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Zhan H, Kang X, Zhang X, Zhang Y, Wang Y, Yang J, Zhang K, Han J, Feng Z, Zhang L, Wu M, Xia Y, Jiang J. Machine Learning Models Reliably Predict Clinical Outcomes in Medial Patellofemoral Ligament Reconstruction. Arthroscopy 2024:S0749-8063(24)00556-5. [PMID: 39128684 DOI: 10.1016/j.arthro.2024.07.028] [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: 03/05/2024] [Revised: 07/17/2024] [Accepted: 07/17/2024] [Indexed: 08/13/2024]
Abstract
PURPOSE To develop the machine learning model to predict clinical outcomes following MPFLR and identify the important predictive indicators. METHODS This study included patients who underwent MPFLR from January 2018 to December 2022. The exclusion criteria were as follows: 1) concurrent bony procedures, 2) history of other knee surgeries, and 3) follow-up period of less than 12 months. Forty-two predictive models were constructed for seven clinical outcomes (failure to achieve MCID of clinical scores, return to pre-injury sports, pivoting sports, and recurrent instability) using six machine learning algorithms (Random Forest, Logistic Regression, Support Vector Machine, Decision Tree, implemented multilayer perceptron, and K-nearest neighbor). The performance of the model was evaluated using metrics such as the area under the receiver operating characteristic curve (AUC), accuracy, specificity, and sensitivity. Additionally, Shapley Additive Explanation summary plot was employed to identify the important predictive factors of the best-performing model. RESULTS A total of 218 patients met criteria. For the best-performing models in predicting failure to achieve the MCID for Lysholm, IKDC, Kujala, and Tegner scores, the AUCs and accuracies were 0.884 (good) and 87.3%, 0.859 (good) and 86.2%, 0.969 (excellent) and 97.0%, and 0.760 (fair) and 76.8%, respectively; 0.952 (excellent) and 95.2% for return to pre-injury sports; 0.756 (fair) and 75.4% for return to pivoting sports; and 0.943 (excellent) and 94.9% for recurrent instability. Low preoperative Tegner score, shorter time to surgery, and absence of severe trochlear dysplasia were significant predictors for return to pre-injury sports, while absence of severe trochlear dysplasia and patellar alta were significant predictors for return to pivoting sports. Older age, female sex, and low preoperative Lysholm score were highly predictive of recurrent instability. CONCLUSION The predictive models developed using machine learning algorithms can reliably forecast the clinical outcomes of MPFLR, particularly demonstrating excellent performance in predicting recurrent instability. LEVEL OF EVIDENCE Level III, case-control study.
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Affiliation(s)
- Hongwei Zhan
- Department of Sports Medicine, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Xin Kang
- Department of Sports Medicine, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Xiaobo Zhang
- Department of Sports Medicine, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Yuji Zhang
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China
| | - Yanming Wang
- Second School of Clinical Medicine, Lanzhou University, Lanzhou, Gansu, China
| | - Jing Yang
- Second School of Clinical Medicine, Lanzhou University, Lanzhou, Gansu, China
| | - Kun Zhang
- Department of Sports Medicine, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Jingjing Han
- Second School of Clinical Medicine, Lanzhou University, Lanzhou, Gansu, China
| | - Zhiwei Feng
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China
| | - Liang Zhang
- Department of Sports Medicine, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Meng Wu
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China
| | - Yayi Xia
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China
| | - Jin Jiang
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China.
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McBee JC, Han DY, Liu L, Ma L, Adjeroh DA, Xu D, Hu G. Assessing ChatGPT's Competency in Addressing Interdisciplinary Inquiries on Chatbot Uses in Sports Rehabilitation: Simulation Study. JMIR MEDICAL EDUCATION 2024; 10:e51157. [PMID: 39042885 DOI: 10.2196/51157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 08/21/2023] [Accepted: 07/23/2024] [Indexed: 07/25/2024]
Abstract
BACKGROUND ChatGPT showcases exceptional conversational capabilities and extensive cross-disciplinary knowledge. In addition, it can perform multiple roles in a single chat session. This unique multirole-playing feature positions ChatGPT as a promising tool for exploring interdisciplinary subjects. OBJECTIVE The aim of this study was to evaluate ChatGPT's competency in addressing interdisciplinary inquiries based on a case study exploring the opportunities and challenges of chatbot uses in sports rehabilitation. METHODS We developed a model termed PanelGPT to assess ChatGPT's competency in addressing interdisciplinary topics through simulated panel discussions. Taking chatbot uses in sports rehabilitation as an example of an interdisciplinary topic, we prompted ChatGPT through PanelGPT to role-play a physiotherapist, psychologist, nutritionist, artificial intelligence expert, and athlete in a simulated panel discussion. During the simulation, we posed questions to the panel while ChatGPT acted as both the panelists for responses and the moderator for steering the discussion. We performed the simulation using ChatGPT-4 and evaluated the responses by referring to the literature and our human expertise. RESULTS By tackling questions related to chatbot uses in sports rehabilitation with respect to patient education, physiotherapy, physiology, nutrition, and ethical considerations, responses from the ChatGPT-simulated panel discussion reasonably pointed to various benefits such as 24/7 support, personalized advice, automated tracking, and reminders. ChatGPT also correctly emphasized the importance of patient education, and identified challenges such as limited interaction modes, inaccuracies in emotion-related advice, assurance of data privacy and security, transparency in data handling, and fairness in model training. It also stressed that chatbots are to assist as a copilot, not to replace human health care professionals in the rehabilitation process. CONCLUSIONS ChatGPT exhibits strong competency in addressing interdisciplinary inquiry by simulating multiple experts from complementary backgrounds, with significant implications in assisting medical education.
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Affiliation(s)
- Joseph C McBee
- Department of Microbiology, Immunology, & Cell Biology, West Virginia University, Morgantown, WV, United States
- Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV, United States
| | - Daniel Y Han
- Department of Microbiology, Immunology, & Cell Biology, West Virginia University, Morgantown, WV, United States
| | - Li Liu
- College of Health Solutions, Arizona State University, Phoenix, AZ, United States
- Biodesign Institute, Arizona State University, Tempe, AZ, United States
| | - Leah Ma
- College of Health, Education, and Human Services, Wright State University, Dayton, OH, United States
| | - Donald A Adjeroh
- Lane Department of Computer Science & Electrical Engineering, West Virginia University, Morgantown, WV, United States
| | - Dong Xu
- Department of Electrical Engineering and Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, United States
| | - Gangqing Hu
- Department of Microbiology, Immunology, & Cell Biology, West Virginia University, Morgantown, WV, United States
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Killoughery IT, Pitsiladis YP. Olympic AI agenda: we need collaboration to achieve evolution. Br J Sports Med 2024:bjsports-2024-108667. [PMID: 39107076 DOI: 10.1136/bjsports-2024-108667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/26/2024] [Indexed: 08/09/2024]
Affiliation(s)
- Iain T Killoughery
- Department of Sport, Physical Education and Health, Hong Kong Baptist University, Hong Kong, Hong Kong
| | - Yannis P Pitsiladis
- Department of Sport, Physical Education and Health, Hong Kong Baptist University, Hong Kong, Hong Kong
- International Federation of Sports Medicine, Lausanne, Switzerland
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Palacio C, Hovorka M, Acosta M, Bautista R, Chen C, Hovorka J. Predicting factors for extremity fracture among border-fall patients using machine learning computing. Heliyon 2024; 10:e32185. [PMID: 38961975 PMCID: PMC11219316 DOI: 10.1016/j.heliyon.2024.e32185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Accepted: 05/29/2024] [Indexed: 07/05/2024] Open
Abstract
Background The factors causing the injuries sustained from falls at US-Mexican border include falls from border wall or fence, fleeing from border patrols, ejecting from vehicle, and others. This study aimed to determine the factors leading to anatomical injuries and to identify the importance of factors leading to limb fracture and internal organ injuries. Methods A total of 178 patients who sustained musculoskeletal injuries or internal organ injuries and were admitted to our hospital were included in this retrospective study. Factors indexed for analysis included demographics, comorbidities, and falling mechanic factors. Correlations between anatomical injuries and mechanical injuries were analyzed. Multilayer perceptron neural network (MPNN) was used to identify predictive factors and to stratify the importance of these factors leading to injuries. The SPSS software was used for statistical analysis and predictive factor analysis. Results The extremity fracture was associated with border wall/fence fall (p = 0.001) and fleeing (p = 0.002). The spine fracture was correlated with bridge jump/fall (p = 0.007), fence jump/fall (p = 0.026). The vehicle ejecting/MVA was correlated with head injury (P < 0.001), chest injury (P < 0.001), and abdominal injury p < 0.001). MNPP stratify the importance of factor causing injury with multiple factor considered. Conclusion The various injury factors caused different anatomical injuries. Multifactorial assessment associated with these injuries can improve the accuracy of diagnosis and develop a predictive model for clinical applications.
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Affiliation(s)
- Carlos Palacio
- South Texas Health System – McAllen Department of Trauma, McAllen, TX, 78503, USA
| | - Maximillian Hovorka
- South Texas Health System – McAllen Department of Trauma, McAllen, TX, 78503, USA
| | - Marie Acosta
- South Texas Health System – McAllen Department of Trauma, McAllen, TX, 78503, USA
| | - Ruby Bautista
- South Texas Health System – McAllen Department of Trauma, McAllen, TX, 78503, USA
| | - Chaoyang Chen
- South Texas Health System – McAllen Department of Trauma, McAllen, TX, 78503, USA
- Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI, 48201, USA
| | - John Hovorka
- South Texas Health System – McAllen Department of Trauma, McAllen, TX, 78503, USA
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Yin R, Chen H, Wang C, Qin C, Tao T, Hao Y, Wu R, Jiang Y, Gui J. Transformer-Based Multilabel Deep Learning Model Is Efficient for Detecting Ankle Lateral and Medial Ligament Injuries on Magnetic Resonance Imaging and Improving Clinicians' Diagnostic Accuracy for Rotational Chronic Ankle Instability. Arthroscopy 2024:S0749-8063(24)00409-2. [PMID: 38876447 DOI: 10.1016/j.arthro.2024.05.027] [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: 11/10/2023] [Revised: 05/11/2024] [Accepted: 05/19/2024] [Indexed: 06/16/2024]
Abstract
PURPOSE To develop a deep learning (DL) model that can simultaneously detect lateral and medial collateral ligament injuries of the ankle, aiding in the diagnosis of chronic ankle instability (CAI), and assess its impact on clinicians' diagnostic performance. METHODS DL models were developed and externally validated on retrospectively collected ankle magnetic resonance imaging (MRI) between April 2016 and March 2022 respectively at 3 centers. Included patients had confirmed diagnoses of CAI through arthroscopy, as well as individuals who had undergone MRI and physical examinations that ruled out ligament injuries. DL models were constructed based on a multilabel paradigm. A transformer-based multilabel DL model (AnkleNet) was developed and compared with 4 convolution neural network (CNN) models. Subsequently, a reader study was conducted to evaluate the impact of model assistance on clinicians when diagnosing challenging cases: identifying rotational CAI (RCAI). Diagnostic performance was assessed using area under the receiver operating characteristic curve (AUC). RESULTS Our transformer-based model achieved AUCs of 0.910 and 0.892 for detecting lateral and medial collateral ligament injury, respectively, both of which were significantly higher than those of CNN-based models (all P < .001). In terms of further CAI diagnosis, there was a macro-average AUC of 0.870 and a balanced accuracy of 0.805. The reader study indicated that incorporation with our model significantly enhanced the diagnostic accuracy of clinicians (P = .042), particularly junior clinicians, and led to a reduction in diagnostic variability. The code of the model can be accessed at https://github.com/ChiariRay/AnkleNet. CONCLUSIONS Our transformer-based model was able to detect lateral and medial collateral ligament injuries based on MRI and outperformed CNN-based models, demonstrating a promising performance in diagnosing CAI, especially patients with RCAI. CLINICAL RELEVANCE Developing such an algorithm can improve the diagnostic performance of clinicians, aiding in identifying patients who would benefit from arthroscopy, such as patients with RCAI.
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Affiliation(s)
- Rui Yin
- Department of Sports Medicine and Joint Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Hao Chen
- Department of Clinical Neuroscience, Cambridge University, Cambridge, U.K; School of Computer Science, University of Birmingham, Birmingham, U.K
| | - Changjiang Wang
- Department of Sports Medicine and Joint Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Chaoren Qin
- Department of Sports Medicine and Joint Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Tianqi Tao
- Department of Sports Medicine and Joint Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yunjia Hao
- Department of Sports Medicine and Joint Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Hand and Foot Microsurgery, Xuzhou Central Hospital, Xuzhou, China
| | - Rui Wu
- Department of Sports Medicine and Joint Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Orthopedics, The Second People's Hospital of Lianyungang, Lianyungang, China
| | - Yiqiu Jiang
- Department of Sports Medicine and Joint Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Jianchao Gui
- Department of Sports Medicine and Joint Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
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Herring SA, Putukian M, Kibler WB, LeClere L, Boyajian-O'Neill L, Day MA, Franks RR, Indelicato P, Matuszak J, Miller TL, O'Connor F, Poddar S, Svoboda SJ, Zaremski JL. Team Physician Consensus Statement: Return to Sport/Return to Play and the Team Physician: A Team Physician Consensus Statement-2023 Update. Curr Sports Med Rep 2024; 23:183-191. [PMID: 38709944 DOI: 10.1249/jsr.0000000000001169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Affiliation(s)
- Stanley A Herring
- Department of Rehabilitation Medicine, University of Washington, Seattle, WA
| | | | - W Ben Kibler
- Shoulder Center of Kentucky, Lexington Clinic, Lexington KY
| | - Lance LeClere
- Department of Orthopaedic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | | | | | | | - Peter Indelicato
- Department of Orthopedic Surgery and Sports Medicine, College of Medicine, University of Florida, Gainesville, FL
| | | | - Timothy L Miller
- Department of Orthopaedic Surgery, The Ohio State University Wexner Medical Center, Columbus, OH
| | - Fran O'Connor
- Department of Military and Emergency Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD
| | - Sourav Poddar
- Primary Sports Medicine, University of Colorado School of Medicine, Denver, CO
| | | | - Jason L Zaremski
- Department of Physical Medicine & Rehabilitation, University of Florida Health, Gainesville, FL
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7
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Herring SA, Putukian M, Kibler WB, Leclere L, Boyajian-O'Neill L, Day MA, Franks RR, Indelicato P, Matuszak J, Miller TL, O'Connor F, Poddar S, Svoboda SJ, Zaremski JL. Team Physician Consensus Statement: Return to Sport/Return to Play and the Team Physician: A Team Physician Consensus Statement-2023 Update. Med Sci Sports Exerc 2024; 56:767-775. [PMID: 38616326 DOI: 10.1249/mss.0000000000003371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Affiliation(s)
- Stanley A Herring
- Department of Rehabilitation Medicine, University of Washington, Seattle, WA
| | | | - W Ben Kibler
- Shoulder Center of Kentucky, Lexington Clinic, Lexington KY
| | - Lance Leclere
- Department of Orthopaedic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | | | | | | | - Peter Indelicato
- Department of Orthopedic Surgery and Sports Medicine, College of Medicine, University of Florida, Gainesville, FL
| | | | - Timothy L Miller
- Department of Orthopaedic Surgery, The Ohio State University Wexner Medical Center, Columbus, OH
| | - Fran O'Connor
- Department of Military and Emergency Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD
| | - Sourav Poddar
- Primary Sports Medicine, University of Colorado School of Medicine, Denver, CO
| | | | - Jason L Zaremski
- Department of Physical Medicine & Rehabilitation, University of Florida Health, Gainesville, FL
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Reis FJJ, Alaiti RK, Vallio CS, Hespanhol L. Artificial intelligence and Machine Learning approaches in sports: Concepts, applications, challenges, and future perspectives. Braz J Phys Ther 2024; 28:101083. [PMID: 38838418 PMCID: PMC11215955 DOI: 10.1016/j.bjpt.2024.101083] [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: 04/12/2023] [Revised: 04/09/2024] [Accepted: 05/08/2024] [Indexed: 06/07/2024] Open
Abstract
BACKGROUND The development and application of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare have gained attention as a promising and powerful resource to change the landscape of healthcare. The potential of these technologies for injury prediction, performance analysis, personalized training, and treatment comes with challenges related to the complexity of sports dynamics and the multidimensional aspects of athletic performance. OBJECTIVES We aimed to present the current state of AI and ML applications in sports science, specifically in the areas of injury prediction, performance enhancement, and rehabilitation. We also examine the challenges of incorporating AI and ML into sports and suggest directions for future research. METHOD We conducted a comprehensive literature review, focusing on publications related to AI and ML applications in sports. This review encompassed studies on injury prediction, performance analysis, and personalized training, emphasizing the AI and ML models applied in sports. RESULTS The findings highlight significant advancements in injury prediction accuracy, performance analysis precision, and the customization of training programs through AI and ML. However, future studies need to address challenges such as ethical considerations, data quality, interpretability of ML models, and the integration of complex data. CONCLUSION AI and ML may be useful for the prevention, detection, diagnosis, and treatment of health conditions. In this Masterclass paper, we introduce AI and ML concepts, outline recent breakthroughs in AI technologies and their applications, identify the challenges for further progress of AI systems, and discuss ethical issues, clinical and research opportunities, and future perspectives.
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Affiliation(s)
- Felipe J J Reis
- Department of Physical Therapy, Federal Institute of Rio de Janeiro, Rio de Janeiro, Brazil; Pain in Motion Research Group, Department of Physical Therapy, Human Physiology and Anatomy, Faculty of Physical Education & Physiotherapy, Vrije Universiteit Brussel, Brussels, Belgium; School of Physical and Occupational Therapy, McGill University, Montreal, Canada.
| | - Rafael Krasic Alaiti
- Nucleus of Neuroscience and Behavior and Nucleus of Applied Neuroscience, Universidade de Sao Paulo (USP), Sao Paulo, Brazil; Research, Technology, and Data Science Office, Grupo Superador, Sao Paulo, Brazil
| | - Caio Sain Vallio
- Health Innovation, Data Science, and MLOps Semantics, São Paulo, Brazil
| | - Luiz Hespanhol
- Department of Physical Therapy, Faculty of Medicine, University of Sao Paulo (USP), Sao Paulo, Brazil; Amsterdam Collaboration on Health & Safety in Sports, Department of Public and Occupational Health, Amsterdam Movement Sciences, Amsterdam University Medical Centers (UMC) location VU University Medical Center Amsterdam (VUmc), Amsterdam, the Netherlands
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Desai V. The Future of Artificial Intelligence in Sports Medicine and Return to Play. Semin Musculoskelet Radiol 2024; 28:203-212. [PMID: 38484772 DOI: 10.1055/s-0043-1778019] [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: 03/19/2024]
Abstract
Artificial intelligence (AI) has shown tremendous growth over the last decade, with the more recent development of clinical applications in health care. The ability of AI to synthesize large amounts of complex data automatically allows health care providers to access previously unavailable metrics and thus enhance and personalize patient care. These innovations include AI-assisted diagnostic tools, prediction models for each treatment pathway, and various tools for workflow optimization. The extension of AI into sports medicine is still early, but numerous AI-driven algorithms, devices, and research initiatives have delved into predicting and preventing athlete injury, aiding in injury assessment, optimizing recovery plans, monitoring rehabilitation progress, and predicting return to play.
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Affiliation(s)
- Vishal Desai
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania
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10
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Zhao Y, Coppola A, Karamchandani U, Amiras D, Gupte CM. Artificial intelligence applied to magnetic resonance imaging reliably detects the presence, but not the location, of meniscus tears: a systematic review and meta-analysis. Eur Radiol 2024:10.1007/s00330-024-10625-7. [PMID: 38386028 DOI: 10.1007/s00330-024-10625-7] [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: 12/24/2023] [Revised: 12/24/2023] [Accepted: 01/13/2024] [Indexed: 02/23/2024]
Abstract
OBJECTIVES To review and compare the accuracy of convolutional neural networks (CNN) for the diagnosis of meniscal tears in the current literature and analyze the decision-making processes utilized by these CNN algorithms. MATERIALS AND METHODS PubMed, MEDLINE, EMBASE, and Cochrane databases up to December 2022 were searched in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) statement. Risk of analysis was used for all identified articles. Predictive performance values, including sensitivity and specificity, were extracted for quantitative analysis. The meta-analysis was divided between AI prediction models identifying the presence of meniscus tears and the location of meniscus tears. RESULTS Eleven articles were included in the final review, with a total of 13,467 patients and 57,551 images. Heterogeneity was statistically significantly large for the sensitivity of the tear identification analysis (I2 = 79%). A higher level of accuracy was observed in identifying the presence of a meniscal tear over locating tears in specific regions of the meniscus (AUC, 0.939 vs 0.905). Pooled sensitivity and specificity were 0.87 (95% confidence interval (CI) 0.80-0.91) and 0.89 (95% CI 0.83-0.93) for meniscus tear identification and 0.88 (95% CI 0.82-0.91) and 0.84 (95% CI 0.81-0.85) for locating the tears. CONCLUSIONS AI prediction models achieved favorable performance in the diagnosis, but not location, of meniscus tears. Further studies on the clinical utilities of deep learning should include standardized reporting, external validation, and full reports of the predictive performances of these models, with a view to localizing tears more accurately. CLINICAL RELEVANCE STATEMENT Meniscus tears are hard to diagnose in the knee magnetic resonance images. AI prediction models may play an important role in improving the diagnostic accuracy of clinicians and radiologists. KEY POINTS • Artificial intelligence (AI) provides great potential in improving the diagnosis of meniscus tears. • The pooled diagnostic performance for artificial intelligence (AI) in identifying meniscus tears was better (sensitivity 87%, specificity 89%) than locating the tears (sensitivity 88%, specificity 84%). • AI is good at confirming the diagnosis of meniscus tears, but future work is required to guide the management of the disease.
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Affiliation(s)
- Yi Zhao
- Imperial College London School of Medicine, Exhibition Rd, South Kensington, London, SW7 2BU, UK.
| | - Andrew Coppola
- Imperial College London School of Medicine, Exhibition Rd, South Kensington, London, SW7 2BU, UK
| | | | - Dimitri Amiras
- Imperial College London School of Medicine, Exhibition Rd, South Kensington, London, SW7 2BU, UK
- Imperial College London NHS Trust, London, UK
| | - Chinmay M Gupte
- Imperial College London School of Medicine, Exhibition Rd, South Kensington, London, SW7 2BU, UK
- Imperial College London NHS Trust, London, UK
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Andriollo L, Picchi A, Sangaletti R, Perticarini L, Rossi SMP, Logroscino G, Benazzo F. The Role of Artificial Intelligence in Anterior Cruciate Ligament Injuries: Current Concepts and Future Perspectives. Healthcare (Basel) 2024; 12:300. [PMID: 38338185 PMCID: PMC10855330 DOI: 10.3390/healthcare12030300] [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: 12/31/2023] [Revised: 01/19/2024] [Accepted: 01/22/2024] [Indexed: 02/12/2024] Open
Abstract
The remarkable progress in data aggregation and deep learning algorithms has positioned artificial intelligence (AI) and machine learning (ML) to revolutionize the field of medicine. AI is becoming more and more prevalent in the healthcare sector, and its impact on orthopedic surgery is already evident in several fields. This review aims to examine the literature that explores the comprehensive clinical relevance of AI-based tools utilized before, during, and after anterior cruciate ligament (ACL) reconstruction. The review focuses on current clinical applications and future prospects in preoperative management, encompassing risk prediction and diagnostics; intraoperative tools, specifically navigation, identifying complex anatomic landmarks during surgery; and postoperative applications in terms of postoperative care and rehabilitation. Additionally, AI tools in educational and training settings are presented. Orthopedic surgeons are showing a growing interest in AI, as evidenced by the applications discussed in this review, particularly those related to ACL injury. The exponential increase in studies on AI tools applicable to the management of ACL tears promises a significant future impact in its clinical application, with growing attention from orthopedic surgeons.
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Affiliation(s)
- Luca Andriollo
- Robotic Prosthetic Surgery Unit—Sports Traumatology Unit, Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy; (R.S.); (L.P.); (S.M.P.R.); (F.B.)
- Department of Orthopedics, Catholic University of the Sacred Heart, 00168 Rome, Italy
| | - Aurelio Picchi
- Unit of Orthopedics, Department of Life, Health and Environmental Sciences, University of L’Aquila, 67100 L’Aquila, Italy; (A.P.); (G.L.)
| | - Rudy Sangaletti
- Robotic Prosthetic Surgery Unit—Sports Traumatology Unit, Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy; (R.S.); (L.P.); (S.M.P.R.); (F.B.)
| | - Loris Perticarini
- Robotic Prosthetic Surgery Unit—Sports Traumatology Unit, Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy; (R.S.); (L.P.); (S.M.P.R.); (F.B.)
| | - Stefano Marco Paolo Rossi
- Robotic Prosthetic Surgery Unit—Sports Traumatology Unit, Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy; (R.S.); (L.P.); (S.M.P.R.); (F.B.)
| | - Giandomenico Logroscino
- Unit of Orthopedics, Department of Life, Health and Environmental Sciences, University of L’Aquila, 67100 L’Aquila, Italy; (A.P.); (G.L.)
| | - Francesco Benazzo
- Robotic Prosthetic Surgery Unit—Sports Traumatology Unit, Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy; (R.S.); (L.P.); (S.M.P.R.); (F.B.)
- Biomedical Sciences Area, IUSS University School for Advanced Studies, 27100 Pavia, Italy
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Zsidai B, Hilkert AS, Kaarre J, Narup E, Senorski EH, Grassi A, Ley C, Longo UG, Herbst E, Hirschmann MT, Kopf S, Seil R, Tischer T, Samuelsson K, Feldt R. A practical guide to the implementation of AI in orthopaedic research - part 1: opportunities in clinical application and overcoming existing challenges. J Exp Orthop 2023; 10:117. [PMID: 37968370 PMCID: PMC10651597 DOI: 10.1186/s40634-023-00683-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 10/21/2023] [Indexed: 11/17/2023] Open
Abstract
Artificial intelligence (AI) has the potential to transform medical research by improving disease diagnosis, clinical decision-making, and outcome prediction. Despite the rapid adoption of AI and machine learning (ML) in other domains and industry, deployment in medical research and clinical practice poses several challenges due to the inherent characteristics and barriers of the healthcare sector. Therefore, researchers aiming to perform AI-intensive studies require a fundamental understanding of the key concepts, biases, and clinical safety concerns associated with the use of AI. Through the analysis of large, multimodal datasets, AI has the potential to revolutionize orthopaedic research, with new insights regarding the optimal diagnosis and management of patients affected musculoskeletal injury and disease. The article is the first in a series introducing fundamental concepts and best practices to guide healthcare professionals and researcher interested in performing AI-intensive orthopaedic research studies. The vast potential of AI in orthopaedics is illustrated through examples involving disease- or injury-specific outcome prediction, medical image analysis, clinical decision support systems and digital twin technology. Furthermore, it is essential to address the role of human involvement in training unbiased, generalizable AI models, their explainability in high-risk clinical settings and the implementation of expert oversight and clinical safety measures for failure. In conclusion, the opportunities and challenges of AI in medicine are presented to ensure the safe and ethical deployment of AI models for orthopaedic research and clinical application. Level of evidence IV.
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Affiliation(s)
- Bálint Zsidai
- Sahlgrenska Sports Medicine Center, Gothenburg, Sweden.
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
| | - Ann-Sophie Hilkert
- Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Medfield Diagnostics AB, Gothenburg, Sweden
| | - Janina Kaarre
- Sahlgrenska Sports Medicine Center, Gothenburg, Sweden
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Orthopaedic Surgery, UPMC Freddie Fu Sports Medicine Center, University of Pittsburgh, Pittsburgh, USA
| | - Eric Narup
- Sahlgrenska Sports Medicine Center, Gothenburg, Sweden
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Eric Hamrin Senorski
- Sahlgrenska Sports Medicine Center, Gothenburg, Sweden
- Department of Health and Rehabilitation, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Sportrehab Sports Medicine Clinic, Gothenburg, Sweden
| | - Alberto Grassi
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- IIa Clinica Ortopedica E Traumatologica, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
| | - Christophe Ley
- Department of Mathematics, University of Luxembourg, Esch-Sur-Alzette, Luxembourg
| | - Umile Giuseppe Longo
- Department of Orthopaedic and Trauma Surgery, Campus Bio-Medico University, Rome, Italy
| | - Elmar Herbst
- Department of Trauma, Hand and Reconstructive Surgery, University Hospital Münster, Münster, Germany
| | - Michael T Hirschmann
- Department of Orthopedic Surgery and Traumatology, Head Knee Surgery and DKF Head of Research, Kantonsspital Baselland, 4101, Bruderholz, Switzerland
| | - Sebastian Kopf
- Center of Orthopaedics and Traumatology, University Hospital Brandenburg a.d.H., Brandenburg Medical School Theodor Fontane, 14770, Brandenburg a.d.H., Germany
- Faculty of Health Sciences Brandenburg, Brandenburg Medical School Theodor Fontane, 14770, Brandenburg a.d.H., Germany
| | - Romain Seil
- Department of Orthopaedic Surgery, Centre Hospitalier Luxembourg and Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Thomas Tischer
- Clinic for Orthopaedics and Trauma Surgery, Malteser Waldkrankenhaus St. Marien, Erlangen, Germany
| | - Kristian Samuelsson
- Sahlgrenska Sports Medicine Center, Gothenburg, Sweden
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Orthopaedics, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Robert Feldt
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
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Abou Al Ardat B, Nyland J, Creath R, Murphy T, Narayanan R, Onks C. Micro-doppler radar to evaluate risk for musculoskeletal injury: Protocol for a case-control study with gold standard comparison. PLoS One 2023; 18:e0292675. [PMID: 37815998 PMCID: PMC10564143 DOI: 10.1371/journal.pone.0292675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Accepted: 09/26/2023] [Indexed: 10/12/2023] Open
Abstract
BACKGROUND Beyond causing significant morbidity and cost, musculoskeletal injuries (MSKI) are among the most common reasons for primary care visits. A validated injury risk assessment tool for MSKI is conspicuously absent from current care. While motion capture (MC) systems are the current gold standard for assessing human motion, their disadvantages include large size, non-portability, high cost, and limited spatial resolution. As an alternative we introduce the Micro Doppler Radar (MDR); in contrast with MC, it is small, portable, inexpensive, and has superior spatial resolution capabilities. While Phase 1 testing has confirmed that MDR can identify individuals at high risk for MSKI, Phase 2 testing is still needed. Our aims are to 1) Use MDR technology and MC to identify individuals at high-risk for MSKI 2) Evaluate whether MDR has diagnostic accuracy superior to MC 3) Develop MDR algorithms that enhance accuracy and enable automation. METHODS AND FINDINGS A case control study will compare the movement patterns of 125 ACL reconstruction patients to 125 healthy controls. This study was reviewed and approved by the Pennsylvania State University Human Research Protection Program (HRPP) on May 18, 2022, and the IRB approval number is STUDY00020118. The ACL group is used as a model for a "high risk" population as up to 24% will have a repeat surgery within 2 years. An 8-camera Motion Analysis MC system with Cortex 8 software to collect MC data. Components for the radar technology will be purchased, assembled, and packaged. A micro-doppler signature projection algorithm will determine correct classification of ACL versus healthy control. Our previously tested algorithm for processing the MDR data will be used to identify the two groups. Discrimination, sensitivity and specificity will be calculated to compare the accuracy of MDR to MC in identifying the two groups. CONCLUSIONS We describe the rationale and methodology of a case-control study using novel MDR technology to detect individuals at high-risk for MSKI. We expect this novel approach to exhibit superior accuracy than the current gold standard. Future translational studies will determine utility in the context of clinical primary care.
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Affiliation(s)
- Bilal Abou Al Ardat
- Pennsylvania State University College of Medicine, Hershey, PA, United States of America
| | - Jennifer Nyland
- Department of Neural and Behavioral Sciences, Penn State College of Medicine, Hershey, PA, United States of America
| | - Robert Creath
- Exercise Science Department, Lebanon Valley College, Annville, PA, United States of America
| | - Terrence Murphy
- Pennsylvania State University College of Medicine, Hershey, PA, United States of America
| | - Ram Narayanan
- Pennsylvania State University College of Engineering, The Pennsylvania State University, University Park, PA, United States of America
| | - Cayce Onks
- Pennsylvania State University College of Medicine, Hershey, PA, United States of America
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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.
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Affiliation(s)
| | | | | | | | | | | | | | - Prem N Ramkumar
- Hospital for Special Surgery, New York, New York; Long Beach Orthopaedic Institute, Long Beach, California
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McBee JC, Han DY, Liu L, Ma L, Adjeroh DA, Xu D, Hu G. Interdisciplinary Inquiry via PanelGPT: Application to Explore Chatbot Application in Sports Rehabilitation. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.23.23292452. [PMID: 37546795 PMCID: PMC10402232 DOI: 10.1101/2023.07.23.23292452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Background ChatGPT showcases exceptional conversational capabilities and extensive cross-disciplinary knowledge. In addition, it possesses the ability to perform multiple roles within a single chat session. This unique multi-role-playing feature positions ChatGPT as a promising tool to explore interdisciplinary subjects. Objective The study intended to guide ChatGPT for interdisciplinary exploration through simulated panel discussions. As a proof-of-concept, we employed this method to evaluate the advantages and challenges of using chatbots in sports rehabilitation. Methods We proposed a model termed PanelGPT to explore ChatGPTs' knowledge graph on interdisciplinary topics through simulated panel discussions. Applied to "chatbots in sports rehabilitation", ChatGPT role-played both the moderator and panelists, which included a physiotherapist, psychologist, nutritionist, AI expert, and an athlete. We act as the audience posed questions to the panel, with ChatGPT acting as both the panelists for responses and the moderator for hosting the discussion. We performed the simulation using the ChatGPT-4 model and evaluated the responses with existing literature and human expertise. Results Each simulation mimicked a real-life panel discussion: The moderator introduced the panel and posed opening/closing questions, to which all panelists responded. The experts engaged with each other to address inquiries from the audience, primarily from their respective fields of expertise. By tackling questions related to education, physiotherapy, physiology, nutrition, and ethical consideration, the discussion highlighted benefits such as 24/7 support, personalized advice, automated tracking, and reminders. It also emphasized the importance of user education and identified challenges such as limited interaction modes, inaccuracies in emotion-related advice, assurance on data privacy and security, transparency in data handling, and fairness in model training. The panelists reached a consensus that chatbots are designed to assist, not replace, human healthcare professionals in the rehabilitation process. Conclusions Compared to a typical conversation with ChatGPT, the multi-perspective approach of PanelGPT facilitates a comprehensive understanding of an interdisciplinary topic by integrating insights from experts with complementary knowledge. Beyond addressing the exemplified topic of chatbots in sports rehabilitation, the model can be adapted to tackle a wide array of interdisciplinary topics within educational, research, and healthcare settings.
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Affiliation(s)
- Joseph C. McBee
- Department of Microbiology, Immunology & Cell Biology, West Virginia University, Morgantown, WV 26506, USA
- Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV 26506, USA
| | - Daniel Y. Han
- Department of Microbiology, Immunology & Cell Biology, West Virginia University, Morgantown, WV 26506, USA
| | - Li Liu
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- Biodesign Institute, Arizona State University, Tempe, AZ, 85281 USA
| | - Leah Ma
- College of Health, Education, and Human Services, Wright State University, Dayton, OH 45345, USA
| | - Donald A. Adjeroh
- Lane Department of Computer Science & Electrical Engineering, West Virginia University, Morgantown, WV 26506, USA
| | - Dong Xu
- Department of Electrical Engineer and Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO65211, USA
| | - Gangqing Hu
- Department of Microbiology, Immunology & Cell Biology, West Virginia University, Morgantown, WV 26506, USA
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Fayed AM, Mansur NSB, de Carvalho KA, Behrens A, D'Hooghe P, de Cesar Netto C. Artificial intelligence and ChatGPT in Orthopaedics and sports medicine. J Exp Orthop 2023; 10:74. [PMID: 37493985 PMCID: PMC10371934 DOI: 10.1186/s40634-023-00642-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 07/18/2023] [Indexed: 07/27/2023] Open
Abstract
Artificial intelligence (AI) is looked upon nowadays as the potential major catalyst for the fourth industrial revolution. In the last decade, AI use in Orthopaedics increased approximately tenfold. Artificial intelligence helps with tracking activities, evaluating diagnostic images, predicting injury risk, and several other uses. Chat Generated Pre-trained Transformer (ChatGPT), which is an AI-chatbot, represents an extremely controversial topic in the academic community. The aim of this review article is to simplify the concept of AI and study the extent of AI use in Orthopaedics and sports medicine literature. Additionally, the article will also evaluate the role of ChatGPT in scientific research and publications.Level of evidence: Level V, letter to review.
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Affiliation(s)
- Aly M Fayed
- Department of Orthopaedics and Rehabilitation, University of Iowa Hospitals and Clinics, Iowa City, IA, USA.
| | | | - Kepler Alencar de Carvalho
- Department of Orthopaedics and Rehabilitation, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
| | - Andrew Behrens
- Department of Orthopaedics and Rehabilitation, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
| | - Pieter D'Hooghe
- Aspetar Orthopedic and Sports Medicine Hospital, Doha, Qatar
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Salimi M, Parry JA, Shahrokhi R, Mosalamiaghili S. Application of artificial intelligence in trauma orthopedics: Limitation and prospects. World J Clin Cases 2023; 11:4231-4240. [PMID: 37449222 PMCID: PMC10337008 DOI: 10.12998/wjcc.v11.i18.4231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 04/23/2023] [Accepted: 05/08/2023] [Indexed: 06/26/2023] Open
Abstract
The varieties and capabilities of artificial intelligence and machine learning in orthopedic surgery are extensively expanding. One promising method is neural networks, emphasizing big data and computer-based learning systems to develop a statistical fracture-detecting model. It derives patterns and rules from outstanding amounts of data to analyze the probabilities of different outcomes using new sets of similar data. The sensitivity and specificity of machine learning in detecting fractures vary from previous studies. AI may be most promising in the diagnosis of less-obvious fractures that are more commonly missed. Future studies are necessary to develop more accurate and effective detection models that can be used clinically.
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Affiliation(s)
- Maryam Salimi
- Department of Orthopaedic Surgery, Denver Health Medical Center, Denver, CO 80215, United States
| | - Joshua A Parry
- Department of Orthopaedic Surgery, Denver Health Medical Center, Denver, CO 80215, United States
| | - Raha Shahrokhi
- Student Research Committee, Shiraz University of Medical Sciences, Shiraz 7138433608, Iran
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Cheng K, Guo Q, He Y, Lu Y, Xie R, Li C, Wu H. Artificial Intelligence in Sports Medicine: Could GPT-4 Make Human Doctors Obsolete? Ann Biomed Eng 2023:10.1007/s10439-023-03213-1. [PMID: 37097528 DOI: 10.1007/s10439-023-03213-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 04/17/2023] [Indexed: 04/26/2023]
Abstract
Sports medicine, an essential branch of orthopedics, focuses on preserving, restoring, improving, and rebuilding the function of the human motor system. As a thriving interdisciplinary field, sports medicine attracts not only the interest of the orthopedic community, but also artificial intelligence (AI). In this study, our team summarized the potential applications of GPT-4 in sports medicine including diagnostic imaging, exercise prescription, medical supervision, surgery treatment, sports nutrition, and science research. In our opinion, it is impossible that GPT-4 could make sports physicians obsolete. Instead, it could become an indispensable scientific assistant for sport doctors in future.
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Affiliation(s)
- Kunming Cheng
- Department of Intensive Care Unit, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Qiang Guo
- Department of Orthopedics, Baodi Clinical College of Tianjin Medical University, Tianjin, China
| | - Yongbin He
- School of Sport Medicine and Rehabilitation, Beijing Sport University, Beijing, China
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yanqiu Lu
- Department of Intensive Care Unit, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Ruijie Xie
- Department of Microsurgery, The Affiliated Nanhua Hospital, Hengyang Medical School, University of South China, Hengyang, China.
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg, Germany.
| | - Cheng Li
- Department of Orthopaedic Surgery, Beijing Jishuitan Hospital, Fourth Clinical College of Peking University, Beijing, China.
- Center for Musculoskeletal Surgery (CMSC), Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt University of Berlin, and Berlin Institute of Health, Berlin, Germany.
| | - Haiyang Wu
- Department of Graduate School, Tianjin Medical University, Tianjin, China.
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, USA.
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Ye Z, Zhang T, Wu C, Qiao Y, Su W, Chen J, Xie G, Dong S, Xu J, Zhao J. Predicting the Objective and Subjective Clinical Outcomes of Anterior Cruciate Ligament Reconstruction: A Machine Learning Analysis of 432 Patients. Am J Sports Med 2022; 50:3786-3795. [PMID: 36285651 DOI: 10.1177/03635465221129870] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
BACKGROUND Sports levels, baseline patient-reported outcome measures (PROMs), and surgical procedures are correlated with the outcomes of anterior cruciate ligament reconstruction (ACLR). Machine learning may be superior to conventional statistical methods in making repeatable and accurate predictions. PURPOSE To identify the best-performing machine learning models for predicting the objective and subjective clinical outcomes of ACLR and to determine the most important predictors. STUDY DESIGN Case-control study; Level of evidence, 3. METHODS A total of 432 patients who underwent anatomic double-bundle ACLR with hamstring tendon autograft between January 2010 and February 2019 were included in the machine learning analysis. A total of 15 predictive variables and 6 outcome variables were selected to validate the logistic regression, Gaussian naïve Bayes machine, random forest, Extreme Gradient Boosting (XGBoost), isotonically calibrated XGBoost, and sigmoid calibrated XGBoost models. For each clinical outcome, the best-performing model was determined using the area under the receiver operating characteristic curve (AUC), whereas the importance and direction of each predictive variable were demonstrated in a Shapley Additive Explanations summary plot. RESULTS The AUC and accuracy of the best-performing model, respectively, were 0.944 (excellent) and 98.6% for graft failure; 0.920 (excellent) and 91.4% for residual laxity; 0.930 (excellent) and 91.0% for failure to achieve the minimal clinically important difference (MCID) of the Lysholm score; 0.942 (excellent) and 95.1% for failure to achieve the MCID of the International Knee Documentation Committee (IKDC) score; 0.773 (fair) and 70.5% for return to preinjury sports; and 0.777 (fair) and 69.2% for return to pivoting sports. Medial meniscal resection, participation in competitive sports, and steep posterior tibial slope were top predictors of graft failure, whereas high-grade preoperative knee laxity, long follow-up period, and participation in competitive sports were top predictors of residual laxity. High preoperative Lysholm and IKDC scores were highly predictive of not achieving the MCIDs of PROMs. Young age, male sex, high preoperative IKDC score, and large graft diameter were important predictors of return to preinjury or pivoting sports. CONCLUSION Machine learning analysis can provide reliable predictions for the objective and subjective clinical outcomes (graft failure, residual laxity, PROMs, and return to sports) of ACLR. Patient-specific evaluation and decision making are recommended before and after surgery.
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Affiliation(s)
- Zipeng Ye
- Department of Sports Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tianlun Zhang
- Department of Sports Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chenliang Wu
- Department of Sports Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yi Qiao
- Department of Sports Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wei Su
- Department of Sports Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiebo Chen
- Department of Sports Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Guoming Xie
- Department of Sports Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shikui Dong
- Department of Sports Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Junjie Xu
- Department of Sports Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jinzhong Zhao
- Department of Sports Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Huebner M, Ma W. Health challenges and acute sports injuries restrict weightlifting training of older athletes. BMJ Open Sport Exerc Med 2022; 8:e001372. [PMID: 35813126 PMCID: PMC9214356 DOI: 10.1136/bmjsem-2022-001372] [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] [Accepted: 06/11/2022] [Indexed: 11/20/2022] Open
Abstract
Objectives To quantify acute injuries sustained during weightlifting that result in training restrictions and identify potential risk factors or preventative factors in Master athletes and to evaluate potentially complex interactions of age, sex, health-related and training-related predictors of injuries with machine learning (ML) algorithms. Methods A total of 976 Masters weightlifters from Australia, Canada, Europe and the USA, ages 35–88 (51.1% women), completed an online survey that included questions on weightlifting injuries, chronic diseases, sport history and training practices. Ensembles of ML algorithms were used to identify factors associated with acute weightlifting injuries and performance of the prediction models was evaluated. In addition, a subgroup of variables selected by six experts were entered into a logistic regression model to estimate the likelihood of an injury. Results The accuracy of ML models predicting injuries ranged from 0.727 to 0.876 for back, hips, knees and wrists, but were less accurate (0.644) for shoulder injuries. Male Master athletes had a higher prevalence of weightlifting injuries than female Master athletes, ranging from 12% to 42%. Chronic inflammation or osteoarthritis were common among both men and women. This was associated with an increase in acute injuries. Conclusions Training-specific variables, such as choices of training programmes or nutrition programmes, may aid in preventing acute injuries. ML models can identify potential risk factors or preventative measures for sport injuries.
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Affiliation(s)
- Marianne Huebner
- Department of Statistics and Probability, Michigan State University, East Lansing, Michigan, USA
- Department of Kinesiology, Michigan State University, East Lansing, Michigan, USA
| | - Wenjuan Ma
- Center for Statistical Training and Consulting, Michigan State University, East Lansing, Michigan, USA
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21
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Iyengar KP, Zaw Pe E, Jalli J, Shashidhara MK, Jain VK, Vaish A, Vaishya R. Industry 5.0 technology capabilities in Trauma and Orthopaedics. J Orthop 2022; 32:125-132. [DOI: 10.1016/j.jor.2022.06.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 05/16/2022] [Accepted: 06/01/2022] [Indexed: 12/29/2022] Open
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Kunze KN, Polce EM, Clapp IM, Alter T, Nho SJ. Association Between Preoperative Patient Factors and Clinically Meaningful Outcomes After Hip Arthroscopy for Femoroacetabular Impingement Syndrome: A Machine Learning Analysis. Am J Sports Med 2022; 50:746-756. [PMID: 35006010 DOI: 10.1177/03635465211067546] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
BACKGROUND The International Hip Outcome Tool 12-Item Questionnaire (IHOT-12) has been proposed as a more appropriate outcome assessment for hip arthroscopy populations. The extent to which preoperative patient factors predict achieving clinically meaningful outcomes among patients undergoing hip arthroscopy for femoroacetabular impingement syndrome (FAIS) remains poorly understood. PURPOSE To determine the predictive relationship of preoperative imaging, patient-reported outcome measures, and patient demographics with achievement of the minimal clinically important difference (MCID), Patient Acceptable Symptom State (PASS), and substantial clinical benefit (SCB) for the IHOT-12 at a minimum of 2 years postoperatively. STUDY DESIGN Case-control study; Level of evidence, 3. METHODS Data were analyzed for consecutive patients who underwent hip arthroscopy for FAIS between 2012 and 2018 and completed the IHOT-12 preoperatively and at a minimum of 2 years postoperatively. Fifteen novel machine learning algorithms were developed using 47 potential demographic, clinical, and radiographic predictors. Model performance was evaluated with discrimination, calibration, decision-curve analysis and the brier score. RESULTS A total of 859 patients were identified, with 685 (79.7%) achieving the MCID, 535 (62.3%) achieving the PASS, and 498 (58.0%) achieving the SCB. For predicting the MCID, discrimination for the best-performing models ranged from fair to excellent (area under the curve [AUC], 0.69-0.89), although calibration was excellent (calibration intercept and slopes: -0.06 to 0.02 and 0.24 to 0.85, respectively). For predicting the PASS, discrimination for the best-performing models ranged from fair to excellent (AUC, 0.63-0.81), with excellent calibration (calibration intercept and slopes: 0.03-0.18 and 0.52-0.90, respectively). For predicting the SCB, discrimination for the best-performing models ranged from fair to good (AUC, 0.61-0.77), with excellent calibration (calibration intercept and slopes: -0.08 to 0.00 and 0.56 to 1.02, respectively). Thematic predictors for failing to achieve the MCID, PASS, and SCB were presence of back pain, anxiety/depression, chronic symptom duration, preoperative hip injections, and increasing body mass index (BMI). Specifically, thresholds associated with lower likelihood to achieve a clinically meaningful outcome were preoperative Hip Outcome Score-Activities of Daily Living <55, preoperative Hip Outcome Score-Sports Subscale >55.6, preoperative IHOT-12 score ≥48.5, preoperative modified Harris Hip Score ≤51.7, age >41 years, BMI ≥27, and preoperative α angle >76.6°. CONCLUSION We developed novel machine learning algorithms that leveraged preoperative demographic, clinical, and imaging-based features to reliably predict clinically meaningful improvement after hip arthroscopy for FAIS. Despite consistent improvements after hip arthroscopy, meaningful improvements are negatively influenced by greater BMI, back pain, chronic symptom duration, preoperative mental health, and use of hip corticosteroid injections.
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Affiliation(s)
- Kyle N Kunze
- Department of Orthopedic Surgery, Hospital for Special Surgery, New York, New York, USA
| | - Evan M Polce
- University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Ian Michael Clapp
- Department of Orthopedic Surgery, Division of Sports Medicine, Rush University Medical Center, Chicago, Illinois, USA
| | - Thomas Alter
- Department of Orthopedic Surgery, Division of Sports Medicine, Rush University Medical Center, Chicago, Illinois, USA
| | - Shane J Nho
- Department of Orthopedic Surgery, Division of Sports Medicine, Rush University Medical Center, Chicago, Illinois, USA
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Machine Learning Model Identifies Increased Operative Time and Greater BMI as Predictors for Overnight Admission After Outpatient Hip Arthroscopy. Arthrosc Sports Med Rehabil 2022; 3:e1981-e1990. [PMID: 34977657 PMCID: PMC8689272 DOI: 10.1016/j.asmr.2021.10.001] [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: 04/27/2021] [Accepted: 10/06/2021] [Indexed: 01/05/2023] Open
Abstract
Purpose The purposes of this study were to identify patient characteristics and risk factors for overnight admission following outpatient hip arthroscopy and to develop a machine learning algorithm that can effectively identify patients requiring admission following elective hip arthroscopy. Methods A retrospective review of a prospectively collected national surgical outcomes database was performed to identify patients who underwent elective outpatient hip arthroscopy from 2006 to 2018. Patients admitted overnight postoperatively were identified as those with length of stay of 1 or more days. Models were generated using random forest (RF), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), elastic net penalized logistic regression, and an additional model was produced as a weighted ensemble of the four final algorithms. Results Overall, 1,276 patients were included. The median age was 43 years, and 64.2% (819) were female. Of the included patients, 109 (8.5%) required an overnight stay following elective outpatient hip arthroscopy. The most important factors for inpatient admission were increasing operative time, general anesthesia, age extremes, male gender, greater body mass index (BMI), American Society of Anesthesiologists classification >1, and the following preoperative lab values outside of normal ranges: sodium, platelet count, hematocrit, and leukocyte count. The ensemble model achieved the best performance based on discrimination assessed via internal validation (area under the curve = .71), calibration, and decision curve analysis. The model was integrated into a Web-based open-access application able to provide both personalized predictions and explanations. Conclusion A machine learning algorithm developed based on preoperative features identified increasing operative time, age extremes, greater BMI, sodium, hematocrit, platelets, and leukocyte count as the most important variables associated with inpatient admission with fair validity.
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