biomedical Research Institute, School of Medicine, Pusan National University, Yangsan, Republic of Korea
| | - Yoon Jae Cho
- Department of Orthopaedic Surgery, School of Medicine, Pusan National University, Busan, Republic of Korea
- Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea
| | - Sohee Park
- Convergence Medical Sciences, Pusan National University, Yangsan, Republic of Korea
- Data Science Center, Insilicogen, Inc, Yongin-si, Korea
| | - Yun Hak Kim
- Department of Biomedical Informatics, School of Medicine, Pusan National University, Yangsan, Republic of Korea.
- Department of Anatomy, School of Medicine, Pusan National University, Yangsan, Republic of Korea.
| | - Tae Sik Goh
- Department of Orthopaedic Surgery, School of Medicine, Pusan National University, Busan, Republic of Korea.
- Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea.
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Lan Q, Li S, Zhang J, Guo H, Yan L, Tang F. Reliable prediction of implant size and axial alignment in AI-based 3D preoperative planning for total knee arthroplasty. Sci Rep 2024; 14:16971. [PMID: 39043748 PMCID: PMC11266554 DOI: 10.1038/s41598-024-67276-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 07/09/2024] [Indexed: 07/25/2024] Open
Abstract
The size and axial alignment of prostheses, when planned during total knee replacement (TKA) are critical for recovery of knee function and improvement of knee pain symptoms. This research aims to study the effect of artificial intelligence (AI)-based preoperative three dimensional (3D) planning technology on prosthesis size and axial alignment planning in TKA, and to compare its advantages with two dimensional (2D) X-ray template measurement technology. A total of 60 patients with knee osteoarthritis (KOA) who underwent TKA for the first time were included in the AI (n = 30) and 2D (n = 30) groups. The preoperative and postoperative prosthesis size, femoral valgus correction angle (VCA) and hip-knee-ankle angle (HKA) were recorded and compared between the two groups. The results of the University of Western Ontario and McMaster University Osteoarthritis Index (WOMAC) and the American Knee Association Score (AKS) were evaluated before surgery, 3 months, 6 months, and 12 months after surgery. The accuracy of prosthesis size, VCA and HKA prediction in AI group was significantly higher than that in 2D group (P < 0.05). The WOMAC and AKS scores in AI group at 3 months, 6 months and 12 months after surgery were better than those in 2D group (P < 0.05). Both groups showed significant improvement in WOMAC and AKS scores at 12 months follow-up. AI-based preoperative 3D planning technique has more reliable planning effect for prosthesis size and axial alignment in TKA.
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Affiliation(s)
- Qing Lan
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
- Department of Orthopedics, Fujian Provincial Hospital, Fuzhou, China
- Fuzhou University Affiliated Provincial Hospital, Fuzhou, China
| | - Shulin Li
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
- Department of Orthopedics, Fujian Provincial Hospital, Fuzhou, China
- Fuzhou University Affiliated Provincial Hospital, Fuzhou, China
| | - Jiahao Zhang
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
| | - Huiling Guo
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
- Department of Orthopedics, Fujian Provincial Hospital, Fuzhou, China
- Fuzhou University Affiliated Provincial Hospital, Fuzhou, China
| | - Laipeng Yan
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
- Department of Orthopedics, Fujian Provincial Hospital, Fuzhou, China
- Fuzhou University Affiliated Provincial Hospital, Fuzhou, China
| | - Faqiang Tang
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China.
- Department of Orthopedics, Fujian Provincial Hospital, Fuzhou, China.
- Fuzhou University Affiliated Provincial Hospital, Fuzhou, China.
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Pichler L, Klein L, Perka CF, Gwinner C, El Kayali MKD. The accuracy of preoperative implant size prediction achieved by digital templating in total knee arthroplasty is not affected by the quality of lateral knee radiographs. J Exp Orthop 2024; 11:e12102. [PMID: 39050591 PMCID: PMC11267166 DOI: 10.1002/jeo2.12102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 06/06/2024] [Accepted: 06/23/2024] [Indexed: 07/27/2024] Open
Abstract
Background Digital templating software can be used for preoperative implant size prediction in total knee arthroplasty (TKA). However, the accuracy of its prediction is reported to be low, and the impact of radiograph quality is unclear. Purpose To investigate on the application of lateral knee radiograph quality criteria for knee rotation (KR) and knee abduction/adduction (KA) and their impact on the accuracy of final implant size prediction achieved by preoperative digital templating for TKA. Methods A total of 191 radiographs of patients undergoing TKA were allocated into four groups according to their KR as measured at the posterior femoral condyles and their KA as measured at the distal femoral condyles on lateral knee radiographs: group A (KR ≤ 5 mm, KA ≤ 5 mm), B1 (KR > 5 mm, KA ≤ 5 mm), B2 (KR ≤ 5 mm, KA > 5 mm) and B3 (KR > 5 mm, KA > 5 mm). Preoperative templating of femoral and tibial implant size using digital templating software was carried out by two observers. Correlation coefficients (CCs) between planned and final implant size, percentage of cases with planned to final size match as well as percentage of cases within ±1 and ±2 of planned to final size were reported according to groups. Results Group A showed the highest percentage of cases with matching planned to final femoral implant size (45%) and the highest percentage of cases with ±1 planned to final implant size (86%) as compared to B1 (match 28%, ±1 84%), B2 (match 41%, ±1 84%) and B3 (match 35%, ±1 78%). CCs for planned to final implant size were reported at >0.75 in all groups. No statistically significant difference in the CCs of planned to final implant size amongst groups was found. Conclusion The accuracy of implant size prediction achieved by preoperative digital templating for TKA is neither affected by KR nor KA on lateral knee radiographs. Level of evidence Level III.
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Affiliation(s)
- Lorenz Pichler
- Charité—Universitätsmedizin BerlinCentrum für Muskuloskeletale ChirurgieBerlinGermany
| | - Leonhard Klein
- Charité—Universitätsmedizin BerlinCentrum für Muskuloskeletale ChirurgieBerlinGermany
| | - Carsten F. Perka
- Charité—Universitätsmedizin BerlinCentrum für Muskuloskeletale ChirurgieBerlinGermany
| | - Clemens Gwinner
- Charité—Universitätsmedizin BerlinCentrum für Muskuloskeletale ChirurgieBerlinGermany
| | - Moses K. D. El Kayali
- Charité—Universitätsmedizin BerlinCentrum für Muskuloskeletale ChirurgieBerlinGermany
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Rupp M, Moser LB, Hess S, Angele P, Aurich M, Dyrna F, Nehrer S, Neubauer M, Pawelczyk J, Izadpanah K, Zellner J, Niemeyer P, AGA‐Komitee Innovation und Translation. Orthopaedic surgeons display a positive outlook towards artificial intelligence: A survey among members of the AGA Society for Arthroscopy and Joint Surgery. J Exp Orthop 2024; 11:e12080. [PMID: 38974054 PMCID: PMC11227606 DOI: 10.1002/jeo2.12080] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Revised: 06/13/2024] [Accepted: 06/21/2024] [Indexed: 07/09/2024] Open
Abstract
Purpose The purpose of this study was to evaluate the perspective of orthopaedic surgeons on the impact of artificial intelligence (AI) and to evaluate the influence of experience, workplace setting and familiarity with digital solutions on views on AI. Methods Orthopaedic surgeons of the AGA Society for Arthroscopy and Joint Surgery were invited to participate in an online, cross-sectional survey designed to gather information on professional background, subjective AI knowledge, opinion on the future impact of AI, openness towards different applications of AI, and perceived advantages and disadvantages of AI. Subgroup analyses were performed to examine the influence of experience, workplace setting and openness towards digital solutions on perspectives towards AI. Results Overall, 360 orthopaedic surgeons participated. The majority indicated average (43.6%) or rudimentary (38.1%) AI knowledge. Most (54.5%) expected AI to substantially influence orthopaedics within 5-10 years, predominantly as a complementary tool (91.1%). Preoperative planning (83.8%) was identified as the most likely clinical use case. A lack of consensus was observed regarding acceptable error levels. Time savings in preoperative planning (62.5%) and improved documentation (81%) were identified as notable advantages while declining skills of the next generation (64.5%) were rated as the most substantial drawback. There were significant differences in subjective AI knowledge depending on participants' experience (p = 0.021) and familiarity with digital solutions (p < 0.001), acceptable error levels depending on workplace setting (p = 0.004), and prediction of AI impact depending on familiarity with digital solutions (p < 0.001). Conclusion The majority of orthopaedic surgeons in this survey anticipated a notable positive impact of AI on their field, primarily as an assistive technology. A lack of consensus on acceptable error levels of AI and concerns about declining skills among future surgeons were observed. Level of Evidence Level IV, cross-sectional study.
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Affiliation(s)
- Marco‐Christopher Rupp
- Sektion Sportorthopädie, Klinikum rechts der IsarTechnische Universität MünchenMunichGermany
- Steadman Philippon Research InstituteVailColoradoUSA
| | - Lukas B. Moser
- Klinische Abteilung für Orthopädie und TraumatologieUniversitätsklinikum KremsKrems an der DonauAustria
- Zentrum für Regenerative MedizinUniversität für Weiterbildung KremsKrems an der DonauAustria
- SporthopaedicumRegensburgGermany
| | - Silvan Hess
- Universitätsklinik für Orthopädische Chirurgie und Traumatologie, InselspitalBernSwitzerland
| | - Peter Angele
- SporthopaedicumRegensburgGermany
- Klinik für Unfall‐ und WiederherstellungschirurgieUniversitätsklinikum RegensburgRegensburgGermany
| | | | | | - Stefan Nehrer
- Klinische Abteilung für Orthopädie und TraumatologieUniversitätsklinikum KremsKrems an der DonauAustria
- Zentrum für Regenerative MedizinUniversität für Weiterbildung KremsKrems an der DonauAustria
- Fakultät für Gesundheit und MedizinUniversität für Weiterbildung KremsKrems an der DonauAustria
| | - Markus Neubauer
- Klinische Abteilung für Orthopädie und TraumatologieUniversitätsklinikum KremsKrems an der DonauAustria
- Zentrum für Regenerative MedizinUniversität für Weiterbildung KremsKrems an der DonauAustria
| | - Johannes Pawelczyk
- Sektion Sportorthopädie, Klinikum rechts der IsarTechnische Universität MünchenMunichGermany
| | - Kaywan Izadpanah
- Klinik für Orthopädie und Unfallchirurgie, Universitätsklinikum Freiburg, Medizinische FakultätAlbert‐Ludwigs‐Universität FreiburgFreiburgGermany
| | | | - Philipp Niemeyer
- OCM – Orthopädische Chirurgie MünchenMunichGermany
- Albert‐Ludwigs‐UniversityFreiburgGermany
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Vahabi A, Er E, Biçer EK, Şahin F, Kavakli K, Aydoğdu S. Accuracy and clinical role of digital templating for total knee arthroplasty performed on haemophilic knees. Haemophilia 2024; 30:1043-1049. [PMID: 39014891 DOI: 10.1111/hae.15072] [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: 03/05/2024] [Revised: 05/21/2024] [Accepted: 06/09/2024] [Indexed: 07/18/2024]
Abstract
INTRODUCTION In total knee arthroplasty (TKA), choosing the correct implant size is important. There is lack of data on accuracy of templating on haemophilic knees. Our aim was to test the accuracy of 2D digital templating for TKA on haemophilic arthropathy (HA) of knee. MATERIALS AND METHODS TKAs performed on HA between January 2011 and January 2022 were screened. Osteoarthritis (OA) group was created as control group by a one-to-one matching regarding type of implant used. Intra- and interobserver correlations were measured in HA, then correlation between templated and implanted sizes was investigated in four assessments (femur AP, femur lateral, tibia AP, tibia lateral), then compared with OA group. Fifty-eight knees in each group included. RESULTS Regarding intraobserver correlation in HA, there was excellent correlation for femur AP [.93 (.73-.98)], femur lateral [.98 (.91-.99)], and tibia AP (1.0) templating. Regarding interobserver correlation in HA, excellent correlation was observed for femur lateral [.93 (.74-.98)] and tibia AP templating [.90 (.65-.97)]. Regarding correlation of templated and applied sizes in HA; tibia AP, tibia lateral and femur lateral templating showed good correlation [.81 (.70-.89), .86 (.77-.91), .79 (.67-.87) while femur AP templating showed moderate correlation [.67 (.50-.79)]. Comparing HA and OA, there was no difference in correlation levels regarding femur AP, femur lateral, tibia AP and tibia lateral templating (p = .056, p = .781, p = .761, p = .083, respectively). CONCLUSION Although 2D digital templating shows comparable correlation in HA and OA, clinical applicability of templating on HA appears to be limited in its current state.
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Affiliation(s)
- Arman Vahabi
- Department of Orthopedics and Traumatology, Ege University School of Medicine, Izmir, Turkey
| | - Erdem Er
- Department of Orthopaedics and Traumatology, Kars Harakani State Hospital, Kars, Turkey
| | - Elcil Kaya Biçer
- Department of Orthopedics and Traumatology, Ege University School of Medicine, Izmir, Turkey
| | - Fahri Şahin
- Department of Internal Medicine Division of Hematology, Ege University Faculty of Medicine, Izmir, Turkey
| | - Kaan Kavakli
- Department of Pediatrics Division of Hemato-Oncology, Ege University Faculty of Medicine, Izmir, Turkey
| | - Semih Aydoğdu
- Department of Orthopedics and Traumatology, Ege University School of Medicine, Izmir, Turkey
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Yao J, Du Z, Yang F, Duan R, Feng T. The relationship between heavy metals and metabolic syndrome using machine learning. Front Public Health 2024; 12:1378041. [PMID: 38686033 PMCID: PMC11057329 DOI: 10.3389/fpubh.2024.1378041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 04/02/2024] [Indexed: 05/02/2024] Open
Abstract
Background Exposure to high levels of heavy metals has been widely recognized as an important risk factor for metabolic syndrome (MetS). The main purpose of this study is to assess the associations between the level of heavy metal exposure and Mets using machine learning (ML) method. Methods The data used in this study are from the national health and nutrition examination survey 2003-2018. According to the demographic information and heavy metal exposure level of participants, a total of 22 variables were included. Lasso was used to screen out the key variables, and 9 commonly used ML models were selected to establish the associations with the 5-fold cross validation method. Finally, we choose the SHapley Additive exPlanations (SHAP) method to explain the prediction results of Adaboost model. Results 11,667 eligible individuals were randomly divided into two groups to train and verify the prediction model. Through lasso, characteristic variables were selected from 24 variables as predictors. The AUC (area under curve) of the models selected in this study were all greater than 0.7, and AdaBoost was the best model. The AUC value of AdaBoost was 0.807, the accuracy was 0.720, and the sensitivity was 0.792. It is noteworthy that higher levels of cadmium, body mass index, cesium, being female, and increasing age were associated with an increased probability of MetS. Conversely, lower levels of cobalt and molybdenum were linked to a decrease in the estimated probability of MetS. Conclusion Our study highlights the AdaBoost model proved to be highly effective, precise, and resilient in detecting a correlation between exposure to heavy metals and MetS. Through the use of interpretable methods, we identified cadmium, molybdenum, cobalt, cesium, uranium, and barium as prominent contributors within the predictive model.
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Affiliation(s)
- Jun Yao
- Department of Respiratory and Critical Care, Guangyuan Central Hospital, Guangyuan, Sichuan, China
| | - Zhilin Du
- Department of Oncology, Chengdu Seventh People’s Hospital (Affliated Cancer Hospital of Chengdu Medical College), Chengdu, Sichuan, China
| | - Fuyue Yang
- Department of Rheumatology and Immunology, Chengdu Fifth People's Hospital, Chengdu, Sichuan, China
| | - Ran Duan
- Clinical Medical College, Chengdu Medical College, Chengdu, Sichuan, China
- Department of Oncology, The First Aliated Hospital of Chengdu Medical College, Chengdu, Sichuan, China
| | - Tong Feng
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
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Huffman N, Pasqualini I, Khan ST, Klika AK, Deren ME, Jin Y, Kunze KN, Piuzzi NS. Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence: A Critical Analysis Review. JBJS Rev 2024; 12:01874474-202403000-00006. [PMID: 38466797 DOI: 10.2106/jbjs.rvw.23.00232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
» The application of artificial intelligence (AI) in the field of orthopaedic surgery holds potential for revolutionizing health care delivery across 3 crucial domains: (I) personalized prediction of clinical outcomes and adverse events, which may optimize patient selection, surgical planning, and enhance patient safety and outcomes; (II) diagnostic automated and semiautomated imaging analyses, which may reduce time burden and facilitate precise and timely diagnoses; and (III) forecasting of resource utilization, which may reduce health care costs and increase value for patients and institutions.» Computer vision is one of the most highly studied areas of AI within orthopaedics, with applications pertaining to fracture classification, identification of the manufacturer and model of prosthetic implants, and surveillance of prosthesis loosening and failure.» Prognostic applications of AI within orthopaedics include identifying patients who will likely benefit from a specified treatment, predicting prosthetic implant size, postoperative length of stay, discharge disposition, and surgical complications. Not only may these applications be beneficial to patients but also to institutions and payors because they may inform potential cost expenditure, improve overall hospital efficiency, and help anticipate resource utilization.» AI infrastructure development requires institutional financial commitment and a team of clinicians and data scientists with expertise in AI that can complement skill sets and knowledge. Once a team is established and a goal is determined, teams (1) obtain, curate, and label data; (2) establish a reference standard; (3) develop an AI model; (4) evaluate the performance of the AI model; (5) externally validate the model, and (6) reinforce, improve, and evaluate the model's performance until clinical implementation is possible.» Understanding the implications of AI in orthopaedics may eventually lead to wide-ranging improvements in patient care. However, AI, while holding tremendous promise, is not without methodological and ethical limitations that are essential to address. First, it is important to ensure external validity of programs before their use in a clinical setting. Investigators should maintain high quality data records and registry surveillance, exercise caution when evaluating others' reported AI applications, and increase transparency of the methodological conduct of current models to improve external validity and avoid propagating bias. By addressing these challenges and responsibly embracing the potential of AI, the medical field may eventually be able to harness its power to improve patient care and outcomes.
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Affiliation(s)
- Nickelas Huffman
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | | | - Shujaa T Khan
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Alison K Klika
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Matthew E Deren
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Yuxuan Jin
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Kyle N Kunze
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York
| | - Nicolas S Piuzzi
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
- Department of Biomedical Engineering, Cleveland Clinic Foundation, Cleveland, Ohio
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Salman LA, Khatkar H, Al-Ani A, Alzobi OZ, Abudalou A, Hatnouly AT, Ahmed G, Hameed S, AlAteeq Aldosari M. Reliability of artificial intelligence in predicting total knee arthroplasty component sizes: a systematic review. EUROPEAN JOURNAL OF ORTHOPAEDIC SURGERY & TRAUMATOLOGY : ORTHOPEDIE TRAUMATOLOGIE 2024; 34:747-756. [PMID: 38010443 PMCID: PMC10858112 DOI: 10.1007/s00590-023-03784-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 11/01/2023] [Indexed: 11/29/2023]
Abstract
PURPOSE This systematic review aimed to investigate the reliability of AI predictive models of intraoperative implant sizing in total knee arthroplasty (TKA). METHODS Four databases were searched from inception till July 2023 for original studies that studied the reliability of AI prediction in TKA. The primary outcome was the accuracy ± 1 size. This review was conducted per PRISMA guidelines, and the risk of bias was assessed using the MINORS criteria. RESULTS A total of four observational studies comprised of at least 34,547 patients were included in this review. A mean MINORS score of 11 out of 16 was assigned to the review. All included studies were published between 2021 and 2022, with a total of nine different AI algorithms reported. Among these AI models, the accuracy of TKA femoral component sizing prediction ranged from 88.3 to 99.7% within a deviation of one size, while tibial component sizing exhibited an accuracy ranging from 90 to 99.9% ± 1 size. CONCLUSION This study demonstrated the potential of AI as a valuable complement for planning TKA, exhibiting a satisfactory level of reliability in predicting TKA implant sizes. This predictive accuracy is comparable to that of the manual and digital templating techniques currently documented in the literature. However, future research is imperative to assess the impact of AI on patient care and cost-effectiveness. LEVEL OF EVIDENCE III PROSPERO registration number: CRD42023446868.
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Affiliation(s)
- Loay A Salman
- Department of Orthopaedic Surgery, Surgical Specialty Center, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar.
| | | | - Abdallah Al-Ani
- Office of Scientific Affairs and Research, King Hussein Cancer Center, Amman, Jordan
| | - Osama Z Alzobi
- Department of Orthopaedic Surgery, Surgical Specialty Center, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Abedallah Abudalou
- Department of Orthopaedic Surgery, Surgical Specialty Center, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Ashraf T Hatnouly
- Department of Orthopaedic Surgery, Surgical Specialty Center, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Ghalib Ahmed
- Department of Orthopaedic Surgery, Surgical Specialty Center, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Shamsi Hameed
- Department of Orthopaedic Surgery, Surgical Specialty Center, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Mohamed AlAteeq Aldosari
- Department of Orthopaedic Surgery, Surgical Specialty Center, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
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Kunze KN, Williams RJ, Ranawat AS, Pearle AD, Kelly BT, Karlsson J, Martin RK, Pareek A. Artificial intelligence (AI) and large data registries: Understanding the advantages and limitations of contemporary data sets for use in AI research. Knee Surg Sports Traumatol Arthrosc 2024; 32:13-18. [PMID: 38226678 DOI: 10.1002/ksa.12018] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 11/27/2023] [Indexed: 01/17/2024]
Affiliation(s)
- Kyle N Kunze
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, USA
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, New York, New York, USA
| | - Riley J Williams
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, USA
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, New York, New York, USA
| | - Anil S Ranawat
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, USA
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, New York, New York, USA
| | - Andrew D Pearle
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, USA
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, New York, New York, USA
| | - Bryan T Kelly
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, USA
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, New York, New York, USA
| | - Jon Karlsson
- Department of Orthopaedics, Sahlgrenska University Hospital, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden
| | - R Kyle Martin
- Department of Orthopedic Surgery, University of Minnesota, Minneapolis, Minnesota, USA
| | - Ayoosh Pareek
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, USA
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, New York, New York, USA
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Longo UG, Di Naro C, Campisi S, Casciaro C, Bandini B, Pareek A, Bruschetta R, Pioggia G, Cerasa A, Tartarisco G. Application of Machine Learning Algorithms for Prognostic Assessment in Rotator Cuff Pathologies: A Clinical Data-Based Approach. Diagnostics (Basel) 2023; 13:2915. [PMID: 37761282 PMCID: PMC10530213 DOI: 10.3390/diagnostics13182915] [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: 08/01/2023] [Revised: 08/30/2023] [Accepted: 09/04/2023] [Indexed: 09/29/2023] Open
Abstract
AIM The overall aim of this proposal is to ameliorate the care of rotator cuff (RC) tear patients by applying an innovative machine learning approach for outcome prediction after arthroscopic repair. MATERIALS AND METHODS We applied state-of-the-art machine learning algorithms to evaluate the best predictors of the outcome, and 100 RC patients were evaluated at baseline (T0), after 1 month (T1), 3 months (T2), 6 months (T3), and 1 year (T4) from surgical intervention. The outcome measure was the Costant-Murley Shoulder Score, whereas age, sex, BMI, the 36-Item Short-Form Survey, the Simple Shoulder Test, the Hospital Anxiety and Depression Scale, the American Shoulder and Elbow Surgeons Score, the Oxford Shoulder Score, and the Shoulder Pain and Disability Index were considered as predictive factors. Support vector machine (SVM), k-nearest neighbors (k-NN), naïve Bayes (NB), and random forest (RF) algorithms were employed. RESULTS Across all sessions, the classifiers demonstrated suboptimal performance when using both the complete and shrunken sets of features. Specifically, the logistic regression (LR) classifier achieved a mean accuracy of 46.5% ± 6%, while the random forest (RF) classifier achieved 51.25% ± 4%. For the shrunken set of features, LR obtained a mean accuracy of 48.5% ± 6%, and RF achieved 45.5% ± 4.5%. No statistical differences were found when comparing the performance metrics of ML algorithms. CONCLUSIONS This study underlines the importance of extending the application of AI methods to new predictors, such as neuroimaging and kinematic data, in order to better record significant shifts in RC patients' prognosis. LIMITATIONS The data quality within the cohort could represent a limitation, since certain variables, such as smoking, diabetes, and work injury, are known to have an impact on the outcome.
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Affiliation(s)
- Umile Giuseppe Longo
- Orthopaedic and Trauma Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy; (C.D.N.); (C.C.)
- Research Unit of Orthopaedic and Trauma Surgery, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
| | - Calogero Di Naro
- Orthopaedic and Trauma Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy; (C.D.N.); (C.C.)
- Research Unit of Orthopaedic and Trauma Surgery, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
| | - Simona Campisi
- Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy (CNR), 98164 Messina, Italy; (S.C.); (R.B.); (G.P.); (A.C.)
- Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
| | - Carlo Casciaro
- Orthopaedic and Trauma Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy; (C.D.N.); (C.C.)
- Research Unit of Orthopaedic and Trauma Surgery, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
| | - Benedetta Bandini
- Orthopaedic and Trauma Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy; (C.D.N.); (C.C.)
- Research Unit of Orthopaedic and Trauma Surgery, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
| | - Ayoosh Pareek
- Hospital for Special Surgery, New York, NY 10021, USA;
| | - Roberta Bruschetta
- Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy (CNR), 98164 Messina, Italy; (S.C.); (R.B.); (G.P.); (A.C.)
- Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
| | - Giovanni Pioggia
- Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy (CNR), 98164 Messina, Italy; (S.C.); (R.B.); (G.P.); (A.C.)
| | - Antonio Cerasa
- Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy (CNR), 98164 Messina, Italy; (S.C.); (R.B.); (G.P.); (A.C.)
- S’Anna Institute, 88900 Crotone, Italy
- Pharmacotechnology Documentation and Transfer Unit, Preclinical and Translational Pharmacology, Department of Pharmacy, Health Science and Nutrition, University of Calabria, 87036 Arcavacata di Rende, Italy
| | - Gennaro Tartarisco
- Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy (CNR), 98164 Messina, Italy; (S.C.); (R.B.); (G.P.); (A.C.)
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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: 1] [Impact Index Per Article: 0.5] [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.
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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.
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Imaging in Hip Arthroplasty Management-Part 1: Templating: Past, Present and Future. J Clin Med 2022; 11:jcm11185465. [PMID: 36143112 PMCID: PMC9503653 DOI: 10.3390/jcm11185465] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 09/11/2022] [Accepted: 09/15/2022] [Indexed: 11/17/2022] Open
Abstract
Hip arthroplasty is a frequently used procedure with high success rates. Its main indications are primary or secondary advanced osteoarthritis, due to acute fracture, osteonecrosis of the femoral head, and hip dysplasia. The goals of HA are to reduce pain and restore normal hip biomechanics, allowing a return to the patient’s normal activities. To reach those goals, the size of implants must suit, and their positioning must meet, quality criteria, which can be determined by preoperative imaging. Moreover, mechanical complications can be influenced by implant size and position, and could be avoided by precise preoperative templating. Templating used to rely on standard radiographs, but recently the use of EOS® imaging and CT has been growing, given the 3D approach provided by these methods. However, there is no consensus on the optimal imaging work-up, which may have an impact on the outcomes of the procedure. This article reviews the current principles of templating, the various imaging techniques used for it, as well as their advantages and drawbacks, and their expected results.
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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: 24] [Impact Index Per Article: 8.0] [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.
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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
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Lambrechts A, Wirix-Speetjens R, Maes F, Van Huffel S. Artificial Intelligence Based Patient-Specific Preoperative Planning Algorithm for Total Knee Arthroplasty. Front Robot AI 2022; 9:840282. [PMID: 35350703 PMCID: PMC8957999 DOI: 10.3389/frobt.2022.840282] [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] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 02/07/2022] [Indexed: 11/24/2022] Open
Abstract
Previous studies have shown that the manufacturer's default preoperative plans for total knee arthroplasty with patient-specific guides require frequent, time-consuming changes by the surgeon. Currently, no research has been done on predicting preoperative plans for orthopedic surgery using machine learning. Therefore, this study aims to evaluate whether artificial intelligence (AI) driven planning tools can create surgeon and patient-specific preoperative plans that require fewer changes by the surgeon. A dataset of 5409 preoperative plans, including the manufacturer's default and the plans corrected by 39 surgeons, was collected. Features were extracted from the preoperative plans that describe the implant sizes, position, and orientation in a surgeon- and patient-specific manner. Based on these features, non-linear regression models were employed to predict the surgeon's corrected preoperative plan. The average number of corrections a surgeon has to make to the preoperative plan generated using AI was reduced by 39.7% compared to the manufacturer's default plan. The femoral and tibial implant size in the manufacturer's plan was correct in 68.4% and 73.1% of the cases, respectively, while the AI-based plan was correct in 82.2% and 85.0% of the cases, respectively, compared to the surgeon approved plan. Our method successfully demonstrated the use of machine learning to create preoperative plans in a surgeon- and patient-specific manner for total knee arthroplasty.
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Affiliation(s)
- Adriaan Lambrechts
- Materialise NV, Leuven, Belgium
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | | | - Frederik Maes
- Department of Electrical Engineering (ESAT), Processing Speech and Images (PSI), KU Leuven, Leuven, Belgium
- Medical Imaging Research Center, UZ Leuven, Leuven, Belgium
| | - Sabine Van Huffel
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
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