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Wong CR, Zhu A, Baltzer HL. The Accuracy of Artificial Intelligence Models in Hand/Wrist Fracture and Dislocation Diagnosis: A Systematic Review and Meta-Analysis. JBJS Rev 2024; 12:01874474-202409000-00006. [PMID: 39236148 DOI: 10.2106/jbjs.rvw.24.00106] [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: 09/07/2024]
Abstract
BACKGROUND Early and accurate diagnosis is critical to preserve function and reduce healthcare costs in patients with hand and wrist injury. As such, artificial intelligence (AI) models have been developed for the purpose of diagnosing fractures through imaging. The purpose of this systematic review and meta-analysis was to determine the accuracy of AI models in identifying hand and wrist fractures and dislocations. METHODS Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Diagnostic Test Accuracy guidelines, Ovid MEDLINE, Embase, and Cochrane Central Register of Controlled Trials were searched from their inception to October 10, 2023. Studies were included if they utilized an AI model (index test) for detecting hand and wrist fractures and dislocations in pediatric (<18 years) or adult (>18 years) patients through any radiologic imaging, with the reference standard established through image review by a medical expert. Results were synthesized through bivariate analysis. Risk of bias was assessed using the QUADAS-2 tool. This study was registered with PROSPERO (CRD42023486475). Certainty of evidence was assessed using Grading of Recommendations Assessment, Development, and Evaluation. RESULTS A systematic review identified 36 studies. Most studies assessed wrist fractures (27.90%) through radiograph imaging (94.44%), with radiologists serving as the reference standard (66.67%). AI models demonstrated area under the curve (0.946), positive likelihood ratio (7.690; 95% confidence interval, 6.400-9.190), and negative likelihood ratio (0.112; 0.0848-0.145) in diagnosing hand and wrist fractures and dislocations. Examining only studies characterized by a low risk of bias, sensitivity analysis did not reveal any difference from the overall results. Overall certainty of evidence was moderate. CONCLUSION In demonstrating the accuracy of AI models in hand and wrist fracture and dislocation diagnosis, we have demonstrated that the potential use of AI in diagnosing hand and wrist fractures is promising. LEVEL OF EVIDENCE Level III. See Instructions for Authors for a complete description of levels of evidence.
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Affiliation(s)
- Chloe R Wong
- Division of Plastic, Reconstructive & Aesthetic Surgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Alice Zhu
- Division of General Surgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Heather L Baltzer
- Division of Plastic, Reconstructive & Aesthetic Surgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
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2
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Suen K, Zhang R, Kutaiba N. Accuracy of wrist fracture detection on radiographs by artificial intelligence compared to human clinicians. A systematic review and meta-analysis. Eur J Radiol 2024; 178:111593. [PMID: 38981178 DOI: 10.1016/j.ejrad.2024.111593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 06/23/2024] [Accepted: 06/28/2024] [Indexed: 07/11/2024]
Abstract
PURPOSE The aim of the study is to perform a systematic review and meta-analysis comparing the diagnostic performance of artificial intelligence (AI) and human readers in the detection of wrist fractures. METHOD This study conducted a systematic review following PRISMA guidelines. Medline and Embase databases were searched for relevant articles published up to August 14, 2023. All included studies reported the diagnostic performance of AI to detect wrist fractures, with or without comparison to human readers. A meta-analysis was performed to calculate the pooled sensitivity and specificity of AI and human experts in detecting distal radius, and scaphoid fractures respectively. RESULTS Of 213 identified records, 20 studies were included after abstract screening and full-text review. Nine articles examined distal radius fractures, while eight studies examined scaphoid fractures. One study included distal radius and scaphoid fractures, and two studies examined paediatric distal radius fractures. The pooled sensitivity and specificity for AI in detecting distal radius fractures were 0.92 (95% CI 0.88-0.95) and 0.89 (0.84-0.92), respectively. The corresponding values for human readers were 0.95 (0.91-0.97) and 0.94 (0.91-0.96). For scaphoid fractures, pooled sensitivity and specificity for AI were 0.85 (0.73-0.92) and 0.83 (0.76-0.89), while human experts exhibited 0.71 (0.66-0.76) and 0.93 (0.90-0.95), respectively. CONCLUSION The results indicate comparable diagnostic accuracy between AI and human readers, especially for distal radius fractures. For the detection of scaphoid fractures, the human readers were similarly sensitive but more specific. These findings underscore the potential of AI to enhance fracture detection accuracy and improve clinical workflow, rather than to replace human intelligence.
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Affiliation(s)
- Kary Suen
- Department of Radiology, Austin Health, Victoria, Australia.
| | - Richard Zhang
- Department of Radiology, Austin Health, Victoria, Australia
| | - Numan Kutaiba
- Department of Radiology, Austin Health, Victoria, Australia
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Li Y, Liang Z, Li Y, Cao Y, Zhang H, Dong B. Machine learning value in the diagnosis of vertebral fractures: A systematic review and meta-analysis. Eur J Radiol 2024; 181:111714. [PMID: 39241305 DOI: 10.1016/j.ejrad.2024.111714] [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: 06/04/2024] [Revised: 07/28/2024] [Accepted: 08/30/2024] [Indexed: 09/09/2024]
Abstract
PURPOSE To evaluate the diagnostic accuracy of machine learning (ML) in detecting vertebral fractures, considering varying fracture classifications, patient populations, and imaging approaches. METHOD A systematic review and meta-analysis were conducted by searching PubMed, Embase, Cochrane Library, and Web of Science up to December 31, 2023, for studies using ML for vertebral fracture diagnosis. Bias risk was assessed using QUADAS-2. A bivariate mixed-effects model was used for the meta-analysis. Meta-analyses were performed according to five task types (vertebral fractures, osteoporotic vertebral fractures, differentiation of benign and malignant vertebral fractures, differentiation of acute and chronic vertebral fractures, and prediction of vertebral fractures). Subgroup analyses were conducted by different ML models (including ML and DL) and modeling methods (including CT, X-ray, MRI, and clinical features). RESULTS Eighty-one studies were included. ML demonstrated a diagnostic sensitivity of 0.91 and specificity of 0.95 for vertebral fractures. Subgroup analysis showed that DL (SROC 0.98) and CT (SROC 0.98) performed best overall. For osteoporotic fractures, ML showed a sensitivity of 0.93 and specificity of 0.96, with DL (SROC 0.99) and X-ray (SROC 0.99) performing better. For differentiating benign from malignant fractures, ML achieved a sensitivity of 0.92 and specificity of 0.93, with DL (SROC 0.96) and MRI (SROC 0.97) performing best. For differentiating acute from chronic vertebral fractures, ML showed a sensitivity of 0.92 and specificity of 0.93, with ML (SROC 0.96) and CT (SROC 0.97) performing best. For predicting vertebral fractures, ML had a sensitivity of 0.76 and specificity of 0.87, with ML (SROC 0.80) and clinical features (SROC 0.86) performing better. CONCLUSIONS ML, especially DL models applied to CT, MRI, and X-ray, shows high diagnostic accuracy for vertebral fractures. ML also effectively predicts osteoporotic vertebral fractures, aiding in tailored prevention strategies. Further research and validation are required to confirm ML's clinical efficacy.
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Affiliation(s)
- Yue Li
- Pain Ward of Rehabilitation Department, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710054, PR China
| | - Zhuang Liang
- Pain Ward of Rehabilitation Department, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710054, PR China
| | - Yingchun Li
- Pain Ward of Rehabilitation Department, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710054, PR China
| | - Yang Cao
- Pain Ward of Rehabilitation Department, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710054, PR China
| | - Hui Zhang
- Pain Ward of Rehabilitation Department, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710054, PR China
| | - Bo Dong
- Pain Ward of Rehabilitation Department, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710054, PR China.
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Shaik A, Larsen K, Lane NE, Zhao C, Su KJ, Keyak JH, Tian Q, Sha Q, Shen H, Deng HW, Zhou W. A staged approach using machine learning and uncertainty quantification to predict the risk of hip fracture. Bone Rep 2024; 22:101805. [PMID: 39328352 PMCID: PMC11426051 DOI: 10.1016/j.bonr.2024.101805] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2024] [Revised: 08/20/2024] [Accepted: 09/11/2024] [Indexed: 09/28/2024] Open
Abstract
Hip fractures present a significant healthcare challenge, especially within aging populations, where they are often caused by falls. These fractures lead to substantial morbidity and mortality, emphasizing the need for timely surgical intervention. Despite advancements in medical care, hip fractures impose a significant burden on individuals and healthcare systems. This paper focuses on the prediction of hip fracture risk in older and middle-aged adults, where falls and compromised bone quality are predominant factors. The study cohort included 547 patients, with 94 experiencing hip fracture. To assess the risk of hip fracture, clinical variables and clinical variables combined with hip DXA imaging features were evaluated as predictors, followed by a novel staged approach. Hip DXA imaging features included those extracted by convolutional neural networks (CNNs), shape measurements, and texture features. Two ensemble machine learning models were evaluated: Ensemble 1 (clinical variables only) and Ensemble 2 (clinical variables and imaging features) using the logistic regression as the base classifier and bootstrapping for ensemble learning. The staged approach was developed using uncertainty quantification from Ensemble 1 which was used to decide if hip DXA imaging features were necessary to improve prediction for each subject. Ensemble 2 exhibited the highest performance, achieving an Area Under the Curve (AUC) of 0.95, an accuracy of 0.92, a sensitivity of 0.81, and a specificity of 0.94. The staged model also performed well, with an AUC of 0.85, an accuracy of 0.86, a sensitivity of 0.56, and a specificity of 0.92, outperforming Ensemble 1, which had an AUC of 0.55, an accuracy of 0.73, a sensitivity of 0.20, and a specificity of 0.83. Furthermore, the staged model suggested that 54.49 % of patients did not require DXA scanning, effectively balancing accuracy and specificity, while offering a robust solution when DXA data acquisition is not feasible. Statistical tests confirmed significant differences between the models, highlighting the advantages of advanced modeling strategies. Our staged approach offers a cost-effective holistic view of patient health. It can identify individuals at risk of hip fracture with a high accuracy while reducing unnecessary DXA scans. This approach has great promise to guide the need for interventions to prevent hip fracture while reducing diagnostic cost and exposure to radiation.
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Affiliation(s)
- Anjum Shaik
- Department of Applied Computing, Michigan Technological University, 1400 Townsend Dr, Houghton, MI, 49931, United States of America
| | - Kristoffer Larsen
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA
| | - Nancy E. Lane
- Department of Internal Medicine and Division of Rheumatology, UC Davis Health, Sacramento, CA 95817, USA
| | - Chen Zhao
- Department of Computer Science, Kennesaw State University, 680 Arntson Dr, Marietta, GA 30060, USA
| | - Kuan-Jui Su
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Joyce H. Keyak
- Department of Radiological Sciences, Department of Biomedical Engineering, and Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, USA
| | - Qing Tian
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA
| | - Hui Shen
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Hong-Wen Deng
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Weihua Zhou
- Department of Applied Computing, Michigan Technological University, 1400 Townsend Dr, Houghton, MI, 49931, United States of America
- Center for Biocomputing and Digital Health, Institute of Computing and Cybersystems, and Health Research Institute, Michigan Technological University, Houghton, MI 49931, USA
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Kraus M, Anteby R, Konen E, Eshed I, Klang E. Artificial intelligence for X-ray scaphoid fracture detection: a systematic review and diagnostic test accuracy meta-analysis. Eur Radiol 2024; 34:4341-4351. [PMID: 38097728 PMCID: PMC11213739 DOI: 10.1007/s00330-023-10473-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 09/22/2023] [Accepted: 09/28/2023] [Indexed: 06/29/2024]
Abstract
OBJECTIVES Scaphoid fractures are usually diagnosed using X-rays, a low-sensitivity modality. Artificial intelligence (AI) using Convolutional Neural Networks (CNNs) has been explored for diagnosing scaphoid fractures in X-rays. The aim of this systematic review and meta-analysis is to evaluate the use of AI for detecting scaphoid fractures on X-rays and analyze its accuracy and usefulness. MATERIALS AND METHODS This study followed the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) and PRISMA-Diagnostic Test Accuracy. A literature search was conducted in the PubMed database for original articles published until July 2023. The risk of bias and applicability were evaluated using the QUADAS-2 tool. A bivariate diagnostic random-effects meta-analysis was conducted, and the results were analyzed using the Summary Receiver Operating Characteristic (SROC) curve. RESULTS Ten studies met the inclusion criteria and were all retrospective. The AI's diagnostic performance for detecting scaphoid fractures ranged from AUC 0.77 to 0.96. Seven studies were included in the meta-analysis, with a total of 3373 images. The meta-analysis pooled sensitivity and specificity were 0.80 and 0.89, respectively. The meta-analysis overall AUC was 0.88. The QUADAS-2 tool found high risk of bias and concerns about applicability in 9 out of 10 studies. CONCLUSIONS The current results of AI's diagnostic performance for detecting scaphoid fractures in X-rays show promise. The results show high overall sensitivity and specificity and a high SROC result. Further research is needed to compare AI's diagnostic performance to human diagnostic performance in a clinical setting. CLINICAL RELEVANCE STATEMENT Scaphoid fractures are prone to be missed secondary to assessment with a low sensitivity modality and a high occult fracture rate. AI systems can be beneficial for clinicians and radiologists to facilitate early diagnosis, and avoid missed injuries. KEY POINTS • Scaphoid fractures are common and some can be easily missed in X-rays. • Artificial intelligence (AI) systems demonstrate high diagnostic performance for the diagnosis of scaphoid fractures in X-rays. • AI systems can be beneficial in diagnosing both obvious and occult scaphoid fractures.
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Affiliation(s)
- Matan Kraus
- Department of Diagnostic Imaging, Sheba Medical Center, 2 Sheba Road, 5262000, Ramat Gan, Israel.
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
| | - Roi Anteby
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
- Department of General Surgery, Sheba Medical Center, 2 Sheba Road, 5262000, Ramat Gan, Israel
| | - Eli Konen
- Department of Diagnostic Imaging, Sheba Medical Center, 2 Sheba Road, 5262000, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Iris Eshed
- Department of Diagnostic Imaging, Sheba Medical Center, 2 Sheba Road, 5262000, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Eyal Klang
- Department of Diagnostic Imaging, Sheba Medical Center, 2 Sheba Road, 5262000, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
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Shaik A, Larsen K, Lane NE, Zhao C, Su KJ, Keyak JH, Tian Q, Sha Q, Shen H, Deng HW, Zhou W. A Staged Approach using Machine Learning and Uncertainty Quantification to Predict the Risk of Hip Fracture. ARXIV 2024:arXiv:2405.20071v1. [PMID: 38855554 PMCID: PMC11160872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Hip fractures present a significant healthcare challenge, especially within aging populations, where they are often caused by falls. These fractures lead to substantial morbidity and mortality, emphasizing the need for timely surgical intervention. Despite advancements in medical care, hip fractures impose a significant burden on individuals and healthcare systems. This paper focuses on the prediction of hip fracture risk in older and middle-aged adults, where falls and compromised bone quality are predominant factors. We propose a novel staged model that combines advanced imaging and clinical data to improve predictive performance. By using convolutional neural networks (CNNs) to extract features from hip DXA images, along with clinical variables, shape measurements, and texture features, our method provides a comprehensive framework for assessing fracture risk. The study cohort included 547 patients, with 94 experiencing hip fracture. A staged machine learning-based model was developed using two ensemble models: Ensemble 1 (clinical variables only) and Ensemble 2 (clinical variables and DXA imaging features). This staged approach used uncertainty quantification from Ensemble 1 to decide if DXA features are necessary for further prediction. Ensemble 2 exhibited the highest performance, achieving an Area Under the Curve (AUC) of 0.9541, an accuracy of 0.9195, a sensitivity of 0.8078, and a specificity of 0.9427. The staged model also performed well, with an AUC of 0.8486, an accuracy of 0.8611, a sensitivity of 0.5578, and a specificity of 0.9249, outperforming Ensemble 1, which had an AUC of 0.5549, an accuracy of 0.7239, a sensitivity of 0.1956, and a specificity of 0.8343. Furthermore, the staged model suggested that 54.49% of patients did not require DXA scanning. It effectively balanced accuracy and specificity, offering a robust solution when DXA data acquisition is not always feasible. Statistical tests confirmed significant differences between the models, highlighting the advantages of the advanced modeling strategies. Our staged approach offers a cost-effective holistic view of patients' health. It could identify individuals at risk with a high accuracy but reduce the unnecessary DXA scanning. Our approach has great promise to guide interventions to prevent hip fractures with reduced cost and radiation.
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Affiliation(s)
- Anjum Shaik
- Department of Applied Computing, Michigan Technological University, 1400 Townsend Dr, Houghton, MI, 49931
| | - Kristoffer Larsen
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA
| | - Nancy E. Lane
- Department of Internal Medicine and Division of Rheumatology, UC Davis Health, Sacramento, CA 95817
| | - Chen Zhao
- Department of Computer Science, Kennesaw State University, 680 Arntson Dr, Marietta, GA 30060
| | - Kuan-Jui Su
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112
| | - Joyce H. Keyak
- Department of Radiological Sciences, Department of Biomedical Engineering, and Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, USA
| | - Qing Tian
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA
| | - Hui Shen
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112
| | - Hong-Wen Deng
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112
| | - Weihua Zhou
- Department of Applied Computing, Michigan Technological University, 1400 Townsend Dr, Houghton, MI, 49931
- Center for Biocomputing and Digital Health, Institute of Computing and Cybersystems, and Health Research Institute, Michigan Technological University, Houghton, MI 49931
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Raj M, Ayub A, Pal AK, Pradhan J, Varish N, Kumar S, Varikasuvu SR. Diagnostic Accuracy of Artificial Intelligence-Based Algorithms in Automated Detection of Neck of Femur Fracture on a Plain Radiograph: A Systematic Review and Meta-analysis. Indian J Orthop 2024; 58:457-469. [PMID: 38694696 PMCID: PMC11058182 DOI: 10.1007/s43465-024-01130-6] [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: 12/26/2023] [Accepted: 02/27/2024] [Indexed: 05/04/2024]
Abstract
Objectives To evaluate the diagnostic accuracy of artificial intelligence-based algorithms in identifying neck of femur fracture on a plain radiograph. Design Systematic review and meta-analysis. Data sources PubMed, Web of science, Scopus, IEEE, and the Science direct databases were searched from inception to 30 July 2023. Eligibility criteria for study selection Eligible article types were descriptive, analytical, or trial studies published in the English language providing data on the utility of artificial intelligence (AI) based algorithms in the detection of the neck of the femur (NOF) fracture on plain X-ray. Main outcome measures The prespecified primary outcome was to calculate the sensitivity, specificity, accuracy, Youden index, and positive and negative likelihood ratios. Two teams of reviewers (each consisting of two members) extracted the data from available information in each study. The risk of bias was assessed using a mix of the CLAIM (the Checklist for AI in Medical Imaging) and QUADAS-2 (A Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies) criteria. Results Of the 437 articles retrieved, five were eligible for inclusion, and the pooled sensitivity of AIs in diagnosing the fracture NOF was 85%, with a specificity of 87%. For all studies, the pooled Youden index (YI) was 0.73. The average positive likelihood ratio (PLR) was 19.88, whereas the negative likelihood ratio (NLR) was 0.17. The random effects model showed an overall odds of 1.16 (0.84-1.61) in the forest plot, comparing the AI system with those of human diagnosis. The overall heterogeneity of the studies was marginal (I2 = 51%). The CLAIM criteria for risk of bias assessment had an overall >70% score. Conclusion Artificial intelligence (AI)-based algorithms can be used as a diagnostic adjunct, benefiting clinicians by taking less time and effort in neck of the femur (NOF) fracture diagnosis. Study registration PROSPERO CRD42022375449. Supplementary Information The online version contains supplementary material available at 10.1007/s43465-024-01130-6.
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Affiliation(s)
- Manish Raj
- Department of Orthopaedic, All India Institute of Medical Sciences, Deoghar, Jharkhand India
| | - Arshad Ayub
- Department of Community and Family Medicine, All India Institute of Medical Sciences, Deoghar, Jharkhand India
| | - Arup Kumar Pal
- Department of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand India
| | - Jitesh Pradhan
- Department of Computer Science and Engineering, National Institute of Technology (NIT), Jamshedpur, Jharkhand India
| | - Naushad Varish
- Department of Computer Science and Engineering, GITAM University, Hyderabad Campus, Telangana, India
| | - Sumit Kumar
- Informatics Cluster, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand India
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Awal R, Faisal T. QCT-based 3D finite element modeling to assess patient-specific hip fracture risk and risk factors. J Mech Behav Biomed Mater 2024; 150:106299. [PMID: 38088011 DOI: 10.1016/j.jmbbm.2023.106299] [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: 07/03/2023] [Revised: 09/12/2023] [Accepted: 12/02/2023] [Indexed: 01/09/2024]
Abstract
Early assessment of hip fracture risk may play a critical role in designing preventive mechanisms to reduce the occurrence of hip fracture in geriatric people. The loading direction, clinical, and morphological variables play a vital role in hip fracture. Analyzing the effects of these variables helps predict fractures risk more accurately; thereby suggesting the critical variable that needs to be considered. Hence, this work considered the fall postures by varying the loading direction on the coronal plane (α) and on the transverse plane (β) along with the clinical variables-age, sex, weight, and bone mineral density, and morphological variables-femoral neck axis length, femoral neck width, femoral neck angle, and true moment arm. The strain distribution obtained via finite element analysis (FEA) shows that the angle of adduction (α) during a fall increases the risk of fracture at the greater trochanter and femoral neck, whereas with an increased angle of rotation (β) during the fall, the FRI increases by ∼1.35 folds. The statistical analysis of clinical, morphological, and loading variables (αandβ) delineates that the consideration of only one variable is not enough to realistically predict the possibility of fracture as the correlation between individual variables and FRI is less than 0.1, even though they are shown to be significant (p<0.01). On the contrary, the correlation (R2=0.48) increases as all variables are considered, suggesting the need for considering different variables fork predicting FRI. However, the effect of each variable is different. While loading, clinical, and morphological variables are considered together, the loading direction on transverse plane (β) has high significance, and the anatomical variabilities have no significance.
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Affiliation(s)
- Rabina Awal
- Department of Mechanical Engineering, University of Louisiana at Lafayette, Louisiana, USA
| | - Tanvir Faisal
- Department of Mechanical Engineering, University of Louisiana at Lafayette, Louisiana, USA.
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Pham TD, Holmes SB, Coulthard P. A review on artificial intelligence for the diagnosis of fractures in facial trauma imaging. Front Artif Intell 2024; 6:1278529. [PMID: 38249794 PMCID: PMC10797131 DOI: 10.3389/frai.2023.1278529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 12/11/2023] [Indexed: 01/23/2024] Open
Abstract
Patients with facial trauma may suffer from injuries such as broken bones, bleeding, swelling, bruising, lacerations, burns, and deformity in the face. Common causes of facial-bone fractures are the results of road accidents, violence, and sports injuries. Surgery is needed if the trauma patient would be deprived of normal functioning or subject to facial deformity based on findings from radiology. Although the image reading by radiologists is useful for evaluating suspected facial fractures, there are certain challenges in human-based diagnostics. Artificial intelligence (AI) is making a quantum leap in radiology, producing significant improvements of reports and workflows. Here, an updated literature review is presented on the impact of AI in facial trauma with a special reference to fracture detection in radiology. The purpose is to gain insights into the current development and demand for future research in facial trauma. This review also discusses limitations to be overcome and current important issues for investigation in order to make AI applications to the trauma more effective and realistic in practical settings. The publications selected for review were based on their clinical significance, journal metrics, and journal indexing.
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Affiliation(s)
- Tuan D. Pham
- Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
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10
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Lee KS, Jung SH, Kim DH, Chung SW, Yoon JP. Artificial intelligence- and computer-assisted navigation for shoulder surgery. J Orthop Surg (Hong Kong) 2024; 32:10225536241243166. [PMID: 38546214 DOI: 10.1177/10225536241243166] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/28/2024] Open
Abstract
Background: Over the last few decades, shoulder surgery has undergone rapid advancements, with ongoing exploration and the development of innovative technological approaches. In the coming years, technologies such as robot-assisted surgeries, virtual reality, artificial intelligence, patient-specific instrumentation, and different innovative perioperative and preoperative planning tools will continue to fuel a revolution in the medical field, thereby pushing it toward new frontiers and unprecedented advancements. In relation to this, shoulder surgery will experience significant breakthroughs. Main body: Recent advancements and technological innovations in the field were comprehensively analyzed. We aimed to provide a detailed overview of the current landscape, emphasizing the roles of technologies. Computer-assisted surgery utilizing robotic- or image-guided technologies is widely adopted in various orthopedic specialties. The most advanced components of computer-assisted surgery are navigation and robotic systems, with functions and applications that are continuously expanding. Surgical navigation requires a visual system that presents real-time positional data on surgical instruments or implants in relation to the target bone, displayed on a computer monitor. There are three primary categories of surgical planning that utilize navigation systems. The initial category involves volumetric images, such as ultrasound echogram, computed tomography, and magnetic resonance images. The second type is based on intraoperative fluoroscopic images, and the third type incorporates kinetic information about joints or morphometric data about the target bones acquired intraoperatively. Conclusion: The rapid integration of artificial intelligence and deep learning into the medical domain has a significant and transformative influence. Numerous studies utilizing deep learning-based diagnostics in orthopedics have remarkable achievements and performance.
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Affiliation(s)
- Kang-San Lee
- Department of Orthopaedic Surgery, School of Medicine, Kyungpook National University, Daegu, Korea
| | - Seung Ho Jung
- Department of Orthopaedic Surgery, School of Medicine, Kyungpook National University, Daegu, Korea
| | - Dong-Hyun Kim
- Department of Orthopaedic Surgery, School of Medicine, Kyungpook National University, Daegu, Korea
| | - Seok Won Chung
- Department of Orthopaedic Surgery, School of Medicine, Konkuk University Medical Center, Seoul, Korea
| | - Jong Pil Yoon
- Department of Orthopaedic Surgery, School of Medicine, Kyungpook National University, Daegu, Korea
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Tian CW, Chen XX, Shi L, Zhu HY, Dai GC, Chen H, Rui YF. Machine learning applications for the prediction of extended length of stay in geriatric hip fracture patients. World J Orthop 2023; 14:741-754. [PMID: 37970626 PMCID: PMC10642403 DOI: 10.5312/wjo.v14.i10.741] [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: 07/22/2023] [Revised: 09/08/2023] [Accepted: 09/28/2023] [Indexed: 10/16/2023] Open
Abstract
BACKGROUND Geriatric hip fractures are one of the most common fractures in elderly individuals, and prolonged hospital stays increase the risk of death and complications. Machine learning (ML) has become prevalent in clinical data processing and predictive models. This study aims to develop ML models for predicting extended length of stay (eLOS) among geriatric patients with hip fractures and to identify the associated risk factors. AIM To develop ML models for predicting the eLOS among geriatric patients with hip fractures, identify associated risk factors, and compare the performance of each model. METHODS A retrospective study was conducted at a single orthopaedic trauma centre, enrolling all patients who underwent hip fracture surgery between January 2018 and December 2022. The study collected various patient characteristics, encompassing demographic data, general health status, injury-related data, laboratory examinations, surgery-related data, and length of stay. Features that exhibited significant differences in univariate analysis were integrated into the ML model establishment and subsequently cross-verified. The study compared the performance of the ML models and determined the risk factors for eLOS. RESULTS The study included 763 patients, with 380 experiencing eLOS. Among the models, the decision tree, random forest, and extreme Gradient Boosting models demonstrated the most robust performance. Notably, the artificial neural network model also exhibited impressive results. After cross-validation, the support vector machine and logistic regression models demonstrated superior performance. Predictors for eLOS included delayed surgery, D-dimer level, American Society of Anaesthesiologists (ASA) classification, type of surgery, and sex. CONCLUSION ML proved to be highly accurate in predicting the eLOS for geriatric patients with hip fractures. The identified key risk factors were delayed surgery, D-dimer level, ASA classification, type of surgery, and sex. This valuable information can aid clinicians in allocating resources more efficiently to meet patient demand effectively.
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Affiliation(s)
- Chu-Wei Tian
- Department of Orthopaedics, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Multidisciplinary Team for Geriatric Hip Fracture Comprehensive Management, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
| | - Xiang-Xu Chen
- Department of Orthopaedics, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Multidisciplinary Team for Geriatric Hip Fracture Comprehensive Management, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
| | - Liu Shi
- Department of Orthopaedics, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Multidisciplinary Team for Geriatric Hip Fracture Comprehensive Management, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
| | - Huan-Yi Zhu
- Department of Orthopaedics, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Multidisciplinary Team for Geriatric Hip Fracture Comprehensive Management, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
| | - Guang-Chun Dai
- Department of Orthopaedics, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Multidisciplinary Team for Geriatric Hip Fracture Comprehensive Management, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
| | - Hui Chen
- Department of Orthopaedics, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Multidisciplinary Team for Geriatric Hip Fracture Comprehensive Management, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
| | - Yun-Feng Rui
- Department of Orthopaedics, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Multidisciplinary Team for Geriatric Hip Fracture Comprehensive Management, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
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Gao Y, Soh NYT, Liu N, Lim G, Ting D, Cheng LTE, Wong KM, Liew C, Oh HC, Tan JR, Venkataraman N, Goh SH, Yan YY. Application of a deep learning algorithm in the detection of hip fractures. iScience 2023; 26:107350. [PMID: 37554447 PMCID: PMC10404720 DOI: 10.1016/j.isci.2023.107350] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 05/30/2023] [Accepted: 07/06/2023] [Indexed: 08/10/2023] Open
Abstract
This paper describes the development of a deep learning model for prediction of hip fractures on pelvic radiographs (X-rays). Developed using over 40,000 pelvic radiographs from a single institution, the model demonstrated high sensitivity and specificity when applied to a test set of emergency department radiographs. This study approximates the real-world application of a deep learning fracture detection model by including radiographs with sub-optimal image quality, other non-hip fractures, and metallic implants, which were excluded from prior published work. The study also explores the effect of ethnicity on model performance, as well as the accuracy of visualization algorithm for fracture localization.
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Affiliation(s)
- Yan Gao
- Health Services Research, Changi General Hospital, Singapore Health Services (SingHealth), Singapore, Singapore
| | - Nicholas Yock Teck Soh
- Department of Diagnostic Radiology, Changi General Hospital, Singapore Health Services (SingHealth), Singapore, Singapore
| | - Nan Liu
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore
| | - Gilbert Lim
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore
| | - Daniel Ting
- Singapore Health Services (SingHealth), Duke-NUS Medical School, Singapore, Singapore
| | - Lionel Tim-Ee Cheng
- Department of Diagnostic Radiology, Singapore General Hospital, Singapore Health Services (SingHealth), Singapore, Singapore
- Radiological Sciences ACP, Duke-NUS Medical School, Singapore, Singapore
| | - Kang Min Wong
- Department of Diagnostic Radiology, Changi General Hospital, Singapore Health Services (SingHealth), Singapore, Singapore
- Radiological Sciences ACP, Duke-NUS Medical School, Singapore, Singapore
| | - Charlene Liew
- Department of Diagnostic Radiology, Changi General Hospital, Singapore Health Services (SingHealth), Singapore, Singapore
- Radiological Sciences ACP, Duke-NUS Medical School, Singapore, Singapore
| | - Hong Choon Oh
- Health Services Research, Changi General Hospital, Singapore Health Services (SingHealth), Singapore, Singapore
| | - Jin Rong Tan
- Department of Diagnostic Radiology, Singapore General Hospital, Singapore Health Services (SingHealth), Singapore, Singapore
| | - Narayan Venkataraman
- Department of Medical Informatics, Changi General Hospital, Singapore Health Services (SingHealth), Singapore, Singapore
| | - Siang Hiong Goh
- Department of Emergency Medicine, Changi General Hospital, Singapore Health Services (SingHealth), Singapore, Singapore
| | - Yet Yen Yan
- Department of Diagnostic Radiology, Changi General Hospital, Singapore Health Services (SingHealth), Singapore, Singapore
- Radiological Sciences ACP, Duke-NUS Medical School, Singapore, Singapore
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Hussain A, Fareed A, Taseen S. Bone fracture detection-Can artificial intelligence replace doctors in orthopedic radiography analysis? Front Artif Intell 2023; 6:1223909. [PMID: 37593091 PMCID: PMC10427856 DOI: 10.3389/frai.2023.1223909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 07/20/2023] [Indexed: 08/19/2023] Open
Affiliation(s)
- Aariz Hussain
- Karachi Medical and Dental College and Abbasi Shaheed Hospital, Karachi, Sindh, Pakistan
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