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Zhu X, Liu D, Liu L, Guo J, Li Z, Zhao Y, Wu T, Liu K, Liu X, Pan X, Qi L, Zhang Y, Cheng L, Chen B. Fully Automatic Deep Learning Model for Spine Refracture in Patients with OVCF: A Multi-Center Study. Orthop Surg 2024. [PMID: 38952050 DOI: 10.1111/os.14155] [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: 03/11/2024] [Revised: 06/06/2024] [Accepted: 06/09/2024] [Indexed: 07/03/2024] Open
Abstract
BACKGROUND The reaserch of artificial intelligence (AI) model for predicting spinal refracture is limited to bone mineral density, X-ray and some conventional laboratory indicators, which has its own limitations. Besides, it lacks specific indicators related to osteoporosis and imaging factors that can better reflect bone quality, such as computed tomography (CT). OBJECTIVE To construct a novel predicting model based on bone turn-over markers and CT to identify patients who were more inclined to suffer spine refracture. METHODS CT images and clinical information of 383 patients (training set = 240 cases of osteoporotic vertebral compression fractures (OVCF), validation set = 63, test set = 80) were retrospectively collected from January 2015 to October 2022 at three medical centers. The U-net model was adopted to automatically segment ROI. Three-dimensional (3D) cropping of all spine regions was used to achieve the final ROI regions including 3D_Full and 3D_RoiOnly. We used the Densenet 121-3D model to model the cropped region and simultaneously build a T-NIPT prediction model. Diagnostics of deep learning models were assessed by constructing ROC curves. We generated calibration curves to assess the calibration performance. Additionally, decision curve analysis (DCA) was used to assess the clinical utility of the predictive models. RESULTS The performance of the test model is comparable to its performance on the training set (dice coefficients of 0.798, an mIOU of 0.755, an SA of 0.767, and an OS of 0.017). Univariable and multivariable analysis indicate that T_P1NT was an independent risk factor for refracture. The performance of predicting refractures in different ROI regions showed that 3D_Full model exhibits the highest calibration performance, with a Hosmer-Lemeshow goodness-of-fit (HL) test statistic exceeding 0.05. The analysis of the training and test sets showed that the 3D_Full model, which integrates clinical and deep learning results, demonstrated superior performance with significant improvement (p-value < 0.05) compared to using clinical features independently or using only 3D_RoiOnly. CONCLUSION T_P1NT was an independent risk factor of refracture. Our 3D-FULL model showed better performance in predicting high-risk population of spine refracture than other models and junior doctors do. This model can be applicable to real-world translation due to its automatic segmentation and detection.
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Affiliation(s)
- Xuetao Zhu
- Department of Orthopaedic Surgery, Qilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong University, Jinan, P. R. China
| | - Dejian Liu
- Department of Orthopaedic Surgery, Qilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong University, Jinan, P. R. China
| | - Lian Liu
- Department of Emergency Surgery, Qilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong University, Jinan, P. R. China
| | - Jingxuan Guo
- Department of anesthesiology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Zedi Li
- Department of Orthopaedic Surgery, Qilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong University, Jinan, P. R. China
| | - Yixiang Zhao
- Department of Orthopaedic Surgery, Yantaishan Hospital, Yantai, China
| | - Tianhao Wu
- Department of Hepatopancreatobiliary Surgery, Graduate School of Dalian Medical University, Dalian, China
| | - Kaiwen Liu
- Department of Orthopaedic Surgery, Qilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong University, Jinan, P. R. China
| | - Xinyu Liu
- Department of Orthopaedic Surgery, Qilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong University, Jinan, P. R. China
| | - Xin Pan
- Department of Orthopaedic Surgery, Qilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong University, Jinan, P. R. China
| | - Lei Qi
- Department of Orthopaedic Surgery, Qilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong University, Jinan, P. R. China
| | - Yuanqiang Zhang
- Department of Orthopaedic Surgery, Qilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong University, Jinan, P. R. China
| | - Lei Cheng
- Department of Orthopaedic Surgery, Qilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong University, Jinan, P. R. China
| | - Bin Chen
- Department of Orthopaedic Surgery, Qilu Hospital of Shandong University, Cheeloo College of Medicine of Shandong University, Jinan, P. R. China
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Gan K, Liu Y, Zhang T, Xu D, Lian L, Luo Z, Li J, Lu L. Deep Learning Model for Automatic Identification and Classification of Distal Radius Fracture. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01144-4. [PMID: 38862852 DOI: 10.1007/s10278-024-01144-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 05/13/2024] [Accepted: 05/15/2024] [Indexed: 06/13/2024]
Abstract
Distal radius fracture (DRF) is one of the most common types of wrist fractures. We aimed to construct a model for the automatic segmentation of wrist radiographs using a deep learning approach and further perform automatic identification and classification of DRF. A total of 2240 participants with anteroposterior wrist radiographs from one hospital between January 2015 and October 2021 were included. The outcomes were automatic segmentation of wrist radiographs, identification of DRF, and classification of DRF (type A, type B, type C). The Unet model and Fast-RCNN model were used for automatic segmentation. The DenseNet121 model and ResNet50 model were applied to DRF identification of DRF. The DenseNet121 model, ResNet50 model, VGG-19 model, and InceptionV3 model were used for DRF classification. The area under the curve (AUC) with 95% confidence interval (CI), accuracy, precision, and F1-score was utilized to assess the effectiveness of the identification and classification models. Of these 2240 participants, 1440 (64.3%) had DRF, of which 701 (48.7%) were type A, 278 (19.3%) were type B, and 461 (32.0%) were type C. Both the Unet model and the Fast-RCNN model showed good segmentation of wrist radiographs. For DRF identification, the AUCs of the DenseNet121 model and the ResNet50 model in the testing set were 0.941 (95%CI: 0.926-0.965) and 0.936 (95%CI: 0.913-0.955), respectively. The AUCs of the DenseNet121 model (testing set) for classification type A, type B, and type C were 0.96, 0.96, and 0.96, respectively. The DenseNet121 model may provide clinicians with a tool for interpreting wrist radiographs.
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Affiliation(s)
- Kaifeng Gan
- Department of Orthopaedics, the Affiliated LiHuiLi Hospital of Ningbo University, No. 57 Xingning Road, Yinzhou District, Ningbo, 315211, Zhejiang, China
| | - Yunpeng Liu
- Ningbo University of Technology, Ningbo, 315100, Zhejiang, China
| | - Ting Zhang
- Department of Orthopaedics, the Affiliated LiHuiLi Hospital of Ningbo University, No. 57 Xingning Road, Yinzhou District, Ningbo, 315211, Zhejiang, China
| | - Dingli Xu
- Health Science Center, Ningbo University, Ningbo, 315000, Zhejiang, China
| | - Leidong Lian
- Health Science Center, Ningbo University, Ningbo, 315000, Zhejiang, China
| | - Zhe Luo
- Health Science Center, Ningbo University, Ningbo, 315000, Zhejiang, China
| | - Jin Li
- Department of Orthopaedics, the Affiliated LiHuiLi Hospital of Ningbo University, No. 57 Xingning Road, Yinzhou District, Ningbo, 315211, Zhejiang, China
| | - Liangjie Lu
- Department of Orthopaedics, the Affiliated LiHuiLi Hospital of Ningbo University, No. 57 Xingning Road, Yinzhou District, Ningbo, 315211, Zhejiang, China.
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Nadeem SA, Comellas AP, Regan EA, Hoffman EA, Saha PK. Chest CT-based automated vertebral fracture assessment using artificial intelligence and morphologic features. Med Phys 2024; 51:4201-4218. [PMID: 38721977 DOI: 10.1002/mp.17072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 04/02/2024] [Accepted: 04/02/2024] [Indexed: 06/05/2024] Open
Abstract
BACKGROUND Spinal degeneration and vertebral compression fractures are common among the elderly that adversely affect their mobility, quality of life, lung function, and mortality. Assessment of vertebral fractures in chronic obstructive pulmonary disease (COPD) is important due to the high prevalence of osteoporosis and associated vertebral fractures in COPD. PURPOSE We present new automated methods for (1) segmentation and labelling of individual vertebrae in chest computed tomography (CT) images using deep learning (DL), multi-parametric freeze-and-grow (FG) algorithm, and separation of apparently fused vertebrae using intensity autocorrelation and (2) vertebral deformity fracture detection using computed vertebral height features and parametric computational modelling of an established protocol outlined for trained human experts. METHODS A chest CT-based automated method was developed for quantitative deformity fracture assessment following the protocol by Genant et al. The computational method was accomplished in the following steps: (1) computation of a voxel-level vertebral body likelihood map from chest CT using a trained DL network; (2) delineation and labelling of individual vertebrae on the likelihood map using an iterative multi-parametric FG algorithm; (3) separation of apparently fused vertebrae in CT using intensity autocorrelation; (4) computation of vertebral heights using contour analysis on the central anterior-posterior (AP) plane of a vertebral body; (5) assessment of vertebral fracture status using ratio functions of vertebral heights and optimized thresholds. The method was applied to inspiratory or total lung capacity (TLC) chest scans from the multi-site Genetic Epidemiology of COPD (COPDGene) (ClinicalTrials.gov: NCT00608764) study, and the performance was examined (n = 3231). One hundred and twenty scans randomly selected from this dataset were partitioned into training (n = 80) and validation (n = 40) datasets for the DL-based vertebral body classifier. Also, generalizability of the method to low dose CT imaging (n = 236) was evaluated. RESULTS The vertebral segmentation module achieved a Dice score of .984 as compared to manual outlining results as reference (n = 100); the segmentation performance was consistent across images with the minimum and maximum of Dice scores among images being .980 and .989, respectively. The vertebral labelling module achieved 100% accuracy (n = 100). For low dose CT, the segmentation module produced image-level minimum and maximum Dice scores of .995 and .999, respectively, as compared to standard dose CT as the reference; vertebral labelling at low dose CT was fully consistent with standard dose CT (n = 236). The fracture assessment method achieved overall accuracy, sensitivity, and specificity of 98.3%, 94.8%, and 98.5%, respectively, for 40,050 vertebrae from 3231 COPDGene participants. For generalizability experiments, fracture assessment from low dose CT was consistent with the reference standard dose CT results across all participants. CONCLUSIONS Our CT-based automated method for vertebral fracture assessment is accurate, and it offers a feasible alternative to manual expert reading, especially for large population-based studies, where automation is important for high efficiency. Generalizability of the method to low dose CT imaging further extends the scope of application of the method, particularly since the usage of low dose CT imaging in large population-based studies has increased to reduce cumulative radiation exposure.
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Affiliation(s)
- Syed Ahmed Nadeem
- Department of Radiology, Carver College of Medicine, The University of Iowa, Iowa City, Iowa, USA
| | - Alejandro P Comellas
- Department of Internal Medicine, Carver College of Medicine, The University of Iowa, Iowa City, Iowa, USA
| | - Elizabeth A Regan
- Department of Epidemiology, Colorado School of Public Health, University of Colorado, Aurora, Colorado, USA
- Division of Rheumatology, National Jewish Health, Denver, Colorado, USA
| | - Eric A Hoffman
- Department of Radiology, Carver College of Medicine, The University of Iowa, Iowa City, Iowa, USA
- Department of Internal Medicine, Carver College of Medicine, The University of Iowa, Iowa City, Iowa, USA
- Department of Biomedical Engineering, College of Engineering, The University of Iowa, Iowa City, Iowa, USA
| | - Punam K Saha
- Department of Radiology, Carver College of Medicine, The University of Iowa, Iowa City, Iowa, USA
- Department of Electrical and Computer Engineering, College of Engineering, The University of Iowa, Iowa City, Iowa, USA
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Oeding JF, Kunze KN, Messer CJ, Pareek A, Fufa DT, Pulos N, Rhee PC. Diagnostic Performance of Artificial Intelligence for Detection of Scaphoid and Distal Radius Fractures: A Systematic Review. J Hand Surg Am 2024; 49:411-422. [PMID: 38551529 DOI: 10.1016/j.jhsa.2024.01.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 01/19/2024] [Accepted: 01/31/2024] [Indexed: 05/05/2024]
Abstract
PURPOSE To review the existing literature to (1) determine the diagnostic efficacy of artificial intelligence (AI) models for detecting scaphoid and distal radius fractures and (2) compare the efficacy to human clinical experts. METHODS PubMed, OVID/Medline, and Cochrane libraries were queried for studies investigating the development, validation, and analysis of AI for the detection of scaphoid or distal radius fractures. Data regarding study design, AI model development and architecture, prediction accuracy/area under the receiver operator characteristic curve (AUROC), and imaging modalities were recorded. RESULTS A total of 21 studies were identified, of which 12 (57.1%) used AI to detect fractures of the distal radius, and nine (42.9%) used AI to detect fractures of the scaphoid. AI models demonstrated good diagnostic performance on average, with AUROC values ranging from 0.77 to 0.96 for scaphoid fractures and from 0.90 to 0.99 for distal radius fractures. Accuracy of AI models ranged between 72.0% to 90.3% and 89.0% to 98.0% for scaphoid and distal radius fractures, respectively. When compared to clinical experts, 13 of 14 (92.9%) studies reported that AI models demonstrated comparable or better performance. The type of fracture influenced model performance, with worse overall performance on occult scaphoid fractures; however, models trained specifically on occult fractures demonstrated substantially improved performance when compared to humans. CONCLUSIONS AI models demonstrated excellent performance for detecting scaphoid and distal radius fractures, with the majority demonstrating comparable or better performance compared with human experts. Worse performance was demonstrated on occult fractures. However, when trained specifically on difficult fracture patterns, AI models demonstrated improved performance. CLINICAL RELEVANCE AI models can help detect commonly missed occult fractures while enhancing workflow efficiency for distal radius and scaphoid fracture diagnoses. As performance varies based on fracture type, future studies focused on wrist fracture detection should clearly define whether the goal is to (1) identify difficult-to-detect fractures or (2) improve workflow efficiency by assisting in routine tasks.
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Affiliation(s)
- Jacob F Oeding
- School of Medicine, Mayo Clinic Alix School of Medicine, Rochester, MN; Department of Orthopaedics, Institute of Clinical Sciences, The Sahlgrenska Academy, University of Gotenburg, Gothenburg, Sweden.
| | - Kyle N Kunze
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY
| | - Caden J Messer
- School of Medicine, Mayo Clinic Alix School of Medicine, Rochester, MN
| | - Ayoosh Pareek
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY
| | - Duretti T Fufa
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY
| | - Nicholas Pulos
- Department of Orthopaedic Surgery, Mayo Clinic, Rochester, MN
| | - Peter C Rhee
- Department of Orthopaedic Surgery, Mayo Clinic, Rochester, MN
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Carriero A, Groenhoff L, Vologina E, Basile P, Albera M. Deep Learning in Breast Cancer Imaging: State of the Art and Recent Advancements in Early 2024. Diagnostics (Basel) 2024; 14:848. [PMID: 38667493 PMCID: PMC11048882 DOI: 10.3390/diagnostics14080848] [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: 02/29/2024] [Revised: 04/07/2024] [Accepted: 04/17/2024] [Indexed: 04/28/2024] Open
Abstract
The rapid advancement of artificial intelligence (AI) has significantly impacted various aspects of healthcare, particularly in the medical imaging field. This review focuses on recent developments in the application of deep learning (DL) techniques to breast cancer imaging. DL models, a subset of AI algorithms inspired by human brain architecture, have demonstrated remarkable success in analyzing complex medical images, enhancing diagnostic precision, and streamlining workflows. DL models have been applied to breast cancer diagnosis via mammography, ultrasonography, and magnetic resonance imaging. Furthermore, DL-based radiomic approaches may play a role in breast cancer risk assessment, prognosis prediction, and therapeutic response monitoring. Nevertheless, several challenges have limited the widespread adoption of AI techniques in clinical practice, emphasizing the importance of rigorous validation, interpretability, and technical considerations when implementing DL solutions. By examining fundamental concepts in DL techniques applied to medical imaging and synthesizing the latest advancements and trends, this narrative review aims to provide valuable and up-to-date insights for radiologists seeking to harness the power of AI in breast cancer care.
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Affiliation(s)
| | - Léon Groenhoff
- Radiology Department, Maggiore della Carità Hospital, 28100 Novara, Italy; (A.C.); (E.V.); (P.B.); (M.A.)
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Polzer C, Yilmaz E, Meyer C, Jang H, Jansen O, Lorenz C, Bürger C, Glüer CC, Sedaghat S. AI-based automated detection and stability analysis of traumatic vertebral body fractures on computed tomography. Eur J Radiol 2024; 173:111364. [PMID: 38364589 DOI: 10.1016/j.ejrad.2024.111364] [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: 11/04/2023] [Revised: 12/29/2023] [Accepted: 02/08/2024] [Indexed: 02/18/2024]
Abstract
PURPOSE We developed and tested a neural network for automated detection and stability analysis of vertebral body fractures on computed tomography (CT). MATERIALS AND METHODS 257 patients who underwent CT were included in this Institutional Review Board (IRB) approved study. 463 fractured and 1883 non-fractured vertebral bodies were included, with 190 fractures unstable. Two readers identified vertebral body fractures and assessed their stability. A combination of a Hierarchical Convolutional Neural Network (hNet) and a fracture Classification Network (fNet) was used to build a neural network for the automated detection and stability analysis of vertebral body fractures on CT. Two final test settings were chosen: one with vertebral body levels C1/2 included and one where they were excluded. RESULTS The mean age of the patients was 68 ± 14 years. 140 patients were female. The network showed a slightly higher diagnostic performance when excluding C1/2. Accordingly, the network was able to distinguish fractured and non-fractured vertebral bodies with a sensitivity of 75.8 % and a specificity of 80.3 %. Additionally, the network determined the stability of the vertebral bodies with a sensitivity of 88.4 % and a specificity of 80.3 %. The AUC was 87 % and 91 % for fracture detection and stability analysis, respectively. The sensitivity of our network in indicating the presence of at least one fracture / one unstable fracture within the whole spine achieved values of 78.7 % and 97.2 %, respectively, when excluding C1/2. CONCLUSION The developed neural network can automatically detect vertebral body fractures and evaluate their stability concurrently with a high diagnostic performance.
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Affiliation(s)
- Constanze Polzer
- Department of Radiology and Neuroradiology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Eren Yilmaz
- Section Biomedical Imaging, Department of Radiology and Neuroradiology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany; Department of Computer Science, Ostfalia University of Applied Sciences, Wolfenbüttel, Germany
| | - Carsten Meyer
- Department of Computer Science, Ostfalia University of Applied Sciences, Wolfenbüttel, Germany; Department of Computer Science, Faculty of Engineering, Kiel University, Kiel, Germany
| | - Hyungseok Jang
- Department of Radiology, University of California San Diego, San Diego, USA
| | - Olav Jansen
- Department of Radiology and Neuroradiology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | | | | | - Claus-Christian Glüer
- Section Biomedical Imaging, Department of Radiology and Neuroradiology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Sam Sedaghat
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany.
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Bhatnagar A, Kekatpure AL, Velagala VR, Kekatpure A. A Review on the Use of Artificial Intelligence in Fracture Detection. Cureus 2024; 16:e58364. [PMID: 38756254 PMCID: PMC11097122 DOI: 10.7759/cureus.58364] [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: 10/28/2023] [Accepted: 04/16/2024] [Indexed: 05/18/2024] Open
Abstract
Artificial intelligence (AI) simulates intelligent behavior using computers with minimum human intervention. Recent advances in AI, especially deep learning, have made significant progress in perceptual operations, enabling computers to convey and comprehend complicated input more accurately. Worldwide, fractures affect people of all ages and in all regions of the planet. One of the most prevalent causes of inaccurate diagnosis and medical lawsuits is overlooked fractures on radiographs taken in the emergency room, which can range from 2% to 9%. The workforce will soon be under a great deal of strain due to the growing demand for fracture detection on multiple imaging modalities. A dearth of radiologists worsens this rise in demand as a result of a delay in hiring and a significant percentage of radiologists close to retirement. Additionally, the process of interpreting diagnostic images can sometimes be challenging and tedious. Integrating orthopedic radio-diagnosis with AI presents a promising solution to these problems. There has recently been a noticeable rise in the application of deep learning techniques, namely convolutional neural networks (CNNs), in medical imaging. In the field of orthopedic trauma, CNNs are being documented to operate at the proficiency of expert orthopedic surgeons and radiologists in the identification and categorization of fractures. CNNs can analyze vast amounts of data at a rate that surpasses that of human observations. In this review, we discuss the use of deep learning methods in fracture detection and classification, the integration of AI with various imaging modalities, and the benefits and disadvantages of integrating AI with radio-diagnostics.
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Affiliation(s)
- Aayushi Bhatnagar
- Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Aditya L Kekatpure
- Orthopedic Surgery, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Vivek R Velagala
- Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Aashay Kekatpure
- Orthopedic Surgery, Narendra Kumar Prasadrao Salve Institute of Medical Sciences and Research, Nagpur, IND
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Alzubaidi L, Salhi A, A.Fadhel M, Bai J, Hollman F, Italia K, Pareyon R, Albahri AS, Ouyang C, Santamaría J, Cutbush K, Gupta A, Abbosh A, Gu Y. Trustworthy deep learning framework for the detection of abnormalities in X-ray shoulder images. PLoS One 2024; 19:e0299545. [PMID: 38466693 PMCID: PMC10927121 DOI: 10.1371/journal.pone.0299545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 02/12/2024] [Indexed: 03/13/2024] Open
Abstract
Musculoskeletal conditions affect an estimated 1.7 billion people worldwide, causing intense pain and disability. These conditions lead to 30 million emergency room visits yearly, and the numbers are only increasing. However, diagnosing musculoskeletal issues can be challenging, especially in emergencies where quick decisions are necessary. Deep learning (DL) has shown promise in various medical applications. However, previous methods had poor performance and a lack of transparency in detecting shoulder abnormalities on X-ray images due to a lack of training data and better representation of features. This often resulted in overfitting, poor generalisation, and potential bias in decision-making. To address these issues, a new trustworthy DL framework has been proposed to detect shoulder abnormalities (such as fractures, deformities, and arthritis) using X-ray images. The framework consists of two parts: same-domain transfer learning (TL) to mitigate imageNet mismatch and feature fusion to reduce error rates and improve trust in the final result. Same-domain TL involves training pre-trained models on a large number of labelled X-ray images from various body parts and fine-tuning them on the target dataset of shoulder X-ray images. Feature fusion combines the extracted features with seven DL models to train several ML classifiers. The proposed framework achieved an excellent accuracy rate of 99.2%, F1Score of 99.2%, and Cohen's kappa of 98.5%. Furthermore, the accuracy of the results was validated using three visualisation tools, including gradient-based class activation heat map (Grad CAM), activation visualisation, and locally interpretable model-independent explanations (LIME). The proposed framework outperformed previous DL methods and three orthopaedic surgeons invited to classify the test set, who obtained an average accuracy of 79.1%. The proposed framework has proven effective and robust, improving generalisation and increasing trust in the final results.
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Affiliation(s)
- Laith Alzubaidi
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia
- Queensland Unit for Advanced Shoulder Research (QUASR)/ARC Industrial Transformation Training Centre—Joint Biomechanics, Queensland University of Technology, Brisbane, QLD, Australia
- Centre for Data Science, Queensland University of Technology, Brisbane, QLD, Australia
- Akunah Medical Technology Pty Ltd Company, Brisbane, QLD, Australia
| | - Asma Salhi
- Queensland Unit for Advanced Shoulder Research (QUASR)/ARC Industrial Transformation Training Centre—Joint Biomechanics, Queensland University of Technology, Brisbane, QLD, Australia
- Akunah Medical Technology Pty Ltd Company, Brisbane, QLD, Australia
| | | | - Jinshuai Bai
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia
- Queensland Unit for Advanced Shoulder Research (QUASR)/ARC Industrial Transformation Training Centre—Joint Biomechanics, Queensland University of Technology, Brisbane, QLD, Australia
| | - Freek Hollman
- Queensland Unit for Advanced Shoulder Research (QUASR)/ARC Industrial Transformation Training Centre—Joint Biomechanics, Queensland University of Technology, Brisbane, QLD, Australia
| | - Kristine Italia
- Akunah Medical Technology Pty Ltd Company, Brisbane, QLD, Australia
| | - Roberto Pareyon
- Queensland Unit for Advanced Shoulder Research (QUASR)/ARC Industrial Transformation Training Centre—Joint Biomechanics, Queensland University of Technology, Brisbane, QLD, Australia
| | - A. S. Albahri
- Technical College, Imam Ja’afar Al-Sadiq University, Baghdad, Iraq
| | - Chun Ouyang
- School of Information Systems, Queensland University of Technology, Brisbane, QLD, Australia
| | - Jose Santamaría
- Department of Computer Science, University of Jaén, Jaén, Spain
| | - Kenneth Cutbush
- Queensland Unit for Advanced Shoulder Research (QUASR)/ARC Industrial Transformation Training Centre—Joint Biomechanics, Queensland University of Technology, Brisbane, QLD, Australia
- School of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Ashish Gupta
- Queensland Unit for Advanced Shoulder Research (QUASR)/ARC Industrial Transformation Training Centre—Joint Biomechanics, Queensland University of Technology, Brisbane, QLD, Australia
- Akunah Medical Technology Pty Ltd Company, Brisbane, QLD, Australia
- Greenslopes Private Hospital, Brisbane, QLD, Australia
| | - Amin Abbosh
- School of Information Technology and Electrical Engineering, Brisbane, QLD, Australia
| | - Yuantong Gu
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia
- Queensland Unit for Advanced Shoulder Research (QUASR)/ARC Industrial Transformation Training Centre—Joint Biomechanics, Queensland University of Technology, Brisbane, QLD, Australia
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Xie Y, Li X, Chen F, Wen R, Jing Y, Liu C, Wang J. Artificial intelligence diagnostic model for multi-site fracture X-ray images of extremities based on deep convolutional neural networks. Quant Imaging Med Surg 2024; 14:1930-1943. [PMID: 38415122 PMCID: PMC10895109 DOI: 10.21037/qims-23-878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 11/24/2023] [Indexed: 02/29/2024]
Abstract
Background The rapid and accurate diagnosis of fractures is crucial for timely treatment of trauma patients. Deep learning, one of the most widely used forms of artificial intelligence (AI), is now commonly employed in medical imaging for fracture detection. This study aimed to construct a deep learning model using big data to recognize multiple-fracture X-ray images of extremity bones. Methods Radiographic imaging data of extremities were retrospectively collected from five hospitals between January 2017 and September 2020. The total number of people finally included was 25,635 and the total number of images included was 26,098. After labeling the lesions, the randomized method used 90% of the data as the training set to develop the fracture detection model, and the remaining 10% was used as the validation set to verify the model. The faster region convolutional neural networks (R-CNN) algorithm was adopted to construct diagnostic models for detection. The Dice coefficient was used to evaluate the image segmentation accuracy. The performances of detection models were evaluated with sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Results The free-response receiver operating characteristic (FROC) curve value was 0.886 and 0.843 for the detection of single and multiple fractures, respectively. Additionally, the effective identification AUC for all parts was higher than 0.920. Notably, the AUC for wrist fractures reached 0.952. The average accuracy in detecting bone fracture regions in the extremities was 0.865. When analyzing single and multiple lesions at the patient level, the sensitivity was 0.957 for patients with multiple lesions and 0.852 for those with single lesions. In the segmentation task, the training set (the data set used by the machine learning model to train and learn) and the validation set (the data set used to evaluate the performance of the model) reached 0.996 and 0.975, respectively. Conclusions The faster R-CNN training algorithm exhibits excellent performance in simultaneously identifying fractures in the hands, feet, wrists, ankles, radius and ulna, and tibia and fibula on X-ray images. It demonstrates high accuracy, low false-negative rates, and controllable false-positive rates. It can serve as a valuable screening tool.
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Affiliation(s)
- Yanling Xie
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Xiaoming Li
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Fengxi Chen
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Ru Wen
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Yang Jing
- Huiying Medical Technology Co., Ltd., Beijing, China
| | - Chen Liu
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Jian Wang
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
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Russe MF, Rebmann P, Tran PH, Kellner E, Reisert M, Bamberg F, Kotter E, Kim S. AI-based X-ray fracture analysis of the distal radius: accuracy between representative classification, detection and segmentation deep learning models for clinical practice. BMJ Open 2024; 14:e076954. [PMID: 38262641 PMCID: PMC10823998 DOI: 10.1136/bmjopen-2023-076954] [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: 06/22/2023] [Accepted: 12/21/2023] [Indexed: 01/25/2024] Open
Abstract
OBJECTIVES To aid in selecting the optimal artificial intelligence (AI) solution for clinical application, we directly compared performances of selected representative custom-trained or commercial classification, detection and segmentation models for fracture detection on musculoskeletal radiographs of the distal radius by aligning their outputs. DESIGN AND SETTING This single-centre retrospective study was conducted on a random subset of emergency department radiographs from 2008 to 2018 of the distal radius in Germany. MATERIALS AND METHODS An image set was created to be compatible with training and testing classification and segmentation models by annotating examinations for fractures and overlaying fracture masks, if applicable. Representative classification and segmentation models were trained on 80% of the data. After output binarisation, their derived fracture detection performances as well as that of a standard commercially available solution were compared on the remaining X-rays (20%) using mainly accuracy and area under the receiver operating characteristic (AUROC). RESULTS A total of 2856 examinations with 712 (24.9%) fractures were included in the analysis. Accuracies reached up to 0.97 for the classification model, 0.94 for the segmentation model and 0.95 for BoneView. Cohen's kappa was at least 0.80 in pairwise comparisons, while Fleiss' kappa was 0.83 for all models. Fracture predictions were visualised with all three methods at different levels of detail, ranking from downsampled image region for classification over bounding box for detection to single pixel-level delineation for segmentation. CONCLUSIONS All three investigated approaches reached high performances for detection of distal radius fractures with simple preprocessing and postprocessing protocols on the custom-trained models. Despite their underlying structural differences, selection of one's fracture analysis AI tool in the frame of this study reduces to the desired flavour of automation: automated classification, AI-assisted manual fracture reading or minimised false negatives.
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Affiliation(s)
- Maximilian Frederik Russe
- Department of Diagnostic and Interventional Radiology, Universitätsklinikum Freiburg Medizinische Universitätsklinik, Freiburg im Breisgau, Germany
| | - Philipp Rebmann
- Department of Diagnostic and Interventional Radiology, Universitätsklinikum Freiburg Medizinische Universitätsklinik, Freiburg im Breisgau, Germany
| | - Phuong Hien Tran
- Department of Diagnostic and Interventional Radiology, Universitätsklinikum Freiburg Medizinische Universitätsklinik, Freiburg im Breisgau, Germany
| | - Elias Kellner
- Department of Medical Physics, Universitätsklinikum Freiburg Medizinische Universitätsklinik, Freiburg im Breisgau, Germany
| | - Marco Reisert
- Department of Medical Physics, Universitätsklinikum Freiburg Medizinische Universitätsklinik, Freiburg im Breisgau, Germany
| | - Fabian Bamberg
- Department of Diagnostic and Interventional Radiology, Universitätsklinikum Freiburg Medizinische Universitätsklinik, Freiburg im Breisgau, Germany
| | - Elmar Kotter
- Department of Diagnostic and Interventional Radiology, Universitätsklinikum Freiburg Medizinische Universitätsklinik, Freiburg im Breisgau, Germany
| | - Suam Kim
- Department of Diagnostic and Interventional Radiology, Universitätsklinikum Freiburg Medizinische Universitätsklinik, Freiburg im Breisgau, Germany
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11
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Lee CL, Liu WJ, Tsai SF. Effects of AST-120 on mortality in patients with chronic kidney disease modeled by artificial intelligence or traditional statistical analysis. Sci Rep 2024; 14:738. [PMID: 38184721 PMCID: PMC10771424 DOI: 10.1038/s41598-024-51498-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 01/05/2024] [Indexed: 01/08/2024] Open
Abstract
Chronic kidney disease (CKD) imposes a substantial burden, and patient prognosis remains grim. The impact of AST-120 (AST-120) on the survival of CKD patients lacks a consensus. This study aims to investigate the effects of AST-120 usage on the survival of CKD patients and explore the utility of artificial intelligence models for decision-making. We conducted a retrospective analysis of CKD patients receiving care in the pre-end-stage renal disease (ESRD) program at Taichung Veterans General Hospital from 2000 to 2019. We employed Cox regression models to evaluate the relationship between AST-120 use and patient survival, both before and after propensity score matching. Subsequently, we employed Deep Neural Network (DNN) and Extreme Gradient Boosting (XGBoost) models to assess their performance in predicting AST-120's impact on patient survival. Among the 2584 patients in our cohort, 2199 did not use AST-120, while 385 patients received AST-120. AST-120 users exhibited significantly lower mortality rates compared to non-AST-120 users (13.51% vs. 37.88%, p < 0.0001) and a reduced prevalence of ESRD (44.16% vs. 53.17%, p = 0.0005). Propensity score matching at 1:1 and 1:2 revealed no significant differences, except for dialysis and all-cause mortality, where AST-120 users exhibited significantly lower all-cause mortality (p < 0.0001), with a hazard ratio (HR) of 0.395 (95% CI = 0.295-0.522). This difference remained statistically significant even after propensity matching. In terms of model performance, the XGBoost model demonstrated the highest accuracy (0.72), specificity (0.90), and positive predictive value (0.48), while the logistic regression model showed the highest sensitivity (0.63) and negative predictive value (0.84). The area under the curve (AUC) values for logistic regression, DNN, and XGBoost were 0.73, 0.73, and 0.69, respectively, indicating similar predictive capabilities for mortality. In this cohort of CKD patients, the use of AST-120 is significantly associated with reduced mortality. However, the performance of artificial intelligence models in predicting the impact of AST-120 is not superior to statistical analysis using the current architecture and algorithm.
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Grants
- TCVGH-1093605D, TCVGH-1097316C, TCVGH-1097327D, TCVGH-1103502C, TCVGH-1107305D, TCVGH-1117308C, TCVGH-1117305D, TCVGH-1113602C, TCVGH-1113602D and TCVGH-1103601D Taichung Veterans General Hospital
- TCVGH-1093605D, TCVGH-1097316C, TCVGH-1097327D, TCVGH-1103502C, TCVGH-1107305D, TCVGH-1117308C, TCVGH-1117305D, TCVGH-1113602C, TCVGH-1113602D and TCVGH-1103601D Taichung Veterans General Hospital
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Affiliation(s)
- Chia-Lin Lee
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
- Intelligent Data Mining Laboratory, Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
- Department of Public Health, College of Public Health, China Medical University, Taichung, Taiwan
- School of Medicine, National Yang-Ming University, Taipei, Taiwan
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan
| | - Wei-Ju Liu
- Intelligent Data Mining Laboratory, Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Shang-Feng Tsai
- School of Medicine, National Yang-Ming University, Taipei, Taiwan.
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan.
- Division of Nephrology, Taichung Veterans General Hospital, Taiwan, 160, Sec. 3, Taiwan Boulevard, Taichung, 407, Taiwan.
- Department of Life Science, Tunghai University, Taichung, Taiwan.
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12
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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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13
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田 楚, 陈 翔, 朱 桓, 秦 晟, 石 柳, 芮 云. [Application and prospect of machine learning in orthopaedic trauma]. ZHONGGUO XIU FU CHONG JIAN WAI KE ZA ZHI = ZHONGGUO XIUFU CHONGJIAN WAIKE ZAZHI = CHINESE JOURNAL OF REPARATIVE AND RECONSTRUCTIVE SURGERY 2023; 37:1562-1568. [PMID: 38130202 PMCID: PMC10739668 DOI: 10.7507/1002-1892.202308064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 10/13/2023] [Accepted: 10/19/2023] [Indexed: 12/23/2023]
Abstract
Objective To review the current applications of machine learning in orthopaedic trauma and anticipate its future role in clinical practice. Methods A comprehensive literature review was conducted to assess the status of machine learning algorithms in orthopaedic trauma research, both nationally and internationally. Results The rapid advancement of computer data processing and the growing convergence of medicine and industry have led to the widespread utilization of artificial intelligence in healthcare. Currently, machine learning plays a significant role in orthopaedic trauma, demonstrating high performance and accuracy in various areas including fracture image recognition, diagnosis stratification, clinical decision-making, evaluation, perioperative considerations, and prognostic risk prediction. Nevertheless, challenges persist in the development and clinical implementation of machine learning. These include limited database samples, model interpretation difficulties, and universality and individualisation variations. Conclusion The expansion of clinical sample sizes and enhancements in algorithm performance hold significant promise for the extensive application of machine learning in supporting orthopaedic trauma diagnosis, guiding decision-making, devising individualized medical strategies, and optimizing the allocation of clinical resources.
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Affiliation(s)
- 楚伟 田
- 东南大学附属中大医院骨科(南京 210009)Department of Orthopedics, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China
- 东南大学医学院(南京 210009)School of Medicine, Southeast University, Nanjing Jiangsu, 210009, P. R. China
| | - 翔溆 陈
- 东南大学附属中大医院骨科(南京 210009)Department of Orthopedics, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China
| | - 桓毅 朱
- 东南大学附属中大医院骨科(南京 210009)Department of Orthopedics, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China
- 东南大学医学院(南京 210009)School of Medicine, Southeast University, Nanjing Jiangsu, 210009, P. R. China
| | - 晟博 秦
- 东南大学附属中大医院骨科(南京 210009)Department of Orthopedics, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China
| | - 柳 石
- 东南大学附属中大医院骨科(南京 210009)Department of Orthopedics, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China
- 东南大学医学院(南京 210009)School of Medicine, Southeast University, Nanjing Jiangsu, 210009, P. R. China
- 东南大学附属中大医院创伤救治中心(南京 210009)Trauma Center, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China
| | - 云峰 芮
- 东南大学附属中大医院骨科(南京 210009)Department of Orthopedics, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China
- 东南大学医学院(南京 210009)School of Medicine, Southeast University, Nanjing Jiangsu, 210009, P. R. China
- 东南大学附属中大医院创伤救治中心(南京 210009)Trauma Center, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China
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14
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Chatterjee S, Bhattacharya M, Pal S, Lee SS, Chakraborty C. ChatGPT and large language models in orthopedics: from education and surgery to research. J Exp Orthop 2023; 10:128. [PMID: 38038796 PMCID: PMC10692045 DOI: 10.1186/s40634-023-00700-1] [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: 08/23/2023] [Accepted: 11/16/2023] [Indexed: 12/02/2023] Open
Abstract
ChatGPT has quickly popularized since its release in November 2022. Currently, large language models (LLMs) and ChatGPT have been applied in various domains of medical science, including in cardiology, nephrology, orthopedics, ophthalmology, gastroenterology, and radiology. Researchers are exploring the potential of LLMs and ChatGPT for clinicians and surgeons in every domain. This study discusses how ChatGPT can help orthopedic clinicians and surgeons perform various medical tasks. LLMs and ChatGPT can help the patient community by providing suggestions and diagnostic guidelines. In this study, the use of LLMs and ChatGPT to enhance and expand the field of orthopedics, including orthopedic education, surgery, and research, is explored. Present LLMs have several shortcomings, which are discussed herein. However, next-generation and future domain-specific LLMs are expected to be more potent and transform patients' quality of life.
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Affiliation(s)
- Srijan Chatterjee
- Institute for Skeletal Aging & Orthopaedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon-Si, 24252, Gangwon-Do, Republic of Korea
| | - Manojit Bhattacharya
- Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore, 756020, Odisha, India
| | - Soumen Pal
- School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Sang-Soo Lee
- Institute for Skeletal Aging & Orthopaedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon-Si, 24252, Gangwon-Do, Republic of Korea.
| | - Chiranjib Chakraborty
- Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal, 700126, India.
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15
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Keller G, Rachunek K, Springer F, Kraus M. Evaluation of a newly designed deep learning-based algorithm for automated assessment of scapholunate distance in wrist radiography as a surrogate parameter for scapholunate ligament rupture and the correlation with arthroscopy. LA RADIOLOGIA MEDICA 2023; 128:1535-1541. [PMID: 37726593 PMCID: PMC10700195 DOI: 10.1007/s11547-023-01720-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 09/04/2023] [Indexed: 09/21/2023]
Abstract
PURPOSE Not diagnosed or mistreated scapholunate ligament (SL) tears represent a frequent cause of degenerative wrist arthritis. A newly developed deep learning (DL)-based automated assessment of the SL distance on radiographs may support clinicians in initial image interpretation. MATERIALS AND METHODS A pre-trained DL algorithm was specifically fine-tuned on static and dynamic dorsopalmar wrist radiography (training data set n = 201) for the automated assessment of the SL distance. Afterwards the DL algorithm was evaluated (evaluation data set n = 364 patients with n = 1604 radiographs) and correlated with results of an experienced human reader and with arthroscopic findings. RESULTS The evaluation data set comprised arthroscopically diagnosed SL insufficiency according to Geissler's stages 0-4 (56.5%, 2.5%, 5.5%, 7.5%, 28.0%). Diagnostic accuracy of the DL algorithm on dorsopalmar radiography regarding SL integrity was close to that of the human reader (e.g. differentiation of Geissler's stages ≤ 2 versus > 2 with a sensitivity of 74% and a specificity of 78% compared to 77% and 80%) with a correlation coefficient of 0.81 (P < 0.01). CONCLUSION A DL algorithm like this might become a valuable tool supporting clinicians' initial decision making on radiography regarding SL integrity and consequential triage for further patient management.
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Affiliation(s)
- Gabriel Keller
- Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Eberhard Karls University Tübingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Germany.
- Department of Diagnostic Radiology, BG Trauma Center Tübingen, Eberhard Karls University Tübingen, Tübingen, Germany.
| | - Katarzyna Rachunek
- Department of Hand, Plastic, Reconstructive and Burn Surgery, BG Trauma Center Tübingen, Eberhard Karls University of Tübingen, 72076, Tübingen, Germany
| | - Fabian Springer
- Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Eberhard Karls University Tübingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Germany
- Department of Diagnostic Radiology, BG Trauma Center Tübingen, Eberhard Karls University Tübingen, Tübingen, Germany
| | - Mathias Kraus
- Institute of Information Systems, FAU Erlangen-Nuremberg, Nuremberg, Germany
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Riazi Esfahani P, Guirgus M, Maalouf M, Mazboudi P, Reddy AJ, Sarsour RO, Hassan SS. Development of a Machine Learning-Based Model for Accurate Detection and Classification of Cervical Spine Fractures Using CT Imaging. Cureus 2023; 15:e47328. [PMID: 38021776 PMCID: PMC10657145 DOI: 10.7759/cureus.47328] [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] [Accepted: 08/09/2023] [Indexed: 12/01/2023] Open
Abstract
Cervical spine fractures represent a significant healthcare challenge, necessitating accurate detection for appropriate management and improved patient outcomes. This study aims to develop a machine learning-based model utilizing a computed tomography (CT) image dataset to detect and classify cervical spine fractures. Leveraging a large dataset of 4,050 CT images obtained from the Radiological Society of North America (RSNA) Cervical Spine Fracture dataset, we evaluate the potential of machine learning and deep learning algorithms in achieving accurate and reliable cervical spine fracture detection. The model demonstrates outstanding performance, achieving an average precision of 1 and 100% precision, recall, sensitivity, specificity, and accuracy values. These exceptional results highlight the potential of machine learning algorithms to enhance clinical decision-making and facilitate prompt treatment initiation for cervical spine fractures. However, further research and validation efforts are warranted to assess the model's generalizability across diverse populations and real-world clinical settings, ultimately contributing to improved patient outcomes in cervical spine fracture cases.
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Affiliation(s)
| | - Monica Guirgus
- Medicine, California University of Science and Medicine, Colton, USA
| | - Maya Maalouf
- Medicine, California University of Science and Medicine, Colton, USA
| | - Pasha Mazboudi
- Medicine, California University of Science and Medicine, Colton, USA
| | - Akshay J Reddy
- Medicine, California University of Science and Medicine, Colton, USA
| | - Reem O Sarsour
- Medicine, California University of Science and Medicine, Colton, USA
| | - Sherif S Hassan
- Anatomy, Faculty of Medicine, Cairo University, Cairo, EGY
- Medical Education, Anatomy, & Neuroanatomy, California University of Science and Medicine, Colton, USA
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Achar S, Hwang D, Finkenstaedt T, Malis V, Bae WC. Deep-Learning-Aided Evaluation of Spondylolysis Imaged with Ultrashort Echo Time Magnetic Resonance Imaging. SENSORS (BASEL, SWITZERLAND) 2023; 23:8001. [PMID: 37766055 PMCID: PMC10538057 DOI: 10.3390/s23188001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 08/31/2023] [Accepted: 09/15/2023] [Indexed: 09/29/2023]
Abstract
Isthmic spondylolysis results in fracture of pars interarticularis of the lumbar spine, found in as many as half of adolescent athletes with persistent low back pain. While computed tomography (CT) is the gold standard for the diagnosis of spondylolysis, the use of ionizing radiation near reproductive organs in young subjects is undesirable. While magnetic resonance imaging (MRI) is preferable, it has lowered sensitivity for detecting the condition. Recently, it has been shown that ultrashort echo time (UTE) MRI can provide markedly improved bone contrast compared to conventional MRI. To take UTE MRI further, we developed supervised deep learning tools to generate (1) CT-like images and (2) saliency maps of fracture probability from UTE MRI, using ex vivo preparation of cadaveric spines. We further compared quantitative metrics of the contrast-to-noise ratio (CNR), mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) between UTE MRI (inverted to make the appearance similar to CT) and CT and between CT-like images and CT. Qualitative results demonstrated the feasibility of successfully generating CT-like images from UTE MRI to provide easier interpretability for bone fractures thanks to improved image contrast and CNR. Quantitatively, the mean CNR of bone against defect-filled tissue was 35, 97, and 146 for UTE MRI, CT-like, and CT images, respectively, being significantly higher for CT-like than UTE MRI images. For the image similarity metrics using the CT image as the reference, CT-like images provided a significantly lower mean MSE (0.038 vs. 0.0528), higher mean PSNR (28.6 vs. 16.5), and higher SSIM (0.73 vs. 0.68) compared to UTE MRI images. Additionally, the saliency maps enabled quick detection of the location with probable pars fracture by providing visual cues to the reader. This proof-of-concept study is limited to the data from ex vivo samples, and additional work in human subjects with spondylolysis would be necessary to refine the models for clinical use. Nonetheless, this study shows that the utilization of UTE MRI and deep learning tools could be highly useful for the evaluation of isthmic spondylolysis.
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Affiliation(s)
- Suraj Achar
- Department of Family Medicine, University of California-San Diego, La Jolla, CA 92093, USA
| | - Dosik Hwang
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
- Center for Healthcare Robotics, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul 03722, Republic of Korea
| | - Tim Finkenstaedt
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University Zurich, 8091 Zurich, Switzerland
| | - Vadim Malis
- Department of Radiology, University of California-San Diego, La Jolla, CA 92093, USA
| | - Won C. Bae
- Department of Radiology, University of California-San Diego, La Jolla, CA 92093, USA
- Department of Radiology, VA San Diego Healthcare System, San Diego, CA 92161, USA
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Shen L, Gao C, Hu S, Kang D, Zhang Z, Xia D, Xu Y, Xiang S, Zhu Q, Xu G, Tang F, Yue H, Yu W, Zhang Z. Using Artificial Intelligence to Diagnose Osteoporotic Vertebral Fractures on Plain Radiographs. J Bone Miner Res 2023; 38:1278-1287. [PMID: 37449775 DOI: 10.1002/jbmr.4879] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 06/18/2023] [Accepted: 07/06/2023] [Indexed: 07/18/2023]
Abstract
Osteoporotic vertebral fracture (OVF) is a risk factor for morbidity and mortality in elderly population, and accurate diagnosis is important for improving treatment outcomes. OVF diagnosis suffers from high misdiagnosis and underdiagnosis rates, as well as high workload. Deep learning methods applied to plain radiographs, a simple, fast, and inexpensive examination, might solve this problem. We developed and validated a deep-learning-based vertebral fracture diagnostic system using area loss ratio, which assisted a multitasking network to perform skeletal position detection and segmentation and identify and grade vertebral fractures. As the training set and internal validation set, we used 11,397 plain radiographs from six community centers in Shanghai. For the external validation set, 1276 participants were recruited from the outpatient clinic of the Shanghai Sixth People's Hospital (1276 plain radiographs). Radiologists performed all X-ray images and used the Genant semiquantitative tool for fracture diagnosis and grading as the ground truth data. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were used to evaluate diagnostic performance. The AI_OVF_SH system demonstrated high accuracy and computational speed in skeletal position detection and segmentation. In the internal validation set, the accuracy, sensitivity, and specificity with the AI_OVF_SH model were 97.41%, 84.08%, and 97.25%, respectively, for all fractures. The sensitivity and specificity for moderate fractures were 88.55% and 99.74%, respectively, and for severe fractures, they were 92.30% and 99.92%. In the external validation set, the accuracy, sensitivity, and specificity for all fractures were 96.85%, 83.35%, and 94.70%, respectively. For moderate fractures, the sensitivity and specificity were 85.61% and 99.85%, respectively, and 93.46% and 99.92% for severe fractures. Therefore, the AI_OVF_SH system is an efficient tool to assist radiologists and clinicians to improve the diagnosing of vertebral fractures. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).
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Affiliation(s)
- Li Shen
- Department of Osteoporosis and Bone Disease, Shanghai Clinical Research Center of Bone Disease, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Clinical Research Center, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chao Gao
- Department of Osteoporosis and Bone Disease, Shanghai Clinical Research Center of Bone Disease, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shundong Hu
- Department of Radiology, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Dan Kang
- Shanghai Jiyinghui Intelligent Technology Co, Shanghai, China
| | - Zhaogang Zhang
- Shanghai Jiyinghui Intelligent Technology Co, Shanghai, China
| | - Dongdong Xia
- Department of Orthopaedics, Ning Bo First Hospital, Zhejiang, China
| | - Yiren Xu
- Department of Radiology, Ning Bo First Hospital, Zhejiang, China
| | - Shoukui Xiang
- Department of Endocrinology and Metabolism, The First People's Hospital of Changzhou, Changzhou, China
| | - Qiong Zhu
- Kangjian Community Health Service Center, Shanghai, China
| | - GeWen Xu
- Kangjian Community Health Service Center, Shanghai, China
| | - Feng Tang
- Jinhui Community Health Service Center, Shanghai, China
| | - Hua Yue
- Department of Osteoporosis and Bone Disease, Shanghai Clinical Research Center of Bone Disease, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wei Yu
- Department of Radiology, Peking Union Medical College Hospital, Beijing, China
| | - Zhenlin Zhang
- Department of Osteoporosis and Bone Disease, Shanghai Clinical Research Center of Bone Disease, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Clinical Research Center, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Tang S, Jing C, Jiang Y, Yang K, Huang Z, Wu H, Cui C, Shi S, Ye X, Tian H, Song D, Xu J, Dong F. The effect of image resolution on convolutional neural networks in breast ultrasound. Heliyon 2023; 9:e19253. [PMID: 37664701 PMCID: PMC10469557 DOI: 10.1016/j.heliyon.2023.e19253] [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: 01/18/2023] [Revised: 04/01/2023] [Accepted: 08/16/2023] [Indexed: 09/05/2023] Open
Abstract
Purpose The objective of this research was to investigate the efficacy of various parameter combinations of Convolutional Neural Networks (CNNs) models, namely MobileNet and DenseNet121, and different input image resolutions (REZs) ranging from 64×64 to 512×512 pixels, for diagnosing breast cancer. Materials and methods During the period of June 2015 to November 2020, two hospitals were involved in the collection of two-dimensional ultrasound breast images for this retrospective multicenter study. The diagnostic performance of the computer models MobileNet and DenseNet 121 was compared at different resolutions. Results The results showed that MobileNet had the best breast cancer diagnosis performance at 320×320pixel REZ and DenseNet121 had the best breast cancer diagnosis performance at 448×448pixel REZ. Conclusion Our study reveals a significant correlation between image resolution and breast cancer diagnosis accuracy. Through the comparison of MobileNet and DenseNet121, it is highlighted that lightweight neural networks (LW-CNNs) can achieve model performance similar to or even slightly better than large neural networks models (HW-CNNs) in ultrasound images, and LW-CNNs' prediction time per image is lower.
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Affiliation(s)
- Shuzhen Tang
- Second Clinical College of Jinan University, Shenzhen 518020, Guangdong, China
| | - Chen Jing
- Second Clinical College of Jinan University, Shenzhen 518020, Guangdong, China
- Shenzhen People's Hospital, Shenzhen 518020, Guangdong, China
| | - Yitao Jiang
- Research and Development Department, Illuminate, LLC, Shenzhen, Guangdong 518000, China
| | - Keen Yang
- Second Clinical College of Jinan University, Shenzhen 518020, Guangdong, China
- Shenzhen People's Hospital, Shenzhen 518020, Guangdong, China
| | - Zhibin Huang
- Second Clinical College of Jinan University, Shenzhen 518020, Guangdong, China
- Shenzhen People's Hospital, Shenzhen 518020, Guangdong, China
| | - Huaiyu Wu
- Second Clinical College of Jinan University, Shenzhen 518020, Guangdong, China
- Shenzhen People's Hospital, Shenzhen 518020, Guangdong, China
| | - Chen Cui
- Research and Development Department, Illuminate, LLC, Shenzhen, Guangdong 518000, China
| | - Siyuan Shi
- Research and Development Department, Illuminate, LLC, Shenzhen, Guangdong 518000, China
| | - Xiuqin Ye
- Second Clinical College of Jinan University, Shenzhen 518020, Guangdong, China
- Shenzhen People's Hospital, Shenzhen 518020, Guangdong, China
| | - Hongtian Tian
- Second Clinical College of Jinan University, Shenzhen 518020, Guangdong, China
- Shenzhen People's Hospital, Shenzhen 518020, Guangdong, China
| | - Di Song
- Second Clinical College of Jinan University, Shenzhen 518020, Guangdong, China
- Shenzhen People's Hospital, Shenzhen 518020, Guangdong, China
| | - Jinfeng Xu
- Second Clinical College of Jinan University, Shenzhen 518020, Guangdong, China
| | - Fajin Dong
- Second Clinical College of Jinan University, Shenzhen 518020, Guangdong, China
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Gasmi I, Calinghen A, Parienti JJ, Belloy F, Fohlen A, Pelage JP. Comparison of diagnostic performance of a deep learning algorithm, emergency physicians, junior radiologists and senior radiologists in the detection of appendicular fractures in children. Pediatr Radiol 2023; 53:1675-1684. [PMID: 36877239 DOI: 10.1007/s00247-023-05621-w] [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] [Received: 11/30/2021] [Revised: 11/21/2022] [Accepted: 01/30/2023] [Indexed: 03/07/2023]
Abstract
BACKGROUND Advances have been made in the use of artificial intelligence (AI) in the field of diagnostic imaging, particularly in the detection of fractures on conventional radiographs. Studies looking at the detection of fractures in the pediatric population are few. The anatomical variations and evolution according to the child's age require specific studies of this population. Failure to diagnose fractures early in children may lead to serious consequences for growth. OBJECTIVE To evaluate the performance of an AI algorithm based on deep neural networks toward detecting traumatic appendicular fractures in a pediatric population. To compare sensitivity, specificity, positive predictive value and negative predictive value of different readers and the AI algorithm. MATERIALS AND METHODS This retrospective study conducted on 878 patients younger than 18 years of age evaluated conventional radiographs obtained after recent non-life-threatening trauma. All radiographs of the shoulder, arm, elbow, forearm, wrist, hand, leg, knee, ankle and foot were evaluated. The diagnostic performance of a consensus of radiology experts in pediatric imaging (reference standard) was compared with those of pediatric radiologists, emergency physicians, senior residents and junior residents. The predictions made by the AI algorithm and the annotations made by the different physicians were compared. RESULTS The algorithm predicted 174 fractures out of 182, corresponding to a sensitivity of 95.6%, a specificity of 91.64% and a negative predictive value of 98.76%. The AI predictions were close to that of pediatric radiologists (sensitivity 98.35%) and that of senior residents (95.05%) and were above those of emergency physicians (81.87%) and junior residents (90.1%). The algorithm identified 3 (1.6%) fractures not initially seen by pediatric radiologists. CONCLUSION This study suggests that deep learning algorithms can be useful in improving the detection of fractures in children.
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Affiliation(s)
- Idriss Gasmi
- Department of Radiology, Caen University Medical Center, 14033 Cedex 9, Caen, France
| | - Arvin Calinghen
- Department of Radiology, Caen University Medical Center, 14033 Cedex 9, Caen, France
| | - Jean-Jacques Parienti
- GRAM 2.0 EA2656 UNICAEN Normandie, University Hospital, Caen, France
- Department of Clinical Research, Caen University Hospital, Caen, France
| | - Frederique Belloy
- Department of Radiology, Caen University Medical Center, 14033 Cedex 9, Caen, France
| | - Audrey Fohlen
- Department of Radiology, Caen University Medical Center, 14033 Cedex 9, Caen, France
- UNICAEN CEA CNRS ISTCT- CERVOxy, Normandie University, 14000, Caen, France
| | - Jean-Pierre Pelage
- Department of Radiology, Caen University Medical Center, 14033 Cedex 9, Caen, France.
- UNICAEN CEA CNRS ISTCT- CERVOxy, Normandie University, 14000, Caen, France.
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Kang W, Lin L, Sun S, Wu S. Three-round learning strategy based on 3D deep convolutional GANs for Alzheimer's disease staging. Sci Rep 2023; 13:5750. [PMID: 37029214 PMCID: PMC10081988 DOI: 10.1038/s41598-023-33055-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 04/06/2023] [Indexed: 04/09/2023] Open
Abstract
Accurately diagnosing of Alzheimer's disease (AD) and its early stages is critical for prompt treatment or potential intervention to delay the the disease's progression. Convolutional neural networks (CNNs) models have shown promising results in structural MRI (sMRI)-based diagnosis, but their performance, particularly for 3D models, is constrained by the lack of labeled training samples. To address the overfitting problem brought on by the insufficient training sample size, we propose a three-round learning strategy that combines transfer learning with generative adversarial learning. In the first round, a 3D Deep Convolutional Generative Adversarial Networks (DCGAN) model was trained with all available sMRI data to learn the common feature of sMRI through unsupervised generative adversarial learning. The second round involved transferring and fine-tuning, and the pre-trained discriminator (D) of the DCGAN learned more specific features for the classification task between AD and cognitively normal (CN). In the final round, the weights learned in the AD versus CN classification task were transferred to the MCI diagnosis. By highlighting brain regions with high prediction weights using 3D Grad-CAM, we further enhanced the model's interpretability. The proposed model achieved accuracies of 92.8%, 78.1%, and 76.4% in the classifications of AD versus CN, AD versus MCI, and MCI versus CN, respectively. The experimental results show that our proposed model avoids overfitting brought on by a paucity of sMRI data and enables the early detection of AD.
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Affiliation(s)
- Wenjie Kang
- Beijing International Platform for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing, 100124, China
| | - Lan Lin
- Beijing International Platform for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing, 100124, China.
| | - Shen Sun
- Beijing International Platform for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing, 100124, China
| | - Shuicai Wu
- Beijing International Platform for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing, 100124, China
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Manuel Román-Belmonte J, De la Corte-Rodríguez H, Adriana Rodríguez-Damiani B, Carlos Rodríguez-Merchán E. Artificial Intelligence in Musculoskeletal Conditions. ARTIF INTELL 2023. [DOI: 10.5772/intechopen.110696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
Abstract
Artificial intelligence (AI) refers to computer capabilities that resemble human intelligence. AI implies the ability to learn and perform tasks that have not been specifically programmed. Moreover, it is an iterative process involving the ability of computerized systems to capture information, transform it into knowledge, and process it to produce adaptive changes in the environment. A large labeled database is needed to train the AI system and generate a robust algorithm. Otherwise, the algorithm cannot be applied in a generalized way. AI can facilitate the interpretation and acquisition of radiological images. In addition, it can facilitate the detection of trauma injuries and assist in orthopedic and rehabilitative processes. The applications of AI in musculoskeletal conditions are promising and are likely to have a significant impact on the future management of these patients.
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23
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Zhang S, Zhao Z, Qiu L, Liang D, Wang K, Xu J, Zhao J, Sun J. Automatic vertebral fracture and three-column injury diagnosis with fracture visualization by a multi-scale attention-guided network. Med Biol Eng Comput 2023:10.1007/s11517-023-02805-2. [PMID: 36848011 DOI: 10.1007/s11517-023-02805-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 02/08/2023] [Indexed: 03/01/2023]
Abstract
Deep learning methods have the potential to improve the efficiency of diagnosis for vertebral fractures with computed tomography (CT) images. Most existing intelligent vertebral fracture diagnosis methods only provide dichotomized results at a patient level. However, a fine-grained and more nuanced outcome is clinically needed. This study proposed a novel network, a multi-scale attention-guided network (MAGNet), to diagnose vertebral fractures and three-column injuries with fracture visualization at a vertebra level. By imposing attention constraints through a disease attention map (DAM), a fusion of multi-scale spatial attention maps, the MAGNet can get task highly relevant features and localize fractures. A total of 989 vertebrae were studied here. After four-fold cross-validation, the area under the ROC curve (AUC) of our model for vertebral fracture dichotomized diagnosis and three-column injury diagnosis was 0.884 ± 0.015 and 0.920 ± 0.104, respectively. The overall performance of our model outperformed classical classification models, attention models, visual explanation methods, and attention-guided methods based on class activation mapping. Our work can promote the clinical application of deep learning to diagnose vertebral fractures and provide a way to visualize and improve the diagnosis results with attention constraints.
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Affiliation(s)
- Shunan Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Ziqi Zhao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Lu Qiu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Duan Liang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Kun Wang
- Renji Hospital, Shanghai, 200127, China
| | - Jun Xu
- Shanghai Sixth People's Hospital, Shanghai, 200233, China.
| | - Jun Zhao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Jianqi Sun
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
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Erne F, Dehncke D, Herath SC, Springer F, Pfeifer N, Eggeling R, Küper MA. Deep Learning in the Detection of Rare Fractures - Development of a "Deep Learning Convolutional Network" Model for Detecting Acetabular Fractures. ZEITSCHRIFT FUR ORTHOPADIE UND UNFALLCHIRURGIE 2023; 161:42-50. [PMID: 34311473 DOI: 10.1055/a-1511-8595] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
BACKGROUND Fracture detection by artificial intelligence and especially Deep Convolutional Neural Networks (DCNN) is a topic of growing interest in current orthopaedic and radiological research. As learning a DCNN usually needs a large amount of training data, mostly frequent fractures as well as conventional X-ray are used. Therefore, less common fractures like acetabular fractures (AF) are underrepresented in the literature. The aim of this pilot study was to establish a DCNN for detection of AF using computer tomography (CT) scans. METHODS Patients with an acetabular fracture were identified from the monocentric consecutive pelvic injury registry at the BG Trauma Center XXX from 01/2003 - 12/2019. All patients with unilateral AF and CT scans available in DICOM-format were included for further processing. All datasets were automatically anonymised and digitally post-processed. Extraction of the relevant region of interests was performed and the technique of data augmentation (DA) was implemented to artificially increase the number of training samples. A DCNN based on Med3D was used for autonomous fracture detection, using global average pooling (GAP) to reduce overfitting. RESULTS From a total of 2,340 patients with a pelvic fracture, 654 patients suffered from an AF. After screening and post-processing of the datasets, a total of 159 datasets were enrolled for training of the algorithm. A random assignment into training datasets (80%) and test datasets (20%) was performed. The technique of bone area extraction, DA and GAP increased the accuracy of fracture detection from 58.8% (native DCNN) up to an accuracy of 82.8% despite the low number of datasets. CONCLUSION The accuracy of fracture detection of our trained DCNN is comparable to published values despite the low number of training datasets. The techniques of bone extraction, DA and GAP are useful for increasing the detection rates of rare fractures by a DCNN. Based on the used DCNN in combination with the described techniques from this pilot study, the possibility of an automatic fracture classification of AF is under investigation in a multicentre study.
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Affiliation(s)
- Felix Erne
- Department of Trauma and Reconstructive Surgery, Occupational Accident Clinic Tübingen, Tübingen, Germany
| | - Daniel Dehncke
- Department of Informatics, Methods in Medical Informatics, Eberhard Karls University of Tübingen Faculty of Mathematics and Natural Sciences, Tubingen, Germany
| | - Steven C Herath
- Department of Trauma and Reconstructive Surgery, Occupational Accident Clinic Tübingen, Tübingen, Germany
| | - Fabian Springer
- Department of Diagnostic & Interventional Radiology, University Hospital Tübingen, Tübingen, Germany.,Department of Radiology, Occupational Accident Clinic Tübingen, Tübingen, Germany
| | - Nico Pfeifer
- Department of Informatics, Methods in Medical Informatics, Eberhard Karls University of Tübingen Faculty of Mathematics and Natural Sciences, Tubingen, Germany
| | - Ralf Eggeling
- Department of Informatics, Methods in Medical Informatics, Eberhard Karls University of Tübingen Faculty of Mathematics and Natural Sciences, Tubingen, Germany
| | - Markus Alexander Küper
- Department of Trauma and Reconstructive Surgery, Occupational Accident Clinic Tübingen, Tübingen, Germany
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25
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Detecting pediatric wrist fractures using deep-learning-based object detection. Pediatr Radiol 2023; 53:1125-1134. [PMID: 36650360 DOI: 10.1007/s00247-023-05588-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 12/09/2022] [Accepted: 12/30/2022] [Indexed: 01/19/2023]
Abstract
BACKGROUND Missed fractures are the leading cause of diagnostic error in the emergency department, and fractures of pediatric bones, particularly subtle wrist fractures, can be misidentified because of their varying characteristics and responses to injury. OBJECTIVE This study evaluated the utility of an object detection deep learning framework for classifying pediatric wrist fractures as positive or negative for fracture, including subtle buckle fractures of the distal radius, and evaluated the performance of this algorithm as augmentation to trainee radiograph interpretation. MATERIALS AND METHODS We obtained 395 posteroanterior wrist radiographs from unique pediatric patients (65% positive for fracture, 30% positive for distal radial buckle fracture) and divided them into train (n = 229), tune (n = 41) and test (n = 125) sets. We trained a Faster R-CNN (region-based convolutional neural network) deep learning object-detection model. Two pediatric and two radiology residents evaluated radiographs initially without the artificial intelligence (AI) assistance, and then subsequently with access to the bounding box generated by the Faster R-CNN model. RESULTS The Faster R-CNN model demonstrated an area under the curve (AUC) of 0.92 (95% confidence interval [CI] 0.87-0.97), accuracy of 88% (n = 110/125; 95% CI 81-93%), sensitivity of 88% (n = 70/80; 95% CI 78-94%) and specificity of 89% (n = 40/45, 95% CI 76-96%) in identifying any fracture and identified 90% of buckle fractures (n = 35/39, 95% CI 76-97%). Access to Faster R-CNN model predictions significantly improved average resident accuracy from 80 to 93% in detecting any fracture (P < 0.001) and from 69 to 92% in detecting buckle fracture (P < 0.001). After accessing AI predictions, residents significantly outperformed AI in cases of disagreement (73% resident correct vs. 27% AI, P = 0.002). CONCLUSION An object-detection-based deep learning approach trained with only a few hundred examples identified radiographs containing pediatric wrist fractures with high accuracy. Access to model predictions significantly improved resident accuracy in diagnosing these fractures.
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Morris MX, Rajesh A, Asaad M, Hassan A, Saadoun R, Butler CE. Deep Learning Applications in Surgery: Current Uses and Future Directions. Am Surg 2023; 89:36-42. [PMID: 35567312 DOI: 10.1177/00031348221101490] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Deep learning (DL) is a subset of machine learning that is rapidly gaining traction in surgical fields. Its tremendous capacity for powerful data-driven problem-solving has generated computational breakthroughs in many realms, with the fields of medicine and surgery becoming increasingly prominent avenues. Through its multi-layer architecture of interconnected neural networks, DL enables feature extraction and pattern recognition of highly complex and large-volume data. Across various surgical specialties, DL is being applied to optimize both preoperative planning and intraoperative performance in new and innovative ways. Surgeons are now able to integrate deep learning tools into their practice to improve patient safety and outcomes. Through this review, we explore the applications of deep learning in surgery and related subspecialties with an aim to shed light on the practical utilization of this technology in the present and near future.
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Affiliation(s)
- Miranda X Morris
- 12277Duke University School of Medicine, Durham, NC, USA.,101571Duke Pratt School of Engineering, Durham, NC, USA
| | - Aashish Rajesh
- Department of Surgery, 14742University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Malke Asaad
- Department of Plastic Surgery, 6595University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Abbas Hassan
- Department of Plastic Surgery, 571198The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Rakan Saadoun
- Department of Plastic Surgery, 6595University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Charles E Butler
- Department of Plastic Surgery, 571198The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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27
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Scala A, Borrelli A, Improta G. Predictive analysis of lower limb fractures in the orthopedic complex operative unit using artificial intelligence: the case study of AOU Ruggi. Sci Rep 2022; 12:22153. [PMID: 36550192 PMCID: PMC9780352 DOI: 10.1038/s41598-022-26667-0] [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: 05/11/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022] Open
Abstract
The length of stay (LOS) in hospital is one of the main parameters for evaluating the management of a health facility, of its departments in relation to the different specializations. Healthcare costs are in fact closely linked to this parameter as well as the profit margin. In the orthopedic field, the provision of this parameter is increasingly complex and of fundamental importance in order to be able to evaluate the planning of resources, the waiting times for any scheduled interventions and the management of the department and related surgical interventions. The purpose of this work is to predict and evaluate the LOS value using machine learning methods and applying multiple linear regression, starting from clinical data of patients hospitalized with lower limb fractures. The data were collected at the "San Giovanni di Dio e Ruggi d'Aragona" hospital in Salerno (Italy).
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Affiliation(s)
- Arianna Scala
- grid.4691.a0000 0001 0790 385XDepartment of Public Health, University of Naples “Federico II”, Naples, Italy
| | - Anna Borrelli
- San Giovanni di Dio e Ruggi d’Aragona” University Hospital, Salerno, Italy
| | - Giovanni Improta
- grid.4691.a0000 0001 0790 385XDepartment of Public Health, University of Naples “Federico II”, Naples, Italy ,Interdepartmental Center for Research in Healthcare Management and Innovation in Healthcare (CIRMIS), Naples, Italy
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28
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Cohen M, Puntonet J, Sanchez J, Kierszbaum E, Crema M, Soyer P, Dion E. Artificial intelligence vs. radiologist: accuracy of wrist fracture detection on radiographs. Eur Radiol 2022; 33:3974-3983. [PMID: 36515712 DOI: 10.1007/s00330-022-09349-3] [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: 05/10/2022] [Revised: 09/05/2022] [Accepted: 11/29/2022] [Indexed: 12/15/2022]
Abstract
OBJECTIVE To compare the performances of artificial intelligence (AI) to those of radiologists in wrist fracture detection on radiographs. METHODS This retrospective study included 637 patients (1917 radiographs) with wrist trauma between January 2017 and December 2019. The AI software used was a deep neuronal network algorithm. Ground truth was established by three senior musculoskeletal radiologists who compared the initial radiology reports (IRR) made by non-specialized radiologists, the results of AI, and the combination of AI and IRR (IR+AI) RESULTS: A total of 318 fractures were reported by the senior radiologists in 247 patients. Sensitivity of AI (83%; 95% CI: 78-87%) was significantly greater than that of IRR (76%; 95% CI: 70-81%) (p < 0.001). Specificities were similar for AI (96%; 95% CI: 93-97%) and for IRR (96%; 95% CI: 94-98%) (p = 0.80). The combination of AI+IRR had a significantly greater sensitivity (88%; 95% CI: 84-92%) compared to AI and IRR (p < 0.001) and a lower specificity (92%; 95% CI: 89-95%) (p < 0.001). The sensitivity for scaphoid fracture detection was acceptable for AI (84%) and IRR (80%) but poor for the detection of other carpal bones fracture (41% for AI and 26% for IRR). CONCLUSIONS Performance of AI in wrist fracture detection on radiographs is better than that of non-specialized radiologists. The combination of AI and radiologist's analysis yields best performances. KEY POINTS • Artificial intelligence has better performances for wrist fracture detection compared to non-expert radiologists in daily practice. • Performance of artificial intelligence greatly differs depending on the anatomical area. • Sensitivity of artificial intelligence for the detection of carpal bones fractures is 56%.
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Affiliation(s)
- Mathieu Cohen
- Department of Radiology - Hotel Dieu Hospital, Assistance Publique-Hopitaux de Paris, Paris, France
- Université Paris Cité, F-75006, Paris, France
| | - Julien Puntonet
- Department of Radiology - Hotel Dieu Hospital, Assistance Publique-Hopitaux de Paris, Paris, France.
- Université Paris Cité, F-75006, Paris, France.
| | - Julien Sanchez
- Université Paris Cité, F-75006, Paris, France
- Institute of Sports Imaging, French National Institute of Sports (INSEP), Paris, France
| | | | - Michel Crema
- Department of Radiology - Hotel Dieu Hospital, Assistance Publique-Hopitaux de Paris, Paris, France
- Institute of Sports Imaging, French National Institute of Sports (INSEP), Paris, France
| | - Philippe Soyer
- Université Paris Cité, F-75006, Paris, France
- Department of Radiology- Cochin Hospital, Assistance Publique-Hopitaux de Paris, 75014, Paris, France
| | - Elisabeth Dion
- Department of Radiology - Hotel Dieu Hospital, Assistance Publique-Hopitaux de Paris, Paris, France
- Université Paris Cité, F-75006, Paris, France
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Yang L, Gao S, Li P, Shi J, Zhou F. Recognition and Segmentation of Individual Bone Fragments with a Deep Learning Approach in CT Scans of Complex Intertrochanteric Fractures: A Retrospective Study. J Digit Imaging 2022; 35:1681-1689. [PMID: 35711073 PMCID: PMC9712885 DOI: 10.1007/s10278-022-00669-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 05/04/2022] [Accepted: 06/07/2022] [Indexed: 10/18/2022] Open
Abstract
The characteristics of bone fragments are the main influencing factors for the choice of treatment in intertrochanteric fractures. This study aimed to develop a deep learning algorithm for recognizing and segmenting individual fragments in CT images of complex intertrochanteric fractures for orthopedic surgeons. This study was based on 160 hip CT scans (43,510 images) of complex fractures of three types based on the Evans-Jensen classification (40 cases of type 3 (IIA) fractures, 80 cases of type 4 (IIB)fractures, and 40 cases of type 5 (III)fractures) retrospectively. The images were randomly split into two groups to construct a training set of 120 CT scans (32,045 images) and a testing set of 40 CT scans (11,465 images). A deep learning model was built into a cascaded architecture composed by a convolutional neural network (CNN) for location of the fracture ROI and another CNN for recognition and segmentation of individual fragments within the ROI. The accuracy of object detection and dice coefficient of segmentation of individual fragments were used to evaluate model performance. The model yielded an average accuracy of 89.4% for individual fragment recognition and an average dice coefficient of 90.5% for segmentation in CT images. The results demonstrated the feasibility of recognition and segmentation of individual fragments in complex intertrochanteric fractures with a deep learning approach. Altogether, these promising results suggest the potential of our model to be applied to many clinical scenarios.
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Affiliation(s)
- Lv Yang
- Department of Orthopedics, Peking University Third Hospital, Beijing, China
| | - Shan Gao
- Department of Orthopedics, Peking University Third Hospital, Beijing, China
| | - Pengfei Li
- Department of Orthopedics, Peking University Third Hospital, Beijing, China
| | - Jiancheng Shi
- Department of Radiology, Peking University Third Hospital, Yanqing Hospital, Beijing, China
| | - Fang Zhou
- Department of Orthopedics, Peking University Third Hospital, Beijing, China.
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30
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Kumar V, Patel S, Baburaj V, Vardhan A, Singh PK, Vaishya R. Current understanding on artificial intelligence and machine learning in orthopaedics - A scoping review. J Orthop 2022; 34:201-206. [PMID: 36104993 PMCID: PMC9465367 DOI: 10.1016/j.jor.2022.08.020] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 08/16/2022] [Accepted: 08/17/2022] [Indexed: 11/25/2022] Open
Abstract
Background Artificial Intelligence (AI) has improved the way of looking at technological challenges. Today, we can afford to see many of the problems as just an input-output system rather than solving from the first principles. The field of Orthopaedics is not spared from this rapidly expanding technology. The recent surge in the use of AI can be attributed mainly to advancements in deep learning methodologies and computing resources. This review was conducted to draw an outline on the role of AI in orthopaedics. Methods We developed a search strategy and looked for articles on PubMed, Scopus, and EMBASE. A total of 40 articles were selected for this study, from tools for medical aid like imaging solutions, implant management, and robotic surgery to understanding scientific questions. Results A total of 40 studies have been included in this review. The role of AI in the various subspecialties such as arthroplasty, trauma, orthopaedic oncology, foot and ankle etc. have been discussed in detail. Conclusion AI has touched most of the aspects of Orthopaedics. The increase in technological literacy, data management plans, and hardware systems, amalgamated with the access to hand-held devices like mobiles, and electronic pads, augur well for the exciting times ahead in this field. We have discussed various technological breakthroughs in AI that have been able to perform in Orthopaedics, and also the limitations and the problem with the black-box approach of modern AI algorithms. We advocate for better interpretable algorithms which can help both the patients and surgeons alike.
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Affiliation(s)
- Vishal Kumar
- Department of Orthopaedics, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Sandeep Patel
- Department of Orthopaedics, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Vishnu Baburaj
- Department of Orthopaedics, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Aditya Vardhan
- Department of Orthopaedics, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Prasoon Kumar Singh
- Department of Orthopaedics, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
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31
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Dankelman LHM, Schilstra S, IJpma FFA, Doornberg JN, Colaris JW, Verhofstad MHJ, Wijffels MME, Prijs J. Artificial intelligence fracture recognition on computed tomography: review of literature and recommendations. Eur J Trauma Emerg Surg 2022; 49:681-691. [PMID: 36284017 PMCID: PMC10175338 DOI: 10.1007/s00068-022-02128-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 10/02/2022] [Indexed: 11/26/2022]
Abstract
Abstract
Purpose
The use of computed tomography (CT) in fractures is time consuming, challenging and suffers from poor inter-surgeon reliability. Convolutional neural networks (CNNs), a subset of artificial intelligence (AI), may overcome shortcomings and reduce clinical burdens to detect and classify fractures. The aim of this review was to summarize literature on CNNs for the detection and classification of fractures on CT scans, focusing on its accuracy and to evaluate the beneficial role in daily practice.
Methods
Literature search was performed according to the PRISMA statement, and Embase, Medline ALL, Web of Science Core Collection, Cochrane Central Register of Controlled Trials and Google Scholar databases were searched. Studies were eligible when the use of AI for the detection of fractures on CT scans was described. Quality assessment was done with a modified version of the methodologic index for nonrandomized studies (MINORS), with a seven-item checklist. Performance of AI was defined as accuracy, F1-score and area under the curve (AUC).
Results
Of the 1140 identified studies, 17 were included. Accuracy ranged from 69 to 99%, the F1-score ranged from 0.35 to 0.94 and the AUC, ranging from 0.77 to 0.95. Based on ten studies, CNN showed a similar or improved diagnostic accuracy in addition to clinical evaluation only.
Conclusions
CNNs are applicable for the detection and classification fractures on CT scans. This can improve automated and clinician-aided diagnostics. Further research should focus on the additional value of CNN used for CT scans in daily clinics.
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Affiliation(s)
- Lente H. M. Dankelman
- Trauma Research Unit, Department of Surgery, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands
| | - Sanne Schilstra
- Department of Orthopedic Surgery, Groningen University Medical Centre, Groningen, The Netherlands
- Department of Surgery, Groningen University Medical Centre, Groningen, The Netherlands
| | - Frank F. A. IJpma
- Department of Surgery, Groningen University Medical Centre, Groningen, The Netherlands
| | - Job N. Doornberg
- Department of Orthopedic Surgery, Groningen University Medical Centre, Groningen, The Netherlands
- Department of Surgery, Groningen University Medical Centre, Groningen, The Netherlands
- Department of Orthopedic & Trauma Surgery, Flinders Medical Centre, Flinders University, Adelaide, Australia
| | - Joost W. Colaris
- Department of Orthopedics, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Michael H. J. Verhofstad
- Trauma Research Unit, Department of Surgery, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands
| | - Mathieu M. E. Wijffels
- Trauma Research Unit, Department of Surgery, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands
| | - Jasper Prijs
- Department of Orthopedic Surgery, Groningen University Medical Centre, Groningen, The Netherlands
- Department of Surgery, Groningen University Medical Centre, Groningen, The Netherlands
- Department of Orthopedic & Trauma Surgery, Flinders Medical Centre, Flinders University, Adelaide, Australia
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Li Y, Yao Q, Yu H, Xie X, Shi Z, Li S, Qiu H, Li C, Qin J. Automated segmentation of vertebral cortex with 3D U-Net-based deep convolutional neural network. Front Bioeng Biotechnol 2022; 10:996723. [PMCID: PMC9626964 DOI: 10.3389/fbioe.2022.996723] [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: 07/18/2022] [Accepted: 09/02/2022] [Indexed: 11/13/2022] Open
Abstract
Objectives: We developed a 3D U-Net-based deep convolutional neural network for the automatic segmentation of the vertebral cortex. The purpose of this study was to evaluate the accuracy of the 3D U-Net deep learning model. Methods: In this study, a fully automated vertebral cortical segmentation method with 3D U-Net was developed, and ten-fold cross-validation was employed. Through data augmentation, we obtained 1,672 3D images of chest CT scans. Segmentation was performed using a conventional image processing method and manually corrected by a senior radiologist to create the gold standard. To compare the segmentation performance, 3D U-Net, Res U-Net, Ki U-Net, and Seg Net were used to segment the vertebral cortex in CT images. The segmentation performance of 3D U-Net and the other three deep learning algorithms was evaluated using DSC, mIoU, MPA, and FPS. Results: The DSC, mIoU, and MPA of 3D U-Net are better than the other three strategies, reaching 0.71 ± 0.03, 0.74 ± 0.08, and 0.83 ± 0.02, respectively, indicating promising automated segmentation results. The FPS is slightly lower than that of Seg Net (23.09 ± 1.26 vs. 30.42 ± 3.57). Conclusion: Cortical bone can be effectively segmented based on 3D U-net.
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Affiliation(s)
- Yang Li
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, China
| | - Qianqian Yao
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, China
| | - Haitao Yu
- Mechanical and Electrical Engineering College, Hainan University, Haikou, China
| | - Xiaofeng Xie
- Mechanical and Electrical Engineering College, Hainan University, Haikou, China
| | - Zeren Shi
- Hangzhou Shimai Intelligent Technology Co., Ltd., Hangzhou, China
| | - Shanshan Li
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, China
| | - Hui Qiu
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, China
| | - Changqin Li
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, China
| | - Jian Qin
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, China,*Correspondence: Jian Qin,
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Artificial Intelligence in Orthopedic Radiography Analysis: A Narrative Review. Diagnostics (Basel) 2022; 12:diagnostics12092235. [PMID: 36140636 PMCID: PMC9498096 DOI: 10.3390/diagnostics12092235] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/12/2022] [Accepted: 09/13/2022] [Indexed: 11/17/2022] Open
Abstract
Artificial intelligence (AI) in medicine is a rapidly growing field. In orthopedics, the clinical implementations of AI have not yet reached their full potential. Deep learning algorithms have shown promising results in computed radiographs for fracture detection, classification of OA, bone age, as well as automated measurements of the lower extremities. Studies investigating the performance of AI compared to trained human readers often show equal or better results, although human validation is indispensable at the current standards. The objective of this narrative review is to give an overview of AI in medicine and summarize the current applications of AI in orthopedic radiography imaging. Due to the different AI software and study design, it is difficult to find a clear structure in this field. To produce more homogeneous studies, open-source access to AI software codes and a consensus on study design should be aimed for.
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Aparisi Gómez MP, Isaac A, Dalili D, Fotiadou A, Kariki EP, Kirschke JS, Krestan CR, Messina C, Oei EHG, Phan CM, Prakash M, Sabir N, Tagliafico A, Aparisi F, Baum T, Link TM, Guglielmi G, Bazzocchi A. Imaging of Metabolic Bone Diseases: The Spine View, Part II. Semin Musculoskelet Radiol 2022; 26:491-500. [PMID: 36103890 DOI: 10.1055/s-0042-1754341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Abstract
Metabolic bone diseases comprise a wide spectrum. Osteoporosis, the most frequent, characteristically involves the spine, with a high impact on health care systems and on the morbidity of patients due to the occurrence of vertebral fractures (VFs).Part II of this review completes an overview of state-of-the-art techniques on the imaging of metabolic bone diseases of the spine, focusing on specific populations and future perspectives. We address the relevance of diagnosis and current status on VF assessment and quantification. We also analyze the diagnostic techniques in the pediatric population and then review the assessment of body composition around the spine and its potential application. We conclude with a discussion of the future of osteoporosis screening, through opportunistic diagnosis and the application of artificial intelligence.
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Affiliation(s)
- Maria Pilar Aparisi Gómez
- Department of Radiology, Auckland City Hospital, Auckland, New Zealand.,Department of Radiology, IMSKE, Valencia, Spain
| | - Amanda Isaac
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Danoob Dalili
- Academic Surgical Unit, South West London Elective Orthopaedic Centre (SWLEOC), Epsom, London, United Kingdom.,Department of Diagnostic and Interventional Radiology, Epsom and St. Helier University Hospitals NHS Trust, London, United Kingdom
| | - Anastasia Fotiadou
- Consultant Radiologist, Royal National Orthopaedic Hospital, Stanmore, United Kingdom
| | - Eleni P Kariki
- Manchester University NHS Foundation Trust, Manchester, United Kingdom.,Division of Informatics, Imaging & Data Sciences, School of Health Sciences, The University of Manchester, Manchester, United Kingdom
| | - Jan S Kirschke
- Interventional und Diagnostic Neuroradiology, School of Medicine, Technical University Munich, Munich, Germany
| | | | | | - Edwin H G Oei
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Catherine M Phan
- Service de Radiologie Ostéo-Articulaire, APHP, Nord-Université de Paris, Hôpital Lariboisière, Paris, France
| | - Mahesh Prakash
- Department of Radiodiagnosis & Imaging, PGIMER, Chandigarh, India
| | - Nuran Sabir
- Department of Radiology, Pamukkale University School of Medicine, Denizli, Turkey
| | - Alberto Tagliafico
- DISSAL, University of Genova, Genova, Italy.,Ospedale Policlinico San Martino, Genova, Italy
| | - Francisco Aparisi
- Department of Radiology, Hospital Vithas Nueve de Octubre, Valencia, Spain
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Thomas M Link
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, California
| | | | - Alberto Bazzocchi
- Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
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35
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Kong SH, Lee JW, Bae BU, Sung JK, Jung KH, Kim JH, Shin CS. Development of a Spine X-Ray-Based Fracture Prediction Model Using a Deep Learning Algorithm. Endocrinol Metab (Seoul) 2022; 37:674-683. [PMID: 35927066 PMCID: PMC9449110 DOI: 10.3803/enm.2022.1461] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 06/20/2022] [Indexed: 11/20/2022] Open
Abstract
BACKGRUOUND Since image-based fracture prediction models using deep learning are lacking, we aimed to develop an X-ray-based fracture prediction model using deep learning with longitudinal data. METHODS This study included 1,595 participants aged 50 to 75 years with at least two lumbosacral radiographs without baseline fractures from 2010 to 2015 at Seoul National University Hospital. Positive and negative cases were defined according to whether vertebral fractures developed during follow-up. The cases were divided into training (n=1,416) and test (n=179) sets. A convolutional neural network (CNN)-based prediction algorithm, DeepSurv, was trained with images and baseline clinical information (age, sex, body mass index, glucocorticoid use, and secondary osteoporosis). The concordance index (C-index) was used to compare performance between DeepSurv and the Fracture Risk Assessment Tool (FRAX) and Cox proportional hazard (CoxPH) models. RESULTS Of the total participants, 1,188 (74.4%) were women, and the mean age was 60.5 years. During a mean follow-up period of 40.7 months, vertebral fractures occurred in 7.5% (120/1,595) of participants. In the test set, when DeepSurv learned with images and clinical features, it showed higher performance than FRAX and CoxPH in terms of C-index values (DeepSurv, 0.612; 95% confidence interval [CI], 0.571 to 0.653; FRAX, 0.547; CoxPH, 0.594; 95% CI, 0.552 to 0.555). Notably, the DeepSurv method without clinical features had a higher C-index (0.614; 95% CI, 0.572 to 0.656) than that of FRAX in women. CONCLUSION DeepSurv, a CNN-based prediction algorithm using baseline image and clinical information, outperformed the FRAX and CoxPH models in predicting osteoporotic fracture from spine radiographs in a longitudinal cohort.
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Affiliation(s)
- Sung Hye Kong
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
| | | | | | | | | | - Jung Hee Kim
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
- Corresponding author: Jung Hee Kim. Department of Internal Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea Tel: +82-2-2072-4839, Fax: +82-2-2072-7246, E-mail:
| | - Chan Soo Shin
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
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Liu T, Lu Y, Zhang Y, Hu J, Gao C. A bone segmentation method based on Multi-scale features fuse U2Net and improved dice loss in CT image process. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103813] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Zhang X, Yang Y, Shen YW, Zhang KR, Jiang ZK, Ma LT, Ding C, Wang BY, Meng Y, Liu H. Diagnostic accuracy and potential covariates of artificial intelligence for diagnosing orthopedic fractures: a systematic literature review and meta-analysis. Eur Radiol 2022; 32:7196-7216. [PMID: 35754091 DOI: 10.1007/s00330-022-08956-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/07/2022] [Accepted: 06/08/2022] [Indexed: 02/05/2023]
Abstract
OBJECTIVES To systematically quantify the diagnostic accuracy and identify potential covariates affecting the performance of artificial intelligence (AI) in diagnosing orthopedic fractures. METHODS PubMed, Embase, Web of Science, and Cochrane Library were systematically searched for studies on AI applications in diagnosing orthopedic fractures from inception to September 29, 2021. Pooled sensitivity and specificity and the area under the receiver operating characteristic curves (AUC) were obtained. This study was registered in the PROSPERO database prior to initiation (CRD 42021254618). RESULTS Thirty-nine were eligible for quantitative analysis. The overall pooled AUC, sensitivity, and specificity were 0.96 (95% CI 0.94-0.98), 90% (95% CI 87-92%), and 92% (95% CI 90-94%), respectively. In subgroup analyses, multicenter designed studies yielded higher sensitivity (92% vs. 88%) and specificity (94% vs. 91%) than single-center studies. AI demonstrated higher sensitivity with transfer learning (with vs. without: 92% vs. 87%) or data augmentation (with vs. without: 92% vs. 87%), compared to those without. Utilizing plain X-rays as input images for AI achieved results comparable to CT (AUC 0.96 vs. 0.96). Moreover, AI achieved comparable results to humans (AUC 0.97 vs. 0.97) and better results than non-expert human readers (AUC 0.98 vs. 0.96; sensitivity 95% vs. 88%). CONCLUSIONS AI demonstrated high accuracy in diagnosing orthopedic fractures from medical images. Larger-scale studies with higher design quality are needed to validate our findings. KEY POINTS • Multicenter study design, application of transfer learning, and data augmentation are closely related to improving the performance of artificial intelligence models in diagnosing orthopedic fractures. • Utilizing plain X-rays as input images for AI to diagnose fractures achieved results comparable to CT (AUC 0.96 vs. 0.96). • AI achieved comparable results to humans (AUC 0.97 vs. 0.97) but was superior to non-expert human readers (AUC 0.98 vs. 0.96, sensitivity 95% vs. 88%) in diagnosing fractures.
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Affiliation(s)
- Xiang Zhang
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, No. 37 Guo Xue Rd, Chengdu, 610041, China
| | - Yi Yang
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, No. 37 Guo Xue Rd, Chengdu, 610041, China
| | - Yi-Wei Shen
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, No. 37 Guo Xue Rd, Chengdu, 610041, China
| | - Ke-Rui Zhang
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, No. 37 Guo Xue Rd, Chengdu, 610041, China
| | - Ze-Kun Jiang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610000, China
| | - Li-Tai Ma
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, No. 37 Guo Xue Rd, Chengdu, 610041, China
| | - Chen Ding
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, No. 37 Guo Xue Rd, Chengdu, 610041, China
| | - Bei-Yu Wang
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, No. 37 Guo Xue Rd, Chengdu, 610041, China
| | - Yang Meng
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, No. 37 Guo Xue Rd, Chengdu, 610041, China
| | - Hao Liu
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, No. 37 Guo Xue Rd, Chengdu, 610041, China.
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Niiya A, Murakami K, Kobayashi R, Sekimoto A, Saeki M, Toyofuku K, Kato M, Shinjo H, Ito Y, Takei M, Murata C, Ohgiya Y. Development of an artificial intelligence-assisted computed tomography diagnosis technology for rib fracture and evaluation of its clinical usefulness. Sci Rep 2022; 12:8363. [PMID: 35589847 PMCID: PMC9119970 DOI: 10.1038/s41598-022-12453-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 05/03/2022] [Indexed: 11/20/2022] Open
Abstract
Artificial intelligence algorithms utilizing deep learning are helpful tools for diagnostic imaging. A deep learning-based automatic detection algorithm was developed for rib fractures on computed tomography (CT) images of high-energy trauma patients. In this study, the clinical effectiveness of this algorithm was evaluated. A total of 56 cases were retrospectively examined, including 46 rib fractures and 10 control cases from our hospital, between January and June 2019. Two radiologists annotated the fracture lesions (complete or incomplete) for each CT image, which is considered the “ground truth.” Thereafter, the algorithm’s diagnostic results for all cases were compared with the ground truth, and the sensitivity and number of false positive (FP) results per case were assessed. The radiologists identified 199 images with a fracture. The sensitivity of the algorithm was 89.8%, and the number of FPs per case was 2.5. After additional learning, the sensitivity increased to 93.5%, and the number of FPs was 1.9 per case. FP results were found in the trabecular bone with the appearance of fracture, vascular grooves, and artifacts. The sensitivity of the algorithm used in this study was sufficient to aid the rapid detection of rib fractures within the evaluated validation set of CT images.
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Affiliation(s)
- Akifumi Niiya
- Department of Radiology, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan.
| | - Kouzou Murakami
- Department of Radiology, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan
| | - Rei Kobayashi
- Department of Radiology, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan
| | - Atsuhito Sekimoto
- Department of Radiology, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan
| | - Miho Saeki
- Department of Radiology, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan
| | - Kosuke Toyofuku
- Department of Radiology, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan
| | - Masako Kato
- Department of Radiology, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan
| | - Hidenori Shinjo
- Department of Radiology, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan
| | - Yoshinori Ito
- Department of Radiology, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan
| | - Mizuki Takei
- Fujifilm Corporation, Nishiazabu 2-Chome, Minato-ku, Tokyo, 26-30, Japan
| | - Chiori Murata
- Fujifilm Corporation, Nishiazabu 2-Chome, Minato-ku, Tokyo, 26-30, Japan
| | - Yoshimitsu Ohgiya
- Department of Radiology, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan
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Kuo RYL, Harrison C, Curran TA, Jones B, Freethy A, Cussons D, Stewart M, Collins GS, Furniss D. Artificial Intelligence in Fracture Detection: A Systematic Review and Meta-Analysis. Radiology 2022; 304:50-62. [PMID: 35348381 DOI: 10.1148/radiol.211785] [Citation(s) in RCA: 60] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Background Patients with fractures are a common emergency presentation and may be misdiagnosed at radiologic imaging. An increasing number of studies apply artificial intelligence (AI) techniques to fracture detection as an adjunct to clinician diagnosis. Purpose To perform a systematic review and meta-analysis comparing the diagnostic performance in fracture detection between AI and clinicians in peer-reviewed publications and the gray literature (ie, articles published on preprint repositories). Materials and Methods A search of multiple electronic databases between January 2018 and July 2020 (updated June 2021) was performed that included any primary research studies that developed and/or validated AI for the purposes of fracture detection at any imaging modality and excluded studies that evaluated image segmentation algorithms. Meta-analysis with a hierarchical model to calculate pooled sensitivity and specificity was used. Risk of bias was assessed by using a modified Prediction Model Study Risk of Bias Assessment Tool, or PROBAST, checklist. Results Included for analysis were 42 studies, with 115 contingency tables extracted from 32 studies (55 061 images). Thirty-seven studies identified fractures on radiographs and five studies identified fractures on CT images. For internal validation test sets, the pooled sensitivity was 92% (95% CI: 88, 93) for AI and 91% (95% CI: 85, 95) for clinicians, and the pooled specificity was 91% (95% CI: 88, 93) for AI and 92% (95% CI: 89, 92) for clinicians. For external validation test sets, the pooled sensitivity was 91% (95% CI: 84, 95) for AI and 94% (95% CI: 90, 96) for clinicians, and the pooled specificity was 91% (95% CI: 81, 95) for AI and 94% (95% CI: 91, 95) for clinicians. There were no statistically significant differences between clinician and AI performance. There were 22 of 42 (52%) studies that were judged to have high risk of bias. Meta-regression identified multiple sources of heterogeneity in the data, including risk of bias and fracture type. Conclusion Artificial intelligence (AI) and clinicians had comparable reported diagnostic performance in fracture detection, suggesting that AI technology holds promise as a diagnostic adjunct in future clinical practice. Clinical trial registration no. CRD42020186641 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Cohen and McInnes in this issue.
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Affiliation(s)
- Rachel Y L Kuo
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
| | - Conrad Harrison
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
| | - Terry-Ann Curran
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
| | - Benjamin Jones
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
| | - Alexander Freethy
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
| | - David Cussons
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
| | - Max Stewart
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
| | - Gary S Collins
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
| | - Dominic Furniss
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
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In Silico Finite Element Modeling of Stress Distribution in Osteosynthesis after Pertrochanteric Fractures. J Clin Med 2022; 11:jcm11071885. [PMID: 35407491 PMCID: PMC8999495 DOI: 10.3390/jcm11071885] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/15/2022] [Accepted: 03/25/2022] [Indexed: 12/13/2022] Open
Abstract
A stabilization method of pertrochanteric femur fractures is a contentious issue. Here, we assess the feasibility of rapid in silico 2D finite element modeling (FEM) to predict the distribution of stresses arising during the two most often used stabilization methods: gamma nail fixation (GNF) and dynamic hip screw (DHS). The modeling was based on standard pre-surgery radiographs of hip joints of 15 patients with pertrochanteric fractures of type A1, A2, and A3 according to the AO/OTA classification. The FEM showed that the stresses were similar for both GNF and DHS, with the medians ranging between 53-60 MPa and consistently lower for A1 than A3 fractures. Stresses also appeared in the fixation materials being about two-fold higher for GNF. Given similar bone stresses caused by both GNF and DHS but shorter surgery time, less extensive dissection, and faster patient mobilization, we submit that the GNF stabilization appears to be the most optimal system for pertrochanteric fractures. In silico FEM appears a viable perioperative method that helps predict the distribution of compressive stresses after osteosynthesis of pertrochanteric fractures. The promptness of modeling fits well into the rigid time framework of hip fracture surgery and may help optimize the fixation procedure for the best outcome. The study extends the use of FEM in complex orthopedic management. However, further datasets are required to firmly position the FEM in the treatment of pertrochanteric fractures.
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Shinohara I, Inui A, Mifune Y, Nishimoto H, Yamaura K, Mukohara S, Yoshikawa T, Kato T, Furukawa T, Hoshino Y, Matsushita T, Kuroda R. Diagnosis of Cubital Tunnel Syndrome Using Deep Learning on Ultrasonographic Images. Diagnostics (Basel) 2022; 12:diagnostics12030632. [PMID: 35328185 PMCID: PMC8947597 DOI: 10.3390/diagnostics12030632] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 02/28/2022] [Accepted: 03/02/2022] [Indexed: 02/04/2023] Open
Abstract
Although electromyography is the routine diagnostic method for cubital tunnel syndrome (CuTS), imaging diagnosis by measuring cross-sectional area (CSA) with ultrasonography (US) has also been attempted in recent years. In this study, deep learning (DL), an artificial intelligence (AI) method, was used on US images, and its diagnostic performance for detecting CuTS was investigated. Elbow images of 30 healthy volunteers and 30 patients diagnosed with CuTS were used. Three thousand US images were prepared per each group to visualize the short axis of the ulnar nerve. Transfer learning was performed on 5000 randomly selected training images using three pre-trained models, and the remaining images were used for testing. The model was evaluated by analyzing a confusion matrix and the area under the receiver operating characteristic curve. Occlusion sensitivity and locally interpretable model-agnostic explanations were used to visualize the features deemed important by the AI. The highest score had an accuracy of 0.90, a precision of 0.86, a recall of 1.00, and an F-measure of 0.92. Visualization results show that the DL models focused on the epineurium of the ulnar nerve and the surrounding soft tissue. The proposed technique enables the accurate prediction of CuTS without the need to measure CSA.
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Affiliation(s)
| | - Atsuyuki Inui
- Correspondence: ; Tel.: +81-78-382-5111; Fax: +81-78-351-6944
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Wang X, Xu Z, Tong Y, Xia L, Jie B, Ding P, Bai H, Zhang Y, He Y. Detection and classification of mandibular fracture on CT scan using deep convolutional neural network. Clin Oral Investig 2022; 26:4593-4601. [PMID: 35218428 DOI: 10.1007/s00784-022-04427-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 02/19/2022] [Indexed: 12/15/2022]
Abstract
OBJECTIVES This study aimed to evaluate the accuracy and reliability of convolutional neural networks (CNNs) for the detection and classification of mandibular fracture on spiral computed tomography (CT). MATERIALS AND METHODS Between January 2013 and July 2020, 686 patients with mandibular fractures who underwent CT scan were classified and annotated by three experienced maxillofacial surgeons serving as the ground truth. An algorithm including two convolutional neural networks (U-Net and ResNet) was trained, validated, and tested using 222, 56, and 408 CT scans, respectively. The diagnostic performance of the algorithm was compared with the ground truth and evaluated by DICE, accuracy, sensitivity, specificity, and area under the ROC curve (AUC). RESULTS One thousand five hundred six mandibular fractures in nine subregions of 686 patients were diagnosed. The DICE of mandible segmentation using U-Net was 0.943. The accuracies of nine subregions were all above 90%, with a mean AUC of 0.956. CONCLUSIONS CNNs showed comparable reliability and accuracy in detecting and classifying mandibular fractures on CT. CLINICAL RELEVANCE The algorithm for automatic detection and classification of mandibular fractures will help improve diagnostic efficiency and provide expertise to areas with lower medical levels.
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Affiliation(s)
- Xuebing Wang
- Department of Oral and Maxillofacial SurgeryNational Engineering Laboratory for Digital and Material Technology of Stomatology; Beijing Key Laboratory of Digital StomatologyNational Clinical Research Center for Oral Diseases, Peking University School and Hospital of Stomatology, No 22 Zhongguancun South Road, Beijing, 100081, People's Republic of China
| | | | - Yanhang Tong
- Department of Oral and Maxillofacial SurgeryNational Engineering Laboratory for Digital and Material Technology of Stomatology; Beijing Key Laboratory of Digital StomatologyNational Clinical Research Center for Oral Diseases, Peking University School and Hospital of Stomatology, No 22 Zhongguancun South Road, Beijing, 100081, People's Republic of China
| | - Long Xia
- Plastic Surgery Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Bimeng Jie
- Department of Oral and Maxillofacial SurgeryNational Engineering Laboratory for Digital and Material Technology of Stomatology; Beijing Key Laboratory of Digital StomatologyNational Clinical Research Center for Oral Diseases, Peking University School and Hospital of Stomatology, No 22 Zhongguancun South Road, Beijing, 100081, People's Republic of China
| | | | | | - Yi Zhang
- Department of Oral and Maxillofacial SurgeryNational Engineering Laboratory for Digital and Material Technology of Stomatology; Beijing Key Laboratory of Digital StomatologyNational Clinical Research Center for Oral Diseases, Peking University School and Hospital of Stomatology, No 22 Zhongguancun South Road, Beijing, 100081, People's Republic of China
| | - Yang He
- Department of Oral and Maxillofacial SurgeryNational Engineering Laboratory for Digital and Material Technology of Stomatology; Beijing Key Laboratory of Digital StomatologyNational Clinical Research Center for Oral Diseases, Peking University School and Hospital of Stomatology, No 22 Zhongguancun South Road, Beijing, 100081, People's Republic of China.
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A Progressive and Cross-Domain Deep Transfer Learning Framework for Wrist Fracture Detection. JOURNAL OF ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING RESEARCH 2022. [DOI: 10.2478/jaiscr-2022-0007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Abstract
There has been an amplified focus on and benefit from the adoption of artificial intelligence (AI) in medical imaging applications. However, deep learning approaches involve training with massive amounts of annotated data in order to guarantee generalization and achieve high accuracies. Gathering and annotating large sets of training images require expertise which is both expensive and time-consuming, especially in the medical field. Furthermore, in health care systems where mistakes can have catastrophic consequences, there is a general mistrust in the black-box aspect of AI models. In this work, we focus on improving the performance of medical imaging applications when limited data is available while focusing on the interpretability aspect of the proposed AI model. This is achieved by employing a novel transfer learning framework, progressive transfer learning, an automated annotation technique and a correlation analysis experiment on the learned representations.
Progressive transfer learning helps jump-start the training of deep neural networks while improving the performance by gradually transferring knowledge from two source tasks into the target task. It is empirically tested on the wrist fracture detection application by first training a general radiology network RadiNet and using its weights to initialize RadiNetwrist
, that is trained on wrist images to detect fractures. Experiments show that RadiNetwrist
achieves an accuracy of 87% and an AUC ROC of 94% as opposed to 83% and 92% when it is pre-trained on the ImageNet dataset.
This improvement in performance is investigated within an explainable AI framework. More concretely, the learned deep representations of RadiNetwrist
are compared to those learned by the baseline model by conducting a correlation analysis experiment. The results show that, when transfer learning is gradually applied, some features are learned earlier in the network. Moreover, the deep layers in the progressive transfer learning framework are shown to encode features that are not encountered when traditional transfer learning techniques are applied.
In addition to the empirical results, a clinical study is conducted and the performance of RadiNetwrist
is compared to that of an expert radiologist. We found that RadiNetwrist
exhibited similar performance to that of radiologists with more than 20 years of experience.
This motivates follow-up research to train on more data to feasibly surpass radiologists’ performance, and investigate the interpretability of AI models in the healthcare domain where the decision-making process needs to be credible and transparent.
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A Surgeon's Guide to Understanding Artificial Intelligence and Machine Learning Studies in Orthopaedic Surgery. Curr Rev Musculoskelet Med 2022; 15:121-132. [PMID: 35141847 DOI: 10.1007/s12178-022-09738-7] [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] [Accepted: 01/17/2022] [Indexed: 10/19/2022]
Abstract
PURPOSE OF REVIEW In recent years, machine learning techniques have been increasingly utilized across medicine, impacting the practice and delivery of healthcare. The data-driven nature of orthopaedic surgery presents many targets for improvement through the use of artificial intelligence, which is reflected in the increasing number of publications in the medical literature. However, the unique methodologies utilized in AI studies can present a barrier to its widespread acceptance and use in orthopaedics. The purpose of our review is to provide a tool that can be used by practitioners to better understand and ultimately leverage AI studies. RECENT FINDINGS The increasing interest in machine learning across medicine is reflected in a greater utilization of AI in recent medical literature. The process of designing machine learning studies includes study design, model choice, data collection/handling, model development, training, testing, and interpretation. Recent studies leveraging ML in orthopaedics provide useful examples for future research endeavors. This manuscript intends to create a guide discussing the use of machine learning and artificial intelligence in orthopaedic surgery research. Our review outlines the process of creating a machine learning algorithm and discusses the different model types, utilizing examples from recent orthopaedic literature to illustrate the techniques involved.
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Laur O, Wang B. Musculoskeletal trauma and artificial intelligence: current trends and projections. Skeletal Radiol 2022; 51:257-269. [PMID: 34089338 DOI: 10.1007/s00256-021-03824-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 05/13/2021] [Accepted: 05/18/2021] [Indexed: 02/02/2023]
Abstract
Musculoskeletal trauma accounts for a significant fraction of emergency department visits and patients seeking urgent care, with a high financial cost to society. Diagnostic imaging is indispensable in the workup and management of trauma patients. However, diagnostic imaging represents a complex multifaceted system, with many aspects of its workflow prone to inefficiencies or human error. Recent technological innovations in artificial intelligence and machine learning have shown promise to revolutionize our systems for providing medical care to patients. This review will provide a general overview of the current state of artificial intelligence and machine learning applications in different aspects of trauma imaging and provide a vision for how such applications could be leveraged to enhance our diagnostic imaging systems and optimize patient outcomes.
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Affiliation(s)
- Olga Laur
- Division of Musculoskeletal Radiology, Department of Radiology, NYU Langone Health, 301 East 17th Street, 6th Floor, New York, NY, 10003, USA
| | - Benjamin Wang
- Division of Musculoskeletal Radiology, Department of Radiology, NYU Langone Health, 301 East 17th Street, 6th Floor, New York, NY, 10003, USA.
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Lee CC, Jung KH, Lee KJ, Park KB. A Bibliometric Analysis of the Field of Computer-Assisted Orthopedic Surgery during 2002–2021. Clin Orthop Surg 2022; 15:227-233. [PMID: 37008968 PMCID: PMC10060768 DOI: 10.4055/cios21217] [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: 11/20/2021] [Revised: 01/23/2022] [Accepted: 02/04/2022] [Indexed: 11/06/2022] Open
Abstract
Background This study aimed to investigate the characteristics of research articles and research trends in computer-assisted orthopedic surgery (CAOS) by conducting bibliometric analyses. Methods CAOS-related research articles published in international journals from 2002 to 2021 were collected using the PubMed database and analyzed using the bibliometric method. Their publication year, journal name, corresponding author's country name, and the number of citations of all collected articles were noted. Contents of the articles were analyzed to evaluate the time point and anatomical site at which the digital technique was applied. Further, the 20-year period was divided into two halves of 10 years each to analyze the research trends. Results A total of 639 CAOS-related articles were identified. An average of 32.0 CAOS-related articles were published annually, with an average of 20.6 and 43.3 published in the first half and second half, respectively. Of all articles, 47.6% were published in the top 10 journals, and 81.2% were written in the top 10 countries. The total numbers of citations were 11.7 and 6.3 in the first and second halves, respectively, but the average annual number of citations was higher in the second half than in the first one. Articles on application of digital techniques during surgery were 62.3% and those on pre-surgery application were 36.9%. Further, articles in the knee (39.0%), spine (28.5%), and hip and pelvis (21.5%) fields accounted for 89.0% of the total publications. But the increase in publications in the said period was highest in the fields of the hand and wrist (+1,300.0%), ankle (+466.7%), and shoulder (+366.7%). Conclusions Over the last 20 years, the publication of CAOS-related research articles in international journals has grown steadily. Although the knee, spine, hip, and pelvis fields account for most CAOS-related research, research in new fields is also increasing. This study analyzed the types of articles and trends in CAOS-related research and provided useful information for future research in the field of CAOS.
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Affiliation(s)
- Chae-Chil Lee
- Department of Orthopedic Surgery, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Korea
| | - Kwang-Hwan Jung
- Department of Orthopedic Surgery, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Korea
| | - Kyung-Joo Lee
- Department of Orthopedic Surgery, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Korea
| | - Ki-Bong Park
- Department of Orthopedic Surgery, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Korea
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AIM in Endocrinology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
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Oliveira E Carmo L, van den Merkhof A, Olczak J, Gordon M, Jutte PC, Jaarsma RL, IJpma FFA, Doornberg JN, Prijs J. An increasing number of convolutional neural networks for fracture recognition and classification in orthopaedics : are these externally validated and ready for clinical application? Bone Jt Open 2021; 2:879-885. [PMID: 34669518 PMCID: PMC8558452 DOI: 10.1302/2633-1462.210.bjo-2021-0133] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Aims The number of convolutional neural networks (CNN) available for fracture detection and classification is rapidly increasing. External validation of a CNN on a temporally separate (separated by time) or geographically separate (separated by location) dataset is crucial to assess generalizability of the CNN before application to clinical practice in other institutions. We aimed to answer the following questions: are current CNNs for fracture recognition externally valid?; which methods are applied for external validation (EV)?; and, what are reported performances of the EV sets compared to the internal validation (IV) sets of these CNNs? Methods The PubMed and Embase databases were systematically searched from January 2010 to October 2020 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The type of EV, characteristics of the external dataset, and diagnostic performance characteristics on the IV and EV datasets were collected and compared. Quality assessment was conducted using a seven-item checklist based on a modified Methodologic Index for NOn-Randomized Studies instrument (MINORS). Results Out of 1,349 studies, 36 reported development of a CNN for fracture detection and/or classification. Of these, only four (11%) reported a form of EV. One study used temporal EV, one conducted both temporal and geographical EV, and two used geographical EV. When comparing the CNN’s performance on the IV set versus the EV set, the following were found: AUCs of 0.967 (IV) versus 0.975 (EV), 0.976 (IV) versus 0.985 to 0.992 (EV), 0.93 to 0.96 (IV) versus 0.80 to 0.89 (EV), and F1-scores of 0.856 to 0.863 (IV) versus 0.757 to 0.840 (EV). Conclusion The number of externally validated CNNs in orthopaedic trauma for fracture recognition is still scarce. This greatly limits the potential for transfer of these CNNs from the developing institute to another hospital to achieve similar diagnostic performance. We recommend the use of geographical EV and statements such as the Consolidated Standards of Reporting Trials–Artificial Intelligence (CONSORT-AI), the Standard Protocol Items: Recommendations for Interventional Trials–Artificial Intelligence (SPIRIT-AI) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis–Machine Learning (TRIPOD-ML) to critically appraise performance of CNNs and improve methodological rigor, quality of future models, and facilitate eventual implementation in clinical practice. Cite this article: Bone Jt Open 2021;2(10):879–885.
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Affiliation(s)
- Luisa Oliveira E Carmo
- Department of Orthopaedic Surgery, University Medical Centre, University of Groningen, Groningen, Groningen, Netherlands
| | - Anke van den Merkhof
- Department of Orthopaedic Surgery, Flinders Medical Centre, Bedford Park, Adelaide, South Australia, Australia.,Flinders University, Bedford Park, Adelaide, South Australia, Australia
| | - Jakub Olczak
- Institute of Clinical Sciences, Danderyd University Hospital, Karolinska Institute, Stockholm, Sweden
| | - Max Gordon
- Institute of Clinical Sciences, Danderyd University Hospital, Karolinska Institute, Stockholm, Sweden
| | - Paul C Jutte
- Department of Orthopaedic Surgery, University Medical Centre, University of Groningen, Groningen, Groningen, Netherlands
| | - Ruurd L Jaarsma
- Department of Orthopaedic Surgery, Flinders Medical Centre, Bedford Park, Adelaide, South Australia, Australia.,Flinders University, Bedford Park, Adelaide, South Australia, Australia
| | - Frank F A IJpma
- Department of Trauma Surgery, University Medical Centre Groningen, University of Groningen, Groningen, Groningen, Netherlands
| | - Job N Doornberg
- Department of Orthopaedic Surgery, University Medical Centre, University of Groningen, Groningen, Groningen, Netherlands.,Department of Orthopaedic Surgery, Flinders Medical Centre, Bedford Park, Adelaide, South Australia, Australia.,Flinders University, Bedford Park, Adelaide, South Australia, Australia.,Department of Trauma Surgery, University Medical Centre Groningen, University of Groningen, Groningen, Groningen, Netherlands
| | - Jasper Prijs
- Department of Orthopaedic Surgery, University Medical Centre, University of Groningen, Groningen, Groningen, Netherlands.,Department of Orthopaedic Surgery, Flinders Medical Centre, Bedford Park, Adelaide, South Australia, Australia.,Flinders University, Bedford Park, Adelaide, South Australia, Australia.,Department of Trauma Surgery, University Medical Centre Groningen, University of Groningen, Groningen, Groningen, Netherlands
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- Machine Learning Consortium
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Xu Y, Li Y, Yin H, Tang W, Fan G. Consecutive Serial Non-Contrast CT Scan-Based Deep Learning Model Facilitates the Prediction of Tumor Invasiveness of Ground-Glass Nodules. Front Oncol 2021; 11:725599. [PMID: 34568054 PMCID: PMC8461974 DOI: 10.3389/fonc.2021.725599] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 08/19/2021] [Indexed: 01/31/2023] Open
Abstract
Introduction Tumors are continuously evolving biological systems which can be monitored by medical imaging. Previous studies only focus on single timepoint images, whether the performance could be further improved by using serial noncontrast CT imaging obtained during nodule follow-up management remains unclear. In this study, we evaluated DL model for predicting tumor invasiveness of GGNs through analyzing time series CT images. Methods A total of 168 pathologically confirmed GGN cases (48 noninvasive lesions and 120 invasive lesions) were retrospectively collected and randomly assigned to the development dataset (n = 123) and independent testing dataset (n = 45). All patients underwent consecutive noncontrast CT examinations, and the baseline CT and 3-month follow-up CT images were collected. The gross region of interest (ROI) patches containing only tumor region and the full ROI patches including both tumor and peritumor regions were cropped from CT images. A baseline model was built on the image features and demographic features. Four DL models were proposed: two single-DL model using gross ROI (model 1) or full ROI patches (model 3) from baseline CT images, and two serial-DL models using gross ROI (model 2) or full ROI patches (model 4) from consecutive CT images (baseline scan and 3-month follow-up scan). In addition, a combined model integrating serial full ROI patches and clinical information was also constructed. The performance of these predictive models was assessed with respect to discrimination and clinical usefulness. Results The area under the curve (AUC) of the baseline model, models 1, 2, 3, and 4 were 0.562 [(95% confidence interval (C)], 0.406~0.710), 0.693 (95% CI, 0.538-0.822), 0.787 (95% CI, 0.639-0.895), 0.727 (95% CI, 0.573-0.849), and 0.811 (95% CI, 0.667-0.912) in the independent testing dataset, respectively. The results indicated that the peritumor region had potential to contribute to tumor invasiveness prediction, and the model performance was further improved by integrating imaging scans at multiple timepoints. Furthermore, the combined model showed best discrimination ability, with AUC, sensitivity, specificity, and accuracy achieving 0.831 (95% CI, 0.690-0.926), 86.7%, 73.3%, and 82.2%, respectively. Conclusion The DL model integrating full ROIs from serial CT images shows improved predictive performance in differentiating noninvasive from invasive GGNs than the model using only baseline CT images, which could benefit the clinical management of GGNs.
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Affiliation(s)
- Yao Xu
- Department of Radiology, Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Yu Li
- Department of Radiology, Dushuhu Public Hospital Affiliated of Soochow University, Suzhou, China
| | - Hongkun Yin
- Department of Advanced Research, Infervision Medical Technology Co. Ltd, Beijing, China
| | - Wen Tang
- Department of Advanced Research, Infervision Medical Technology Co. Ltd, Beijing, China
| | - Guohua Fan
- Department of Radiology, Second Affiliated Hospital of Soochow University, Suzhou, China
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Zapaishchykova A, Dreizin D, Li Z, Wu JY, Roohi SF, Unberath M. An Interpretable Approach to Automated Severity Scoring in Pelvic Trauma. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2021; 12903:424-433. [PMID: 37483538 PMCID: PMC10362989 DOI: 10.1007/978-3-030-87199-4_40] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Pelvic ring disruptions result from blunt injury mechanisms and are often found in patients with multi-system trauma. To grade pelvic fracture severity in trauma victims based on whole-body CT, the Tile AO/OTA classification is frequently used. Due to the high volume of whole-body trauma CTs generated in busy trauma centers, an automated approach to Tile classification would provide substantial value, e. g., to prioritize the reading queue of the attending trauma radiologist. In such scenario, an automated method should perform grading based on a transparent process and based on interpretable features to enable interaction with human readers and lower their workload by offering insights from a first automated read of the scan. This paper introduces an automated yet interpretable pelvic trauma decision support system to assist radiologists in fracture detection and Tile grade classification. The method operates similarly to human interpretation of CT scans and first detects distinct pelvic fractures on CT with high specificity using a Faster-RCNN model that are then interpreted using a structural causal model based on clinical best practices to infer an initial Tile grade. The Bayesian causal model and finally, the object detector are then queried for likely co-occurring fractures that may have been rejected initially due to the highly specific operating point of the detector, resulting in an updated list of detected fractures and corresponding final Tile grade. Our method is transparent in that it provides finding location and type using the object detector, as well as information on important counterfactuals that would invalidate the system's recommendation and achieves an AUC of 83.3%/85.1% for translational/rotational instability. Despite being designed for human-machine teaming, our approach does not compromise on performance compared to previous black-box approaches.
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