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Zamani-Siahkali N, Mirshahvalad SA, Farbod A, Divband G, Pirich C, Veit-Haibach P, Cook G, Beheshti M. SPECT/CT, PET/CT, and PET/MRI for Response Assessment of Bone Metastases. Semin Nucl Med 2024; 54:356-370. [PMID: 38172001 DOI: 10.1053/j.semnuclmed.2023.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 11/14/2023] [Indexed: 01/05/2024]
Abstract
Recent developments in hybrid SPECT/CT systems and the use of cadmium-zinc-telluride (CZT) detectors have improved the diagnostic accuracy of bone scintigraphy. These advancements have paved the way for novel quantitative approaches to accurate and reproducible treatment monitoring of bone metastases. PET/CT imaging using [18F]F-FDG and [18F]F-NaF have shown promising clinical utility in bone metastases assessment and monitoring response to therapy and prediction of treatment response in a broad range of malignancies. Additionally, specific tumor-targeting tracers like [99mTc]Tc-PSMA, [68Ga]Ga-PSMA, or [11C]C- or [18F]F-Choline revealed high diagnostic performance for early assessment and prognostication of bone metastases, particularly in prostate cancer. PET/MRI appears highly accurate imaging modality, but has associated limitations notably, limited availability, more complex logistics and high installation costs. Advances in artificial intelligence (Al) seem to improve the accuracy of imaging modalities and provide an assistant role in the evaluation of treatment response of bone metastases.
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Affiliation(s)
- Nazanin Zamani-Siahkali
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria; Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Seyed Ali Mirshahvalad
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria; Joint Department of Medical Imaging, University Medical Imaging Toronto, University Health Network, Sinai Health System, Women's College Hospital, University of Toronto, Toronto, Canada
| | - Abolfazl Farbod
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria; Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Christian Pirich
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria
| | - Patrick Veit-Haibach
- Joint Department of Medical Imaging, University Medical Imaging Toronto, University Health Network, Sinai Health System, Women's College Hospital, University of Toronto, Toronto, Canada
| | - Gary Cook
- Cancer Imaging Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Mohsen Beheshti
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria.
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Lopes S, Rocha G, Guimarães-Pereira L. Artificial intelligence and its clinical application in Anesthesiology: a systematic review. J Clin Monit Comput 2024; 38:247-259. [PMID: 37864754 PMCID: PMC10995017 DOI: 10.1007/s10877-023-01088-0] [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: 06/11/2023] [Accepted: 10/04/2023] [Indexed: 10/23/2023]
Abstract
PURPOSE Application of artificial intelligence (AI) in medicine is quickly expanding. Despite the amount of evidence and promising results, a thorough overview of the current state of AI in clinical practice of anesthesiology is needed. Therefore, our study aims to systematically review the application of AI in this context. METHODS A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched Medline and Web of Science for articles published up to November 2022 using terms related with AI and clinical practice of anesthesiology. Articles that involved animals, editorials, reviews and sample size lower than 10 patients were excluded. Characteristics and accuracy measures from each study were extracted. RESULTS A total of 46 articles were included in this review. We have grouped them into 4 categories with regard to their clinical applicability: (1) Depth of Anesthesia Monitoring; (2) Image-guided techniques related to Anesthesia; (3) Prediction of events/risks related to Anesthesia; (4) Drug administration control. Each group was analyzed, and the main findings were summarized. Across all fields, the majority of AI methods tested showed superior performance results compared to traditional methods. CONCLUSION AI systems are being integrated into anesthesiology clinical practice, enhancing medical professionals' skills of decision-making, diagnostic accuracy, and therapeutic response.
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Affiliation(s)
- Sara Lopes
- Department of Anesthesiology, Centro Hospitalar Universitário São João, Porto, Portugal.
| | - Gonçalo Rocha
- Surgery and Physiology Department, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Luís Guimarães-Pereira
- Department of Anesthesiology, Centro Hospitalar Universitário São João, Porto, Portugal
- Surgery and Physiology Department, Faculty of Medicine, University of Porto, Porto, Portugal
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Gitto S, Serpi F, Albano D, Risoleo G, Fusco S, Messina C, Sconfienza LM. AI applications in musculoskeletal imaging: a narrative review. Eur Radiol Exp 2024; 8:22. [PMID: 38355767 PMCID: PMC10866817 DOI: 10.1186/s41747-024-00422-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 12/29/2023] [Indexed: 02/16/2024] Open
Abstract
This narrative review focuses on clinical applications of artificial intelligence (AI) in musculoskeletal imaging. A range of musculoskeletal disorders are discussed using a clinical-based approach, including trauma, bone age estimation, osteoarthritis, bone and soft-tissue tumors, and orthopedic implant-related pathology. Several AI algorithms have been applied to fracture detection and classification, which are potentially helpful tools for radiologists and clinicians. In bone age assessment, AI methods have been applied to assist radiologists by automatizing workflow, thus reducing workload and inter-observer variability. AI may potentially aid radiologists in identifying and grading abnormal findings of osteoarthritis as well as predicting the onset or progression of this disease. Either alone or combined with radiomics, AI algorithms may potentially improve diagnosis and outcome prediction of bone and soft-tissue tumors. Finally, information regarding appropriate positioning of orthopedic implants and related complications may be obtained using AI algorithms. In conclusion, rather than replacing radiologists, the use of AI should instead help them to optimize workflow, augment diagnostic performance, and keep up with ever-increasing workload.Relevance statement This narrative review provides an overview of AI applications in musculoskeletal imaging. As the number of AI technologies continues to increase, it will be crucial for radiologists to play a role in their selection and application as well as to fully understand their potential value in clinical practice. Key points • AI may potentially assist musculoskeletal radiologists in several interpretative tasks.• AI applications to trauma, age estimation, osteoarthritis, tumors, and orthopedic implants are discussed.• AI should help radiologists to optimize workflow and augment diagnostic performance.
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Affiliation(s)
- Salvatore Gitto
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Cristina Belgioioso 173, Milan, 20157, Italy
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Francesca Serpi
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Cristina Belgioioso 173, Milan, 20157, Italy
| | - Domenico Albano
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Dipartimento di Scienze Biomediche, Chirurgiche ed Odontoiatriche, Università degli Studi di Milano, Milan, Italy
| | - Giovanni Risoleo
- Scuola di Specializzazione in Radiodiagnostica, Università degli Studi di Milano, Milan, Italy
| | - Stefano Fusco
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Cristina Belgioioso 173, Milan, 20157, Italy
| | - Carmelo Messina
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Cristina Belgioioso 173, Milan, 20157, Italy
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Luca Maria Sconfienza
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Cristina Belgioioso 173, Milan, 20157, Italy.
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
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Caloro E, Gnocchi G, Quarrella C, Ce M, Carrafiello G, Cellina M. Artificial Intelligence in Bone Metastasis Imaging: Recent Progresses from Diagnosis to Treatment - A Narrative Review. Crit Rev Oncog 2024; 29:77-90. [PMID: 38505883 DOI: 10.1615/critrevoncog.2023050470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
The introduction of artificial intelligence (AI) represents an actual revolution in the radiological field, including bone lesion imaging. Bone lesions are often detected both in healthy and oncological patients and the differential diagnosis can be challenging but decisive, because it affects the diagnostic and therapeutic process, especially in case of metastases. Several studies have already demonstrated how the integration of AI-based tools in the current clinical workflow could bring benefits to patients and to healthcare workers. AI technologies could help radiologists in early bone metastases detection, increasing the diagnostic accuracy and reducing the overdiagnosis and the number of unnecessary deeper investigations. In addition, radiomics and radiogenomics approaches could go beyond the qualitative features, visible to the human eyes, extrapolating cancer genomic and behavior information from imaging, in order to plan a targeted and personalized treatment. In this article, we want to provide a comprehensive summary of the most promising AI applications in bone metastasis imaging and their role from diagnosis to treatment and prognosis, including the analysis of future challenges and new perspectives.
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Affiliation(s)
- Elena Caloro
- Università degli studi di Milano, via Festa del Perdono, 7, 20122 Milan, Italy
| | - Giulia Gnocchi
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Cettina Quarrella
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Maurizio Ce
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy
| | - Gianpaolo Carrafiello
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy; Radiology Department, Fondazione IRCCS Cà Granda, Policlinico di Milano Ospedale Maggiore, Università di Milano, 20122 Milan, Italy
| | - Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121, Milan, Italy
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Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior KJ, Poirrier JE, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Rev Pharmacoecon Outcomes Res 2024; 24:63-115. [PMID: 37955147 DOI: 10.1080/14737167.2023.2279107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 10/31/2023] [Indexed: 11/14/2023]
Abstract
INTRODUCTION The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery. METHODS A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted. RESULTS In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively). EXPERT OPINION The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.
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Affiliation(s)
- Katarzyna Kolasa
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
| | - Bisrat Admassu
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
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Artificial Intelligence in Surgical Learning. SURGERIES 2023. [DOI: 10.3390/surgeries4010010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023] Open
Abstract
(1) Background: Artificial Intelligence (AI) is transforming healthcare on all levels. While AI shows immense potential, the clinical implementation is lagging. We present a concise review of AI in surgical learning; (2) Methods: A non-systematic review of AI in surgical learning of the literature in English is provided; (3) Results: AI shows utility for all components of surgical competence within surgical learning. AI presents with great potential within robotic surgery specifically (4) Conclusions: Technology will evolve in ways currently unimaginable, presenting us with novel applications of AI and derivatives thereof. Surgeons must be open to new modes of learning to be able to implement all evidence-based applications of AI in the future. Systematic analyses of AI in surgical learning are needed.
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Lacroix M, Aouad T, Feydy J, Biau D, Larousserie F, Fournier L, Feydy A. Artificial intelligence in musculoskeletal oncology imaging: A critical review of current applications. Diagn Interv Imaging 2023; 104:18-23. [PMID: 36270953 DOI: 10.1016/j.diii.2022.10.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Accepted: 10/05/2022] [Indexed: 01/10/2023]
Abstract
Artificial intelligence (AI) is increasingly being studied in musculoskeletal oncology imaging. AI has been applied to both primary and secondary bone tumors and assessed for various predictive tasks that include detection, segmentation, classification, and prognosis. Still, in the field of clinical research, further efforts are needed to improve AI reproducibility and reach an acceptable level of evidence in musculoskeletal oncology. This review describes the basic principles of the most common AI techniques, including machine learning, deep learning and radiomics. Then, recent developments and current results of AI in the field of musculoskeletal oncology are presented. Finally, limitations and future perspectives of AI in this field are discussed.
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Affiliation(s)
- Maxime Lacroix
- Department of Radiology, Hôpital Européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris, Paris, 75015, France; Université Paris Cité, Faculté de Médecine, Paris, 75006, France; PARCC UMRS 970, INSERM, Paris 75015, France
| | - Theodore Aouad
- Université Paris-Saclay, CentraleSupélec, Inria, Centre for Visual Computing, 91190, Gif-sur-Yvette, France
| | - Jean Feydy
- Université Paris Cité, HeKA team, Inria Paris, Inserm, 75006, Paris, France
| | - David Biau
- Université Paris Cité, Faculté de Médecine, Paris, 75006, France; Department of Orthopedic Surgery, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris, 75014, France
| | - Frédérique Larousserie
- Université Paris Cité, Faculté de Médecine, Paris, 75006, France; Department of Pathology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris, 75014, France
| | - Laure Fournier
- Department of Radiology, Hôpital Européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris, Paris, 75015, France; Université Paris Cité, Faculté de Médecine, Paris, 75006, France; PARCC UMRS 970, INSERM, Paris 75015, France
| | - Antoine Feydy
- Université Paris Cité, Faculté de Médecine, Paris, 75006, France; Department of Radiology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris, 75014, France
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Herrera-Ligero C, Chaler J, Bermejo-Bosch I. Strengthening education in rehabilitation: Assessment technology and digitalization. FRONTIERS IN REHABILITATION SCIENCES 2022; 3:883270. [PMID: 36188966 PMCID: PMC9449490 DOI: 10.3389/fresc.2022.883270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 08/08/2022] [Indexed: 11/29/2022]
Abstract
Rehabilitation is a discipline increasingly growing around the world due to several reasons, but probably the most important one is aging population and chronicity. A need to harmonize education has been identified, and although several International organizations such as the European Union of Medical Specialists (UEMS) and the International Society of Physical Medicine and Rehabilitation (ISPRM) have defined standards, given the quick growth of new evidence and assessment methods an urge to establish new ones arises. Functional assessment and tools used to do so are key in rehabilitation processes. This comprises self-reported questionnaires, conventional clinical evaluation but more notably high technology assessment methods, such as movement analysis systems, posturography, different types of dynamometers and kinesiologic electromyography among others. More recently, a wide range of wearable systems has been introduced in patient assessment. This is generating many published protocols as well as reliability and validity studies. The objective of this narrative review is to present main assessment technologies relevant to rehabilitation, its situation of this specific area in pre-graduate and post-graduate rehabilitation educational programs, and to elaborate a formative proposal including technological foundations of assessment and also highlighting the importance of solid reliability and validity of assessment methods comprehension. The main objective of this proposal is to provide basic knowledge about rehabilitation and methodologies for outcomes evaluation, including new technologies, to all health professionals, but especially to those who work or will work in the field of Rehabilitation.
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Affiliation(s)
- Cristina Herrera-Ligero
- Biomechanics Institute of Valencia, Polytechnic University of Valencia, Valencia, Spain
- Correspondence: Cristina Herrera-Ligero
| | - Joaquim Chaler
- University School of Health and Sport (EUSES & ENTI), University of Girona and University of Barcelona, L'Hospitalet de Llobregat, Catalonia, Spain
- PM&R Department, Hospital Egarsat, Barcelona, Spain
| | - Ignacio Bermejo-Bosch
- Biomechanics Institute of Valencia, Polytechnic University of Valencia, Valencia, Spain
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Tao Y, Huang X, Tan Y, Wang H, Jiang W, Chen Y, Wang C, Luo J, Liu Z, Gao K, Yang W, Guo M, Tang B, Zhou A, Yao M, Chen T, Cao Y, Luo C, Zhang J. Qualitative Histopathological Classification of Primary Bone Tumors Using Deep Learning: A Pilot Study. Front Oncol 2021; 11:735739. [PMID: 34692509 PMCID: PMC8526973 DOI: 10.3389/fonc.2021.735739] [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: 07/03/2021] [Accepted: 09/13/2021] [Indexed: 12/12/2022] Open
Abstract
Background Histopathological diagnosis of bone tumors is challenging for pathologists. We aim to classify bone tumors histopathologically in terms of aggressiveness using deep learning (DL) and compare performance with pathologists. Methods A total of 427 pathological slides of bone tumors were produced and scanned as whole slide imaging (WSI). Tumor area of WSI was annotated by pathologists and cropped into 716,838 image patches of 256 × 256 pixels for training. After six DL models were trained and validated in patch level, performance was evaluated on testing dataset for binary classification (benign vs. non-benign) and ternary classification (benign vs. intermediate vs. malignant) in patch-level and slide-level prediction. The performance of four pathologists with different experiences was compared to the best-performing models. The gradient-weighted class activation mapping was used to visualize patch’s important area. Results VGG-16 and Inception V3 performed better than other models in patch-level binary and ternary classification. For slide-level prediction, VGG-16 and Inception V3 had area under curve of 0.962 and 0.971 for binary classification and Cohen’s kappa score (CKS) of 0.731 and 0.802 for ternary classification. The senior pathologist had CKS of 0.685 comparable to both models (p = 0.688 and p = 0.287) while attending and junior pathologists showed lower CKS than the best model (each p < 0.05). Visualization showed that the DL model depended on pathological features to make predictions. Conclusion DL can effectively classify bone tumors histopathologically in terms of aggressiveness with performance similar to senior pathologists. Our results are promising and would help expedite the future application of DL-assisted histopathological diagnosis for bone tumors.
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Affiliation(s)
- Yuzhang Tao
- Department of Orthopaedics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiao Huang
- Department of Orthopaedics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yiwen Tan
- Department of Pathology, College of Basic Medicine, Chongqing Medical University, Chongqing, China.,Department of Pathology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Hongwei Wang
- Department of Orthopaedics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Weiqian Jiang
- Department of Orthopaedics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yu Chen
- Department of Orthopaedics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Chenglong Wang
- Department of Pathology, Chongqing Hospital of Traditional Chinese Medicine, Chongqing, China
| | - Jing Luo
- Department of Pathology, College of Basic Medicine, Chongqing Medical University, Chongqing, China
| | - Zhi Liu
- Research and Development Department, Chongqing Defang Information Technology Co., Ltd, Chongqing, China
| | - Kangrong Gao
- Research and Development Department, Chongqing Defang Information Technology Co., Ltd, Chongqing, China
| | - Wu Yang
- Department of Orthopaedics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Minkang Guo
- Department of Orthopaedics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Boyu Tang
- Department of Orthopaedics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Aiguo Zhou
- Department of Orthopaedics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Mengli Yao
- Key Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing, China
| | - Tingmei Chen
- Key Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing, China
| | - Youde Cao
- Department of Pathology, College of Basic Medicine, Chongqing Medical University, Chongqing, China
| | - Chengsi Luo
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jian Zhang
- Department of Orthopaedics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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