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Eng E, Mowers C, Sachdev D, Yerke-Hansen P, Jackson GR, Knapik DM, Sabesan VJ. Despite an Advanced Readability for the General Population, ChatGPT-3.5 Can Effectively Supplement Patient-Related Information Provided by the Treating Surgeon Regarding Common Questions About Rotator Cuff Repair. Arthroscopy 2024:S0749-8063(24)00369-4. [PMID: 38777000 DOI: 10.1016/j.arthro.2024.05.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 04/23/2024] [Accepted: 05/02/2024] [Indexed: 05/25/2024]
Abstract
PURPOSE The purpose of this study was to investigate the accuracy of ChatGPT's responses to frequently asked questions (FAQ) prior to rotator cuff repair surgery. METHODS The ten most common FAQs related to rotator cuff repair were compiled from four institution websites. Questions were then input into ChatGPT-3.5 in one session. The provided ChatGPT-3.5 responses were analyzed by two orthopedic surgeons for reliability, quality, and readability using The Journal of the American Medical Association (JAMA) Benchmark criteria, the DISCERN score, and the Flesch-Kincaid Grade Level. RESULTS The JAMA Benchmark criteria score was 0, indicating the absence of reliable source material citations. The mean Flesch-Kincaid Grade Level was 13.4 (range, 11.2-15.0). The mean DISCERN score was 43.4 (range, 36-51) indicating that the quality of the responses overall was considered fair. All responses cited making final decision-making to be made with the treating physician. CONCLUSION ChatGPT-3.5 provided substandard patient-related information in alignment with recommendations from the treating surgeon regarding common questions around rotator cuff repair surgery. Additionally, the responses lacked reliable source material citations and the readability of the responses was relatively advanced with a complex language style. CLINICAL RELEVANCE The findings of this study suggest that ChatGPT-3.5 may not effectively supplement patient-related information in the context of recommendations provided by the treating surgeon prior to rotator cuff repair surgery.
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Affiliation(s)
- Emma Eng
- Charles E. Schmidt College of Medicine at Florida Atlantic University, Boca Raton, FL 33431, USA
| | - Colton Mowers
- Rush University Medical College, Chicago, IL 60612, USA
| | | | - Payton Yerke-Hansen
- Department of Orthopaedic Surgery, Louisiana State University Health -Shreveport, Shreveport, LA 71103, USA
| | - Garrett R Jackson
- Department of Orthopaedic Surgery, University of Missouri, Columbia, MO 65212, USA.
| | - Derrick M Knapik
- Department of Orthopaedic Surgery, Washington University and Barnes-Jewish Orthopedic Center, Chesterfield, MO, USA
| | - Vani J Sabesan
- HCA JFK/University of Miami Miller School of Medicine Orthopaedic Residency Program Palm Beach, FL, Lake Worth, FL 33463, USA
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Liu H, Wang X, Song X, Han B, Li C, Du F, Zhang H. A multiview deep learning-based prediction pipeline augmented with confident learning can improve performance in determining knee arthroplasty candidates. Knee Surg Sports Traumatol Arthrosc 2024. [PMID: 38713857 DOI: 10.1002/ksa.12221] [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: 02/05/2024] [Revised: 04/15/2024] [Accepted: 04/17/2024] [Indexed: 05/09/2024]
Abstract
PURPOSE Preoperative prudent patient selection plays a crucial role in knee osteoarthritis management but faces challenges in appropriate referrals such as total knee arthroplasty (TKA), unicompartmental knee arthroplasty (UKA) and nonoperative intervention. Deep learning (DL) techniques can build prediction models for treatment decision-making. The aim is to develop and evaluate a knee arthroplasty prediction pipeline using three-view X-rays to determine the suitable candidates for TKA, UKA or are not arthroplasty candidates. METHODS A study was conducted using three-view (anterior-posterior, lateral and patellar) X-rays and surgical data of patients undergoing TKA, UKA or nonarthroplasty interventions from sites A and B. Data from site A were used to derive and validate models. Data from site B were used as external test set. A DL pipeline combining YOLOv3 and ResNet-18 with confident learning (CL) was developed. Multiview Convolutional Neural Network, EfficientNet-b4, ResNet-101 and the proposed model without CL were also trained and tested. The models were evaluated using metrics such as area under the receiver operating characteristic curve (AUC), accuracy, precision, specificity, sensitivity and F1 score. RESULTS The data set comprised a total of 1779 knees. Of which 1645 knees were from site A as a derivation set and an internal validation cohort. The external validation cohort consisted of 134 knees. The internal validation cohort demonstrated superior performance for the proposed model augmented with CL, achieving an AUC of 0.94 and an accuracy of 85.9%. External validation further confirmed the model's generalisation, with an AUC of 0.93 and an accuracy of 82.1%. Comparative analysis with other neural network models showed the proposed model's superiority. CONCLUSIONS The proposed DL pipeline, integrating YOLOv3, ResNet-18 and CL, provides accurate predictions for knee arthroplasty candidates based on three-view X-rays. This prediction model could be useful in performing decision making for the type of arthroplasty procedure in an automated fashion. LEVEL OF EVIDENCE Level III, diagnostic study.
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Affiliation(s)
- Hongzhi Liu
- Department of Orthopaedics, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Xiaoyao Wang
- Department of Industrial & Manufacturing Systems Engineering, School of Mechanical Engineering & Automation, Beihang University, Beijing, China
| | - Xinqiu Song
- Department of General Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Bing Han
- Department of Orthopaedics, Weifang Hospital of Traditional Chinese Medicine, Weifang, Shandong, China
| | - Chuiqing Li
- Department of Orthopaedics, Weifang Hospital of Traditional Chinese Medicine, Weifang, Shandong, China
| | - Fuzhou Du
- Department of Industrial & Manufacturing Systems Engineering, School of Mechanical Engineering & Automation, Beihang University, Beijing, China
| | - Hongmei Zhang
- Department of Orthopaedics, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, China
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Velasquez Garcia A, Bukowiec LG, Yang L, Nishikawa H, Fitzsimmons JS, Larson AN, Taunton MJ, Sanchez-Sotelo J, O'Driscoll SW, Wyles CC. Artificial intelligence-based three-dimensional templating for total joint arthroplasty planning: a scoping review. INTERNATIONAL ORTHOPAEDICS 2024; 48:997-1010. [PMID: 38224400 DOI: 10.1007/s00264-024-06088-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 01/03/2024] [Indexed: 01/16/2024]
Abstract
PURPOSE The purpose of this review is to evaluate the current status of research on the application of artificial intelligence (AI)-based three-dimensional (3D) templating in preoperative planning of total joint arthroplasty. METHODS This scoping review followed the PRISMA, PRISMA-ScR guidelines, and five stage methodological framework for scoping reviews. Studies of patients undergoing primary or revision joint arthroplasty surgery that utilised AI-based 3D templating for surgical planning were included. Outcome measures included dataset and model development characteristics, AI performance metrics, and time performance. After AI-based 3D planning, the accuracy of component size and placement estimation and postoperative outcome data were collected. RESULTS Nine studies satisfied inclusion criteria including a focus on computed tomography (CT) or magnetic resonance imaging (MRI)-based AI templating for use in hip or knee arthroplasty. AI-based 3D templating systems reduced surgical planning time and improved implant size/position and imaging feature estimation compared to conventional radiographic templating. Several components of data processing and model development and testing were insufficiently covered in the studies included in this scoping review. CONCLUSIONS AI-based 3D templating systems have the potential to improve preoperative planning for joint arthroplasty surgery. This technology offers more accurate and personalized preoperative planning, which has potential to improve functional outcomes for patients. However, deficiencies in several key areas, including data handling, model development, and testing, can potentially hinder the reproducibility and reliability of the methods proposed. As such, further research is needed to definitively evaluate the efficacy and feasibility of these systems.
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Affiliation(s)
- Ausberto Velasquez Garcia
- Mayo Clinic Department of Orthopedic Surgery, Rochester, MN, 55905, USA
- Department of Orthopedic Surgery, Clinica Universidad de Los Andes, Santiago, Chile
| | - Lainey G Bukowiec
- Mayo Clinic Department of Orthopedic Surgery, Rochester, MN, 55905, USA
| | - Linjun Yang
- Mayo Clinic Department of Orthopedic Surgery, Rochester, MN, 55905, USA
| | - Hiroki Nishikawa
- Mayo Clinic Department of Orthopedic Surgery, Rochester, MN, 55905, USA
- Department of Orthopaedic Surgery, Showa University School of Medicine, Tokyo, Japan
| | | | - A Noelle Larson
- Mayo Clinic Department of Orthopedic Surgery, Rochester, MN, 55905, USA
| | - Michael J Taunton
- Mayo Clinic Department of Orthopedic Surgery, Rochester, MN, 55905, USA
| | | | | | - Cody C Wyles
- Mayo Clinic Department of Orthopedic Surgery, Rochester, MN, 55905, USA.
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Chen TLW, Shimizu MR, Buddhiraju A, Seo HH, Subih MA, Chen SF, Kwon YM. Predicting 30-day unplanned hospital readmission after revision total knee arthroplasty: machine learning model analysis of a national patient cohort. Med Biol Eng Comput 2024:10.1007/s11517-024-03054-7. [PMID: 38451418 DOI: 10.1007/s11517-024-03054-7] [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: 08/21/2023] [Accepted: 02/18/2024] [Indexed: 03/08/2024]
Abstract
Revision total knee arthroplasty (TKA) is associated with a higher risk of readmission than primary TKA. Identifying individual patients predisposed to readmission can facilitate proactive optimization and increase care efficiency. This study developed machine learning (ML) models to predict unplanned readmission following revision TKA using a national-scale patient dataset. A total of 17,443 revision TKA cases (2013-2020) were acquired from the ACS NSQIP database. Four ML models (artificial neural networks, random forest, histogram-based gradient boosting, and k-nearest neighbor) were developed on relevant patient variables to predict readmission following revision TKA. The length of stay, operation time, body mass index (BMI), and laboratory test results were the strongest predictors of readmission. Histogram-based gradient boosting was the best performer in distinguishing readmission (AUC: 0.95) and estimating the readmission probability for individual patients (calibration slope: 1.13; calibration intercept: -0.00; Brier score: 0.064). All models produced higher net benefit than the default strategies of treating all or no patients, supporting the clinical utility of the models. ML demonstrated excellent performance for the prediction of readmission following revision TKA. Optimization of important predictors highlighted by our model may decrease preventable hospital readmission following surgery, thereby leading to reduced financial burden and improved patient satisfaction.
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Affiliation(s)
- Tony Lin-Wei Chen
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Michelle Riyo Shimizu
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Anirudh Buddhiraju
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Henry Hojoon Seo
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Murad Abdullah Subih
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Shane Fei Chen
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Young-Min Kwon
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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Schönnagel L, Caffard T, Vu-Han TL, Zhu J, Nathoo I, Finos K, Camino-Willhuber G, Tani S, Guven AE, Haffer H, Muellner M, Arzani A, Chiapparelli E, Amoroso K, Shue J, Duculan R, Pumberger M, Zippelius T, Sama AA, Cammisa FP, Girardi FP, Mancuso CA, Hughes AP. Predicting postoperative outcomes in lumbar spinal fusion: development of a machine learning model. Spine J 2024; 24:239-249. [PMID: 37866485 DOI: 10.1016/j.spinee.2023.09.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 09/16/2023] [Accepted: 09/30/2023] [Indexed: 10/24/2023]
Abstract
BACKGROUND CONTEXT Degenerative lumbar spondylolisthesis (DLS) is a prevalent spinal disorder, often requiring surgical intervention. Accurately predicting surgical outcomes is crucial to guide clinical decision-making, but this is challenging due to the multifactorial nature of postoperative results. Traditional risk assessment tools have limitations, and with the advent of machine learning, there is potential to enhance the precision and comprehensiveness of preoperative evaluations. PURPOSE We aimed to develop a machine-learning algorithm to predict surgical outcomes in patients with degenerative lumbar spondylolisthesis (DLS) undergoing spinal fusion surgery, only using preoperative data. STUDY DESIGN Retrospective cross-sectional study. PATIENT SAMPLE Patients with DLS undergoing lumbar spinal fusion surgery. OUTCOME MEASURES This study aimed to predict the occurrence of lower back pain (LBP) ≥4 on the numeric analogue scale (NAS) 2 years after surgery. LBP was evaluated as the average pain patients experienced at rest in the week before questioning. NAS ranges from 0 to 10, 0 representing no pain and 10 representing the worst pain imaginable. METHODS We conducted a retrospective analysis of prospectively enrolled patients who underwent spinal fusion surgery for degenerative lumbar spondylolistheses at our institution in the United States between January 2016 and December 2018. The initial patient characteristics to be included in the training of the model were chosen by clinical expertise and through a literature review and included demographic characteristics, comorbidities, and radiologic features. The data was split into a training and validation datasets using a 60/40 split. Four different machine learning models were trained, including the modern XGBoost model, logistic regression, random-forest, and support vector machine (SVM). The models were evaluated according to the area under the curve (AUC) of the receiver operating characteristics (ROC) curve. An AUC of 0.7 to 0.8 was considered fair, 0.8 to 0.9 good, and ≥ 0.9 excellent. Additionally, a calibration plot and the Brier score were calculated for each model. RESULTS A total of 135 patients (66% female) were included. A total of 38 (28%) patients reported LBP ≥ 4 after 2 years, representing the positive class. The XGBoost model demonstrated the best performance in the validation set with an AUC of 0.81 (95% CI 0.67-0.95). The other machine learning models performed significantly worse: with an AUC of 0.52 (95% CI 0.37-0.68) for the SVM, 0.56 (95% CI 0.37-0.76) for the logistic regression and an AUC of 0.56 (95% CI 0.37-0.78) for the random forest. In the XGBoost model age, composition of the erector spinae, and severity of lumbar spinal stenosis as were identified as the most important features. CONCLUSIONS This study represents a novel approach to predicting surgical outcomes in spinal fusion patients. The XGBoost demonstrated a better performance compared with classical models and highlighted the potential contributions of age and paraspinal musculature atrophy as significant factors. These findings have important implications for enhancing patient care through the identification of high-risk individuals and modifiable risk factors. As the incorporation of machine learning algorithms into clinical decision-making continues to gain traction in research and clinical practice, our insights reinforce this trajectory by showcasing the potential of these techniques in forecasting surgical results.
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Affiliation(s)
- Lukas Schönnagel
- Spine Care Institute, Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021, USA; Center for Musculoskeletal Surgery, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Thomas Caffard
- Spine Care Institute, Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021, USA; Universitätsklinikum Ulm, Klinik für Orthopädie, Oberer Eselsberg 45, 89081 Ulm, Germany
| | - Tu-Lan Vu-Han
- Center for Musculoskeletal Surgery, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Jiaqi Zhu
- Biostatistics Core, Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021, USA
| | - Isaac Nathoo
- Spine Care Institute, Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021, USA
| | - Kyle Finos
- Spine Care Institute, Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021, USA
| | - Gaston Camino-Willhuber
- Spine Care Institute, Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021, USA
| | - Soji Tani
- Spine Care Institute, Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021, USA; Department of Orthopaedic Surgery, School of Medicine, Showa University Hospital, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo 142-8666, Japan
| | - Ali E Guven
- Spine Care Institute, Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021, USA; Center for Musculoskeletal Surgery, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Henryk Haffer
- Center for Musculoskeletal Surgery, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Maximilian Muellner
- Center for Musculoskeletal Surgery, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Artine Arzani
- Spine Care Institute, Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021, USA
| | - Erika Chiapparelli
- Spine Care Institute, Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021, USA
| | - Krizia Amoroso
- Spine Care Institute, Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021, USA
| | - Jennifer Shue
- Spine Care Institute, Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021, USA
| | - Roland Duculan
- Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021, USA
| | - Matthias Pumberger
- Center for Musculoskeletal Surgery, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Timo Zippelius
- Universitätsklinikum Ulm, Klinik für Orthopädie, Oberer Eselsberg 45, 89081 Ulm, Germany
| | - Andrew A Sama
- Spine Care Institute, Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021, USA
| | - Frank P Cammisa
- Spine Care Institute, Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021, USA
| | - Federico P Girardi
- Spine Care Institute, Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021, USA
| | - Carol A Mancuso
- Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021, USA
| | - Alexander P Hughes
- Spine Care Institute, Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021, USA.
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Clement ND, Clement R, Clement A. Predicting Functional Outcomes of Total Hip Arthroplasty Using Machine Learning: A Systematic Review. J Clin Med 2024; 13:603. [PMID: 38276109 PMCID: PMC10816364 DOI: 10.3390/jcm13020603] [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: 11/24/2023] [Revised: 01/03/2024] [Accepted: 01/12/2024] [Indexed: 01/27/2024] Open
Abstract
The aim of this review was to assess the reliability of machine learning (ML) techniques to predict the functional outcome of total hip arthroplasty. The literature search was performed up to October 2023, using MEDLINE/PubMed, Embase, Web of Science, and NIH Clinical Trials. Level I to IV evidence was included. Seven studies were identified that included 44,121 patients. The time to follow-up varied from 3 months to more than 2 years. Each study employed one to six ML techniques. The best-performing models were for health-related quality of life (HRQoL) outcomes, with an area under the curve (AUC) of more than 84%. In contrast, predicting the outcome of hip-specific measures was less reliable, with an AUC of between 71% to 87%. Random forest and neural networks were generally the best-performing models. Three studies compared the reliability of ML with traditional regression analysis: one found in favour of ML, one was not clear and stated regression closely followed the best-performing ML model, and one showed a similar AUC for HRQoL outcomes but did show a greater reliability for ML to predict a clinically significant change in the hip-specific function. ML offers acceptable-to-excellent discrimination of predicting functional outcomes and may have a marginal advantage over traditional regression analysis, especially in relation to hip-specific hip functional outcomes.
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Affiliation(s)
- Nick D. Clement
- Edinburgh Orthopaedics, Royal Infirmary of Edinburgh, Little France, Edinburgh EH16 4SA, UK
- Southwest of London Orthopaedic Elective Centre, Epsom KT18 7EG, UK
| | - Rosie Clement
- Edinburgh Orthopaedics, Royal Infirmary of Edinburgh, Little France, Edinburgh EH16 4SA, UK
| | - Abigail Clement
- Edinburgh Orthopaedics, Royal Infirmary of Edinburgh, Little France, Edinburgh EH16 4SA, UK
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Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior KJ, Poirrier JE, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Rev Pharmacoecon Outcomes Res 2024; 24:63-115. [PMID: 37955147 DOI: 10.1080/14737167.2023.2279107] [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: 07/17/2023] [Accepted: 10/31/2023] [Indexed: 11/14/2023]
Abstract
INTRODUCTION The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery. METHODS A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted. RESULTS In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively). EXPERT OPINION The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.
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Affiliation(s)
- Katarzyna Kolasa
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
| | - Bisrat Admassu
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
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Michelsen C, Jørgensen CC, Heltberg M, Jensen MH, Lucchetti A, Petersen PB, Petersen T, Kehlet H, Madsen F, Hansen TB, Gromov K, Jakobsen T, Varnum C, Overgaard S, Rathsach M, Hansen L. Machine-learning vs. logistic regression for preoperative prediction of medical morbidity after fast-track hip and knee arthroplasty-a comparative study. BMC Anesthesiol 2023; 23:391. [PMID: 38030979 PMCID: PMC10685559 DOI: 10.1186/s12871-023-02354-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 11/21/2023] [Indexed: 12/01/2023] Open
Abstract
BACKGROUND Machine-learning models may improve prediction of length of stay (LOS) and morbidity after surgery. However, few studies include fast-track programs, and most rely on administrative coding with limited follow-up and information on perioperative care. This study investigates potential benefits of a machine-learning model for prediction of postoperative morbidity in fast-track total hip (THA) and knee arthroplasty (TKA). METHODS Cohort study in consecutive unselected primary THA/TKA between 2014-2017 from seven Danish centers with established fast-track protocols. Preoperative comorbidity and prescribed medication were recorded prospectively and information on length of stay and readmissions was obtained through the Danish National Patient Registry and medical records. We used a machine-learning model (Boosted Decision Trees) based on boosted decision trees with 33 preoperative variables for predicting "medical" morbidity leading to LOS > 4 days or 90-days readmissions and compared to a logistical regression model based on the same variables. We also evaluated two parsimonious models, using the ten most important variables in the full machine-learning and logistic regression models. Data collected between 2014-2016 (n:18,013) was used for model training and data from 2017 (n:3913) was used for testing. Model performances were analyzed using precision, area under receiver operating (AUROC) and precision recall curves (AUPRC), as well as the Mathews Correlation Coefficient. Variable importance was analyzed using Shapley Additive Explanations values. RESULTS Using a threshold of 20% "risk-patients" (n:782), precision, AUROC and AUPRC were 13.6%, 76.3% and 15.5% vs. 12.4%, 74.7% and 15.6% for the machine-learning and logistic regression model, respectively. The parsimonious machine-learning model performed better than the full logistic regression model. Of the top ten variables, eight were shared between the machine-learning and logistic regression models, but with a considerable age-related variation in importance of specific types of medication. CONCLUSION A machine-learning model using preoperative characteristics and prescriptions slightly improved identification of patients in high-risk of "medical" complications after fast-track THA and TKA compared to a logistic regression model. Such algorithms could help find a manageable population of patients who may benefit most from intensified perioperative care.
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Affiliation(s)
- Christian Michelsen
- The Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100, Copenhagen, Denmark
| | - Christoffer C Jørgensen
- Department of Anesthesia and Intensive Care, Hospital of Northern Zealand, Dyrehavevej 29 3400, Hillerød, Denmark.
- The Centre for Fast-Track Hip and Knee Replacement, 7621, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark.
| | - Mathias Heltberg
- The Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100, Copenhagen, Denmark
| | - Mogens H Jensen
- The Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100, Copenhagen, Denmark
| | - Alessandra Lucchetti
- The Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100, Copenhagen, Denmark
| | - Pelle B Petersen
- Department of Anesthesia and Intensive Care, Hospital of Northern Zealand, Dyrehavevej 29 3400, Hillerød, Denmark
- The Centre for Fast-Track Hip and Knee Replacement, 7621, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Troels Petersen
- The Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100, Copenhagen, Denmark
| | - Henrik Kehlet
- The Centre for Fast-Track Hip and Knee Replacement, 7621, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark
- Section of Surgical Pathophysiology, 7621, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark
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Mika AP, Martin JR, Engstrom SM, Polkowski GG, Wilson JM. Assessing ChatGPT Responses to Common Patient Questions Regarding Total Hip Arthroplasty. J Bone Joint Surg Am 2023; 105:1519-1526. [PMID: 37459402 DOI: 10.2106/jbjs.23.00209] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
BACKGROUND The contemporary patient has access to numerous resources on common orthopaedic procedures before ever presenting for a clinical evaluation. Recently, artificial intelligence (AI)-driven chatbots have become mainstream, allowing patients to engage with interfaces that supply convincing, human-like responses to prompts. ChatGPT (OpenAI), a recently developed AI-based chat technology, is one such application that has garnered rapid growth in popularity. Given the likelihood that patients may soon call on this technology for preoperative education, we sought to determine whether ChatGPT could appropriately answer frequently asked questions regarding total hip arthroplasty (THA). METHODS Ten frequently asked questions regarding total hip arthroplasty were posed to the chatbot during a conversation thread, with no follow-up questions or repetition. Each response was analyzed for accuracy with use of an evidence-based approach. Responses were rated as "excellent response not requiring clarification," "satisfactory requiring minimal clarification," "satisfactory requiring moderate clarification," or "unsatisfactory requiring substantial clarification." RESULTS Of the responses given by the chatbot, only 1 received an "unsatisfactory" rating; 2 did not require any correction, and the majority required either minimal (4 of 10) or moderate (3 of 10) clarification. Although several responses required nuanced clarification, the chatbot's responses were generally unbiased and evidence-based, even for controversial topics. CONCLUSIONS The chatbot effectively provided evidence-based responses to questions commonly asked by patients prior to THA. The chatbot presented information in a way that most patients would be able to understand. This resource may serve as a valuable clinical tool for patient education and understanding prior to orthopaedic consultation in the future.
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Affiliation(s)
- Aleksander P Mika
- Department of Orthopedic Surgery, Vanderbilt University Medical Center, Nashville, Tennessee
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10
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Houserman DJ, Berend KR, Lombardi AV, Fischetti CE, Duhaime EP, Jain A, Crawford DA. The Viability of an Artificial Intelligence/Machine Learning Prediction Model to Determine Candidates for Knee Arthroplasty. J Arthroplasty 2023; 38:2075-2080. [PMID: 35398523 DOI: 10.1016/j.arth.2022.04.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 03/16/2022] [Accepted: 04/02/2022] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND The purpose of this study is to assess the viability of a knee arthroplasty prediction model using 3-view X-rays that helps determine if patients with knee pain are candidates for total knee arthroplasty (TKA), unicompartmental knee arthroplasty (UKA), or are not arthroplasty candidates. METHODS Analysis was performed using radiographic and surgical data from a high-volume joint replacement practice. The dataset included 3 different X-ray views (anterior-posterior, lateral, and sunrise) for 2,767 patients along with information of whether that patient underwent an arthroplasty surgery (UKA or TKA) or not. This resulted in a dataset including 8,301 images from 2,707 patients. This dataset was then split into a training set (70%) and holdout test set (30%). A computer vision model was trained using a transfer learning approach. The performance of the computer vision model was evaluated on the holdout test set. Accuracy and multiclass receiver operating characteristic area under curve was used to evaluate the performance of the model. RESULTS The artificial intelligence model achieved an accuracy of 87.8% on the holdout test set and a quadratic Cohen's kappa score of 0.811. The multiclass receiver operating characteristic area under curve score for TKA was calculated to be 0.97; for UKA a score of 0.96 and for No Surgery a score of 0.98 was achieved. An accuracy of 93.8% was achieved for predicting Surgery versus No Surgery and 88% for TKA versus not TKA was achieved. CONCLUSION The artificial intelligence/machine learning model demonstrated viability for predicting which patients are candidates for a UKA, TKA, or no surgical intervention.
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Affiliation(s)
- David J Houserman
- Department of Orthopedic Surgery, Kettering Health Network-Grandview Medical Center, Dayton, OH
| | - Keith R Berend
- Joint Implant Surgeons, Inc, New Albany, OH; Mount Carmel Health System, New Albany, OH
| | - Adolph V Lombardi
- Joint Implant Surgeons, Inc, New Albany, OH; Mount Carmel Health System, New Albany, OH
| | - Chanel E Fischetti
- Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | | | | | - David A Crawford
- Joint Implant Surgeons, Inc, New Albany, OH; Mount Carmel Health System, New Albany, OH
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11
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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: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 04/19/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) has become involved in many aspects of everyday life, from voice-activated virtual assistants built into smartphones to global online search engines. Similarly, many areas of modern medicine have found ways to incorporate such technologies into mainstream practice. Despite the enthusiasm, robust evidence to support the utility of AI in contemporary total knee arthroplasty (TKA) remains limited. The purpose of this review was to provide an up-to-date summary of the use of AI in TKA and to explore its current and future value. METHODS Initially, a structured systematic review of the literature was carried out, following PRISMA search principles, with the aim of summarising the understanding of the field and identifying clinical and knowledge gaps. RESULTS A limited body of published work exists in this area. Much of the available literature is of poor methodological quality and many published studies could be best described as "demonstration of concepts" rather than "proof of concepts". There exists almost no independent validation of reported findings away from designer/host sites, and the extrapolation of key results to general orthopaedic sites is limited. CONCLUSION While AI has certainly shown value in a small number of specific TKA-associated applications, the majority to date have focused on risk, cost and outcome prediction, rather than surgical care, per se. Extensive future work is needed to demonstrate external validity and reliability in non-designer settings. Well-performed studies are warranted to ensure that the scientific evidence base supporting the use of AI in knee arthroplasty matches the global hype.
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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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12
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Gould DJ, Bailey JA, Spelman T, Bunzli S, Dowsey MM, Choong PFM. Predicting 30-day readmission following total knee arthroplasty using machine learning and clinical expertise applied to clinical administrative and research registry data in an Australian cohort. ARTHROPLASTY 2023; 5:30. [PMID: 37259173 DOI: 10.1186/s42836-023-00186-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 04/10/2023] [Indexed: 06/02/2023] Open
Abstract
BACKGROUND Thirty-day readmission is an increasingly important problem for total knee arthroplasty (TKA) patients. The aim of this study was to develop a risk prediction model using machine learning and clinical insight for 30-day readmission in primary TKA patients. METHOD Data used to train and internally validate a multivariable predictive model were obtained from a single tertiary referral centre for TKA located in Victoria, Australia. Hospital administrative data and clinical registry data were utilised, and predictors were selected through systematic review and subsequent consultation with clinicians caring for TKA patients. Logistic regression and random forest models were compared to one another. Calibration was evaluated by visual inspection of calibration curves and calculation of the integrated calibration index (ICI). Discriminative performance was evaluated using the area under the receiver operating characteristic curve (AUC-ROC). RESULTS The models developed in this study demonstrated adequate calibration for use in the clinical setting, despite having poor discriminative performance. The best-calibrated readmission prediction model was a logistic regression model trained on administrative data using risk factors identified from systematic review and meta-analysis, which are available at the initial consultation (ICI = 0.012, AUC-ROC = 0.589). Models developed to predict complications associated with readmission also had reasonable calibration (ICI = 0.012, AUC-ROC = 0.658). CONCLUSION Discriminative performance of the prediction models was poor, although machine learning provided a slight improvement. The models were reasonably well calibrated, meaning they provide accurate patient-specific probabilities of these outcomes. This information can be used in shared clinical decision-making for discharge planning and post-discharge follow up.
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Affiliation(s)
- Daniel J Gould
- Department of Surgery, St Vincent's Hospital Melbourne, University of Melbourne, Level 2 Clinical Sciences Building, 29 Regent Street, Fitzroy, VIC, 3065, Australia.
| | - James A Bailey
- School of Computing and Information Systems, University of Melbourne, Doug McDonell Building, Parkville, VIC, 3052, Australia
| | - Tim Spelman
- Department of Surgery, St Vincent's Hospital Melbourne, University of Melbourne, Level 2 Clinical Sciences Building, 29 Regent Street, Fitzroy, VIC, 3065, Australia
| | - Samantha Bunzli
- School of Health Sciences and Social Work, Griffith University, Nathan Campus, Nathan, QLD, 4111, Australia
| | - Michelle M Dowsey
- Department of Surgery, St Vincent's Hospital Melbourne, University of Melbourne, Level 2 Clinical Sciences Building, 29 Regent Street, Fitzroy, VIC, 3065, Australia
- Department of Orthopaedics, St. Vincent's Hospital Melbourne, Level 3/35 Victoria Parade, Fitzroy, VIC, 3065, Australia
| | - Peter F M Choong
- Department of Surgery, St Vincent's Hospital Melbourne, University of Melbourne, Level 2 Clinical Sciences Building, 29 Regent Street, Fitzroy, VIC, 3065, Australia
- Department of Orthopaedics, St. Vincent's Hospital Melbourne, Level 3/35 Victoria Parade, Fitzroy, VIC, 3065, Australia
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13
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Harris AB, Hegde V. Letter to the Editor: Preoperative Colonization With Staphylococcus Aureus in THA Is Associated With Increased Length of Stay. Clin Orthop Relat Res 2023; 481:1250-1251. [PMID: 37017590 PMCID: PMC10194492 DOI: 10.1097/corr.0000000000002646] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 03/08/2023] [Indexed: 04/06/2023]
Affiliation(s)
- Andrew B. Harris
- Department of Orthopaedic Surgery, The Johns Hopkins Hospital, Baltimore, MD, USA
| | - Vishal Hegde
- Department of Orthopaedic Surgery, The Johns Hopkins Hospital, Baltimore, MD, USA
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Gupta P, Haeberle HS, Zimmer ZR, Levine WN, Williams RJ, Ramkumar PN. Artificial intelligence-based applications in shoulder surgery leaves much to be desired: a systematic review. JSES REVIEWS, REPORTS, AND TECHNIQUES 2023; 3:189-200. [PMID: 37588443 PMCID: PMC10426484 DOI: 10.1016/j.xrrt.2022.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Background Artificial intelligence (AI) aims to simulate human intelligence using automated computer algorithms. There has been a rapid increase in research applying AI to various subspecialties of orthopedic surgery, including shoulder surgery. The purpose of this review is to assess the scope and validity of current clinical AI applications in shoulder surgery literature. Methods A systematic literature review was conducted using PubMed for all articles published between January 1, 2010 and June 10, 2022. The search query used the terms as follows: (artificial intelligence OR machine learning OR deep learning) AND (shoulder OR shoulder surgery OR rotator cuff). All studies that examined AI application models in shoulder surgery were included and evaluated for model performance and validation (internal, external, or both). Results A total of 45 studies were included in the final analysis. Eighteen studies involved shoulder arthroplasty, 13 rotator cuff, and 14 other areas. Studies applying AI to shoulder surgery primarily involved (1) automated imaging analysis including identifying rotator cuff tears and shoulder implants (2) risk prediction analyses including perioperative complications, functional outcomes, and patient satisfaction. Highest model performance area under the curve ranged from 0.681 (poor) to 1.00 (perfect). Only 2 studies reported external validation. Conclusion Applications of AI in the field of shoulder surgery are expanding rapidly and offer patient-specific risk stratification for shared decision-making and process automation for resource preservation. However, model performance is modest and external validation remains to be demonstrated, suggesting increased scientific rigor is warranted prior to deploying AI-based clinical applications.
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Affiliation(s)
- Puneet Gupta
- Department of Orthopaedic Surgery, George Washington University School of Medicine and Health Sciences, Washington, DC, USA
- Department of Orthopaedic Surgery, Columbia University Irving Medical Center, New York, NY, USA
| | - Heather S. Haeberle
- Department of Orthopaedic Surgery, Columbia University Irving Medical Center, New York, NY, USA
| | - Zachary R. Zimmer
- Department of Orthopaedic Surgery, George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - William N. Levine
- Department of Orthopaedic Surgery, Columbia University Irving Medical Center, New York, NY, USA
| | - Riley J. Williams
- Institute for Cartilage Repair, Hospital for Special Surgery, New York, NY, USA
| | - Prem N. Ramkumar
- Institute for Cartilage Repair, Hospital for Special Surgery, New York, NY, USA
- Long Beach Orthopaedic Institute, Long Beach, CA, USA
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15
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Gupta P, Kingston KA, O’Malley M, Williams RJ, Ramkumar PN. Advancements in Artificial Intelligence for Foot and Ankle Surgery: A Systematic Review. FOOT & ANKLE ORTHOPAEDICS 2023; 8:24730114221151079. [PMID: 36817020 PMCID: PMC9929923 DOI: 10.1177/24730114221151079] [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] [Indexed: 02/16/2023] Open
Abstract
Background There has been a rapid increase in research applying artificial intelligence (AI) to various subspecialties of orthopaedic surgery, including foot and ankle surgery. The purpose of this systematic review is to (1) characterize the topics and objectives of studies using AI in foot and ankle surgery, (2) evaluate the performance of their models, and (3) evaluate their validity (internal or external validation). Methods A systematic literature review was conducted using PubMed/MEDLINE and Embase databases in December 2022. All studies that used AI or its subsets machine learning (ML) and deep learning (DL) in the setting of foot and ankle surgery relevant to orthopaedic surgeons were included. Studies were evaluated for their demographics, subject area, outcomes of interest, model(s) tested, model(s)' performance, and validity (internal or external). Results A total of 31 studies met inclusion criteria: 14 studies investigated AI for image interpretation, 13 studies investigated AI for clinical predictions, and 4 studies were grouped as "other." Studies commonly explored AI for ankle fractures, calcaneus fractures, hallux valgus, Achilles tendon pathologies, plantar fasciitis, and sports injuries. For studies reporting the area under the receiver operating characteristic curve (AUC), AUCs ranged from 0.64 (poor) to 0.99 (excellent). Two studies (6.45%) reported external validation. Conclusion Applications of AI in the field of foot and ankle surgery are expanding, particularly for image interpretation and clinical predictions. Current model performances range from poor to excellent, and most studies lack external validation, demonstrating a need for further research prior to deploying AI-based clinical applications. Level of Evidence Level III, retrospective cohort study.
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Affiliation(s)
- Puneet Gupta
- Department of Orthopaedic Surgery, George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | | | - Martin O’Malley
- Hospital for Special Surgery, New York, NY, USA,Brooklyn Nets, National Basketball Association (NBA), Brooklyn, NY, USA
| | - Riley J. Williams
- Hospital for Special Surgery, New York, NY, USA,Brooklyn Nets, National Basketball Association (NBA), Brooklyn, NY, USA
| | - Prem N. Ramkumar
- Hospital for Special Surgery, New York, NY, USA,Brooklyn Nets, National Basketball Association (NBA), Brooklyn, NY, USA,Prem N. Ramkumar, MD, MBA, Hospital for Special Surgery, 535 E 70th St, New York, NY 10021-4898, USA.
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York TJ, Raj S, Ashdown T, Jones G. Clinician and computer: a study on doctors' perceptions of artificial intelligence in skeletal radiography. BMC MEDICAL EDUCATION 2023; 23:16. [PMID: 36627640 PMCID: PMC9830124 DOI: 10.1186/s12909-022-03976-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 12/19/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Traumatic musculoskeletal injuries are a common presentation to emergency care, the first-line investigation often being plain radiography. The interpretation of this imaging frequently falls to less experienced clinicians despite well-established challenges in reporting. This study presents novel data of clinicians' confidence in interpreting trauma radiographs, their perception of AI in healthcare, and their support for the development of systems applied to skeletal radiography. METHODS A novel questionnaire was distributed through a network of collaborators to clinicians across the Southeast of England. Over a three-month period, responses were compiled into a database before undergoing statistical review. RESULTS The responses of 297 participants were included. The mean self-assessed knowledge of AI in healthcare was 3.68 out of ten, with significantly higher knowledge reported by the most senior doctors (Specialty Trainee/Specialty Registrar or above = 4.88). 13.8% of participants reported an awareness of AI in their clinical practice. Overall, participants indicated substantial favourability towards AI in healthcare (7.87) and in AI applied to skeletal radiography (7.75). There was a preference for a hypothetical system indicating positive findings rather than ruling as negative (7.26 vs 6.20). CONCLUSIONS This study identifies clear support, amongst a cross section of student and qualified doctors, for both the general use of AI technology in healthcare and in its application to skeletal radiography for trauma. The development of systems to address this demand appear well founded and popular. The engagement of a small but reticent minority should be sought, along with improving the wider education of doctors on AI.
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Affiliation(s)
- Thomas James York
- Alexander Fleming Building, Imperial College London, South Kensington Campus, London, UK.
| | | | | | - Gareth Jones
- Imperial College Healthcare NHS Trust, London, UK
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Li Z, Maimaiti Z, Fu J, Chen JY, Xu C. Global research landscape on artificial intelligence in arthroplasty: A bibliometric analysis. Digit Health 2023; 9:20552076231184048. [PMID: 37361434 PMCID: PMC10286212 DOI: 10.1177/20552076231184048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 06/07/2023] [Indexed: 06/28/2023] Open
Abstract
Background Artificial intelligence (AI) has promising applications in arthroplasty. In response to the knowledge explosion resulting from the rapid growth of publications, we applied bibliometric analysis to explore the research profile and topical trends in this field. Methods The articles and reviews related to AI in arthroplasty were retrieved from 2000 to 2021. The Java-based Citespace, VOSviewer, R software-based Bibiometrix, and an online platform systematically evaluated publications by countries, institutions, authors, journals, references, and keywords. Results A total of 867 publications were included. Over the past 22 years, the number of AI-related publications in the field of arthroplasty has grown exponentially. The United States was the most productive and academically influential country. The Cleveland Clinic was the most prolific institution. Most publications were published in high academic impact journals. However, collaborative networks revealed a lack and imbalance of inter-regional, inter-institutional, and inter-author cooperation. Two emerging research areas represented the development trends: major AI subfields such as machine learning and deep learning, and the other is research related to clinical outcomes. Conclusion AI in arthroplasty is evolving rapidly. Collaboration between different regions and institutions should be strengthened to deepen our understanding further and exert critical implications for decision-making. Predicting clinical outcomes of arthroplasty using novel AI strategies may be a promising application in this field.
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Affiliation(s)
- Zhuo Li
- School of Medicine, Nankai University, Tianjin, People's Republic of China
- Department of Orthopedics, The First Medical Center, Chinese PLA General Hospital, Beijing, People's Republic of China
| | - Zulipikaer Maimaiti
- Department of Orthopedics, The First Medical Center, Chinese PLA General Hospital, Beijing, People's Republic of China
| | - Jun Fu
- Department of Orthopedics, The First Medical Center, Chinese PLA General Hospital, Beijing, People's Republic of China
- Department of Orthopedics, The Fourth Medical Center, Chinese PLA General Hospital, Beijing, People's Republic of China
| | - Ji-Ying Chen
- School of Medicine, Nankai University, Tianjin, People's Republic of China
- Department of Orthopedics, The First Medical Center, Chinese PLA General Hospital, Beijing, People's Republic of China
- Department of Orthopedics, The Fourth Medical Center, Chinese PLA General Hospital, Beijing, People's Republic of China
| | - Chi Xu
- Department of Orthopedics, The First Medical Center, Chinese PLA General Hospital, Beijing, People's Republic of China
- Department of Orthopedics, The Fourth Medical Center, Chinese PLA General Hospital, Beijing, People's Republic of China
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Sridhar S, Whitaker B, Mouat-Hunter A, McCrory B. Predicting Length of Stay using machine learning for total joint replacements performed at a rural community hospital. PLoS One 2022; 17:e0277479. [PMID: 36355762 PMCID: PMC9648742 DOI: 10.1371/journal.pone.0277479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 10/28/2022] [Indexed: 11/12/2022] Open
Abstract
Background Predicting patient’s Length of Stay (LOS) before total joint replacement (TJR) surgery is vital for hospitals to optimally manage costs and resources. Many hospitals including in rural areas use publicly available models such as National Surgical Quality Improvement Program (NSQIP) calculator which, unfortunately, performs suboptimally when predicting LOS for TJR procedures. Objective The objective of this research was to develop a Machine Learning (ML) model to predict LOS for TJR procedures performed at a Perioperative Surgical Home implemented rural community hospital for better accuracy and interpretation than the NSQIP calculator. Methods A total of 158 TJR patients were collected and analyzed from a rural community hospital located in Montana. A random forest (RF) model was used to predict patient’s LOS. For interpretation, permuted feature importance and partial dependence plot methods were used to identify the important variables and their relationship with the LOS. Results The root mean square error for the RF model (0.7) was lower than the NSQIP calculator (1.21). The five most important variables for predicting LOS were BMI, Duke Activity Status-Index, diabetes, patient’s household income, and patient’s age. Conclusion This pilot study is the first of its kind to develop an ML model to predict LOS for TJR procedures that were performed at a small-scale rural community hospital. This pilot study contributes an approach for rural hospitals, making them more independent by developing their own predictions instead of relying on public models.
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Affiliation(s)
- Srinivasan Sridhar
- Mechanical and Industrial Engineering, Montana State University, Bozeman, Montana, United States of America
- * E-mail:
| | - Bradley Whitaker
- Electrical and Computer Engineering, Montana State University, Bozeman, Montana, United States of America
| | | | - Bernadette McCrory
- Mechanical and Industrial Engineering, Montana State University, Bozeman, Montana, United States of America
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Comparative Analysis of the Ability of Machine Learning Models in Predicting In-hospital Postoperative Outcomes After Total Hip Arthroplasty. J Am Acad Orthop Surg 2022; 30:e1337-e1347. [PMID: 35947826 DOI: 10.5435/jaaos-d-21-00987] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 05/02/2022] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Machine learning (ML) methods have shown promise in a wide range of applications including the development of patient-specific predictive models before surgical interventions. The purpose of this study was to develop, test, and compare four distinct ML models to predict postoperative parameters after primary total hip arthroplasty. METHODS Data from the Nationwide Inpatient Sample were used to identify patients undergoing total hip arthroplasty from 2016 to 2017. Linear support vector machine (LSVM), random forest (RF), neural network (NN), and extreme gradient boost trees (XGBoost) predictive of mortality, length of stay, and discharge disposition were developed and validated using 15 predictive patient-specific and hospital-specific factors. Area under the curve of the receiver operating characteristic (AUCROC) curve and accuracy were used as validity metrics, and the strongest predictive variables under each model were assessed. RESULTS A total of 177,442 patients were included in this analysis. For mortality, the XGBoost, NN, and LSVM models all had excellent responsiveness during validation while RF had fair responsiveness. LSVM had the highest responsiveness with an AUCROC of 0.973 during validation. For the length of stay, the LSVM and NN models had fair responsiveness while the XGBoost and random forest models had poor responsiveness. LSVM had the highest responsiveness with an AUCROC of 0.744 during validation. For the discharge disposition outcome, LSVM had good responsiveness while the XGBoost, NN, and RF models all had fair responsiveness. LSVM had the highest responsiveness with an AUCROC of 0.801. DISCUSSION The ML methods tested demonstrated a range of poor-to-excellent responsiveness and accuracy in the prediction of the assessed metrics, with LSVM being the best performer. Such models should be further developed, with eventual integration into clinical practice to inform patient discussions and management decision making, with the potential for integration into tiered bundled payment models.
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Guo N, Tian J, Wang L, Sun K, Mi L, Ming H, Zhe Z, Sun F. Discussion on the possibility of multi-layer intelligent technologies to achieve the best recover of musculoskeletal injuries: Smart materials, variable structures, and intelligent therapeutic planning. Front Bioeng Biotechnol 2022; 10:1016598. [PMID: 36246357 PMCID: PMC9561816 DOI: 10.3389/fbioe.2022.1016598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 09/14/2022] [Indexed: 11/16/2022] Open
Abstract
Although intelligent technologies has facilitated the development of precise orthopaedic, simple internal fixation, ligament reconstruction or arthroplasty can only relieve pain of patients in short-term. To achieve the best recover of musculoskeletal injuries, three bottlenecks must be broken through, which includes scientific path planning, bioactive implants and personalized surgical channels building. As scientific surgical path can be planned and built by through AI technology, 4D printing technology can make more bioactive implants be manufactured, and variable structures can establish personalized channels precisely, it is possible to achieve satisfied and effective musculoskeletal injury recovery with the progress of multi-layer intelligent technologies (MLIT).
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Affiliation(s)
- Na Guo
- Department of Computer Science and Technology, Tsinghua University, Beijing, China
- Institute of Precision Medicine, Tsinghua University, Beijing, China
| | - Jiawen Tian
- Department of Computer Science and Technology, Tsinghua University, Beijing, China
- Institute of Precision Medicine, Tsinghua University, Beijing, China
| | - Litao Wang
- College of Engineering, China Agricultural University, Beijing, China
| | - Kai Sun
- Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Lixin Mi
- Musculoskeletal Department, Beijing Rehabilitation Hospital, Beijing, China
| | - Hao Ming
- Orthopaedics, Chinese PLA General Hospital, Beijing, China
| | - Zhao Zhe
- Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Fuchun Sun
- Department of Computer Science and Technology, Tsinghua University, Beijing, China
- Institute of Precision Medicine, Tsinghua University, Beijing, China
- *Correspondence: Fuchun Sun,
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Klemt C, Harvey MJ, Robinson MG, Esposito JG, Yeo I, Kwon YM. Machine learning algorithms predict extended postoperative opioid use in primary total knee arthroplasty. Knee Surg Sports Traumatol Arthrosc 2022; 30:2573-2581. [PMID: 34984528 DOI: 10.1007/s00167-021-06812-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 11/18/2021] [Indexed: 02/02/2023]
Abstract
PURPOSE Adequate postoperative pain control following total knee arthroplasty (TKA) is required to achieve optimal patient recovery. However, the postoperative recovery may lead to an unnaturally extended opioid use, which has been associated with adverse outcomes. This study hypothesizes that machine learning models can accurately predict extended opioid use following primary TKA. METHODS A total of 8873 consecutive patients that underwent primary TKA were evaluated, including 643 patients (7.2%) with extended postoperative opioid use (> 90 days). Electronic patient records were manually reviewed to identify patient demographics and surgical variables associated with prolonged postoperative opioid use. Five machine learning algorithms were developed, encompassing the breadth of state-of-the-art machine learning algorithms available in the literature, to predict extended opioid use following primary TKA, and these models were assessed by discrimination, calibration, and decision curve analysis. RESULTS The strongest predictors for prolonged opioid prescription following primary TKA were preoperative opioid duration (100% importance; p < 0.01), drug abuse (54% importance; p < 0.01), and depression (47% importance; p < 0.01). The five machine learning models all achieved excellent performance across discrimination (AUC > 0.83), calibration, and decision curve analysis. Higher net benefits for all machine learning models were demonstrated, when compared to the default strategies of changing management for all patients or no patients. CONCLUSION The study findings show excellent model performance for the prediction of extended postoperative opioid use following primary total knee arthroplasty, highlighting the potential of these models to assist in preoperatively identifying at risk patients, and allowing the implementation of individualized peri-operative counselling and pain management strategies to mitigate complications associated with prolonged opioid use. LEVEL OF EVIDENCE IV.
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Affiliation(s)
- Christian Klemt
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
| | - Michael Joseph Harvey
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
| | - Matthew Gerald Robinson
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
| | - John G Esposito
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
| | - Ingwon Yeo
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
| | - Young-Min Kwon
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA.
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22
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Lin Z. Reply to 'Comment on 'The effectiveness and safety of topical beta-receptor blocker in treating superficial infantile haemangiomas: A meta-analysis including 20 studies' Dr. Sun et al.'. Br J Clin Pharmacol 2022; 88:3002-3003. [PMID: 35118695 DOI: 10.1111/bcp.15246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 01/04/2022] [Indexed: 11/30/2022] Open
Affiliation(s)
- Zhenying Lin
- Department of Pediatric Surgery, Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China
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Zalikha AK, El-Othmani MM, Shah RP. Predictive capacity of four machine learning models for in-hospital postoperative outcomes following total knee arthroplasty. J Orthop 2022; 31:22-28. [PMID: 35345622 PMCID: PMC8956845 DOI: 10.1016/j.jor.2022.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 02/13/2022] [Accepted: 03/17/2022] [Indexed: 11/26/2022] Open
Abstract
Background Machine learning (ML) methods have shown promise in the development of patient-specific predictive models prior to surgical interventions. The purpose of this study was to develop, test, and compare four distinct ML models to predict postoperative parameters following primary total knee arthroplasty (TKA). Methods Data from the Nationwide Inpatient Sample was used to identify patients undergoing TKA during 2016-2017. Four distinct ML models predictive of mortality, length of stay (LOS), and discharge disposition were developed and validated using 15 predictive patient and hospital-specific factors. Area under the curve of the receiver operating characteristic curve (AUCROC) and accuracy were used as validity metrics, and the strongest predictive variables under each model were assessed. Results A total of 305,577 patients were included. For mortality, the XGBoost, neural network (NN), and LSVM models all had excellent responsiveness during validation, while random forest (RF) had fair responsiveness. For predicting LOS, all four models had poor responsiveness. For the discharge disposition outcome, the LSVM, NN, and XGBoost models had good responsiveness, while the RF model had poor responsiveness. LSVM and XGBoost had the highest responsiveness for predicting discharge disposition with an AUCROC of 0.747. Discussion The ML models tested demonstrated a range of poor to excellent responsiveness and accuracy in the prediction of the assessed metrics, with considerable variability noted in the predictive precision between the models. The continued development of ML models should be encouraged, with eventual integration into clinical practice in order to inform patient discussions, management decision making, and health policy.
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