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Fathima AJ, Fasla MMN. A comprehensive review on heart disease prognostication using different artificial intelligence algorithms. Comput Methods Biomech Biomed Engin 2024; 27:1357-1374. [PMID: 38424704 DOI: 10.1080/10255842.2024.2319706] [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: 01/30/2023] [Revised: 02/06/2024] [Accepted: 02/12/2024] [Indexed: 03/02/2024]
Abstract
Prediction of heart diseases on time is significant in order to preserve life. Many conventional methods have taken efforts on earlier prediction but faced with challenges of higher prediction cost, extended time for computation and complexities with larger volume of data which reduced prediction accuracy. In order to overcome such pitfalls, AI (Artificial Intelligence) technology has been evolved in diagnosing heart diseases through deployment of several ML (Machine Learning) and DL (Deep Learning) algorithms. It improves detection by influencing with its capacity of learning from the massive data containing age, obesity, hypertension and other risk factors of patients and extract it accordingly to differentiate on the circumstances. Moreover, storage of larger data with AI greatly assists in analysing the occurrence of the disease from past historical data. Hence, this paper intends to provide a review on different AI based algorithms used in the heart disease prognostication and delivers its benefits through researching on various existing works. It performs comparative analysis and critical assessment as encompassing accuracies and maximum utilization of algorithms focussed by traditional studies in this area. The major findings of the paper emphasized on the evolution and continuous explorations of AI techniques for heart disease prediction and the future researchers aims in determining the dimensions that have attained high and low prediction accuracies on which appropriate research works can be performed. Finally, future research is included to offer new stimulus for further investigation of AI in cardiac disease diagnosis.
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Affiliation(s)
- A Jainul Fathima
- Assistant Professor, IT Francis Xavier Engineering College, Tirunelveli - 627003, India
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Seetharam K, Thyagaturu H, Ferreira GL, Patel A, Patel C, Elahi A, Pachulski R, Shah J, Mir P, Thodimela A, Pala M, Thet Z, Hamirani Y. Broadening Perspectives of Artificial Intelligence in Echocardiography. Cardiol Ther 2024; 13:267-279. [PMID: 38703292 PMCID: PMC11093957 DOI: 10.1007/s40119-024-00368-3] [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: 11/13/2023] [Accepted: 04/11/2024] [Indexed: 05/06/2024] Open
Abstract
Echocardiography frequently serves as the first-line treatment of diagnostic imaging for several pathological entities in cardiology. Artificial intelligence (AI) has been growing substantially in information technology and various commercial industries. Machine learning (ML), a branch of AI, has been shown to expand the capabilities and potential of echocardiography. ML algorithms expand the field of echocardiography by automated assessment of the ejection fraction and left ventricular function, integrating novel approaches such as speckle tracking or tissue Doppler echocardiography or vector flow mapping, improved phenotyping, distinguishing between cardiac conditions, and incorporating information from mobile health and genomics. In this review article, we assess the impact of AI and ML in echocardiography.
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Affiliation(s)
- Karthik Seetharam
- Division of Cardiovascular Disease, West Virgina University, Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA.
- Wyckoff Heights Medical Center, Brooklyn, NY, USA.
| | - Harshith Thyagaturu
- Division of Cardiovascular Disease, West Virgina University, Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA
| | | | - Aditya Patel
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Chinmay Patel
- University of Pittsburg Medical Center, Harrisburg, PA, USA
| | - Asim Elahi
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Roman Pachulski
- St. John's Episcopal Hospital - South Shore, New York, NY, USA
| | - Jilan Shah
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Parvez Mir
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | | | - Manya Pala
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Zeyar Thet
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Yasmin Hamirani
- Robert Woods Johnson University Hospital/Rutgers University, New Brusnwick, NJ, USA
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Magboo VPC, Magboo MSA. SPECT-MPI for Coronary Artery Disease: A Deep Learning Approach. ACTA MEDICA PHILIPPINA 2024; 58:67-75. [PMID: 38812768 PMCID: PMC11132284 DOI: 10.47895/amp.vi0.7582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/31/2024]
Abstract
Background Worldwide, coronary artery disease (CAD) is a leading cause of mortality and morbidity and remains to be a top health priority in many countries. A non-invasive imaging modality for diagnosis of CAD such as single photon emission computed tomography-myocardial perfusion imaging (SPECT-MPI) is usually requested by cardiologists as it displays radiotracer distribution in the heart reflecting myocardial perfusion. The interpretation of SPECT-MPI is done visually by a nuclear medicine physician and is largely dependent on his clinical experience and showing significant inter-observer variability. Objective The aim of the study is to apply a deep learning approach in the classification of SPECT-MPI for perfusion abnormalities using convolutional neural networks (CNN). Methods A publicly available anonymized SPECT-MPI from a machine learning repository (https://www.kaggle.com/selcankaplan/spect-mpi) was used in this study involving 192 patients who underwent stress-test-rest Tc99m MPI. An exploratory approach of CNN hyperparameter selection to search for optimum neural network model was utilized with particular focus on various dropouts (0.2, 0.5, 0.7), batch sizes (8, 16, 32, 64), and number of dense nodes (32, 64, 128, 256). The base CNN model was also compared with the commonly used pre-trained CNNs in medical images such as VGG16, InceptionV3, DenseNet121 and ResNet50. All simulations experiments were performed in Kaggle using TensorFlow 2.6.0., Keras 2.6.0, and Python language 3.7.10. Results The best performing base CNN model with parameters consisting of 0.7 dropout, batch size 8, and 32 dense nodes generated the highest normalized Matthews Correlation Coefficient at 0.909 and obtained 93.75% accuracy, 96.00% sensitivity, 96.00% precision, and 96.00% F1-score. It also obtained higher classification performance as compared to the pre-trained architectures. Conclusions The results suggest that deep learning approaches through the use of CNN models can be deployed by nuclear medicine physicians in their clinical practice to further augment their decision skills in the interpretation of SPECT-MPI tests. These CNN models can also be used as a dependable and valid second opinion that can aid physicians as a decision-support tool as well as serve as teaching or learning materials for the less-experienced physicians particularly those still in their training career. These highlights the clinical utility of deep learning approaches through CNN models in the practice of nuclear cardiology.
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Affiliation(s)
- Vincent Peter C Magboo
- Department of Physical Sciences and Mathematics, College of Arts and Sciences, University of the Philippines Manila
| | - Ma Sheila A Magboo
- Department of Physical Sciences and Mathematics, College of Arts and Sciences, University of the Philippines Manila
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Rudnicka Z, Pręgowska A, Glądys K, Perkins M, Proniewska K. Advancements in artificial intelligence-driven techniques for interventional cardiology. Cardiol J 2024; 31:321-341. [PMID: 38247435 PMCID: PMC11076027 DOI: 10.5603/cj.98650] [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: 12/22/2023] [Revised: 12/31/2023] [Accepted: 12/29/2023] [Indexed: 01/23/2024] Open
Abstract
This paper aims to thoroughly discuss the impact of artificial intelligence (AI) on clinical practice in interventional cardiology (IC) with special recognition of its most recent advancements. Thus, recent years have been exceptionally abundant in advancements in computational tools, including the development of AI. The application of AI development is currently in its early stages, nevertheless new technologies have proven to be a promising concept, particularly considering IC showing great impact on patient safety, risk stratification and outcomes during the whole therapeutic process. The primary goal is to achieve the integration of multiple cardiac imaging modalities, establish online decision support systems and platforms based on augmented and/or virtual realities, and finally to create automatic medical systems, providing electronic health data on patients. In a simplified way, two main areas of AI utilization in IC may be distinguished, namely, virtual and physical. Consequently, numerous studies have provided data regarding AI utilization in terms of automated interpretation and analysis from various cardiac modalities, including electrocardiogram, echocardiography, angiography, cardiac magnetic resonance imaging, and computed tomography as well as data collected during robotic-assisted percutaneous coronary intervention procedures. Thus, this paper aims to thoroughly discuss the impact of AI on clinical practice in IC with special recognition of its most recent advancements.
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Affiliation(s)
- Zofia Rudnicka
- Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
| | - Agnieszka Pręgowska
- Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
| | - Kinga Glądys
- Jagiellonian University Medical College, Krakow, Poland
| | - Mark Perkins
- Collegium Prometricum, the Business School for Healthcare, Sopot, Poland
- Royal Society of Arts, London, United Kingdom
| | - Klaudia Proniewska
- Department of Bioinformatics and Telemedicine, Jagiellonian University Medical College, Krakow, Poland.
- Center for Digital Medicine and Robotics, Jagiellonian University Medical College, Krakow, Poland.
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Gomes RFT, Schmith J, de Figueiredo RM, Freitas SA, Machado GN, Romanini J, Carrard VC. Use of Artificial Intelligence in the Classification of Elementary Oral Lesions from Clinical Images. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:ijerph20053894. [PMID: 36900902 PMCID: PMC10002140 DOI: 10.3390/ijerph20053894] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 02/16/2023] [Accepted: 02/20/2023] [Indexed: 06/01/2023]
Abstract
OBJECTIVES Artificial intelligence has generated a significant impact in the health field. The aim of this study was to perform the training and validation of a convolutional neural network (CNN)-based model to automatically classify six clinical representation categories of oral lesion images. METHOD The CNN model was developed with the objective of automatically classifying the images into six categories of elementary lesions: (1) papule/nodule; (2) macule/spot; (3) vesicle/bullous; (4) erosion; (5) ulcer and (6) plaque. We selected four architectures and using our dataset we decided to test the following architectures: ResNet-50, VGG16, InceptionV3 and Xception. We used the confusion matrix as the main metric for the CNN evaluation and discussion. RESULTS A total of 5069 images of oral mucosa lesions were used. The oral elementary lesions classification reached the best result using an architecture based on InceptionV3. After hyperparameter optimization, we reached more than 71% correct predictions in all six lesion classes. The classification achieved an average accuracy of 95.09% in our dataset. CONCLUSIONS We reported the development of an artificial intelligence model for the automated classification of elementary lesions from oral clinical images, achieving satisfactory performance. Future directions include the study of including trained layers to establish patterns of characteristics that determine benign, potentially malignant and malignant lesions.
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Affiliation(s)
- Rita Fabiane Teixeira Gomes
- Department of Oral Pathology, Faculdade de Odontologia, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre 90035-003, Brazil
| | - Jean Schmith
- Polytechnic School, University of Vale do Rio dos Sinos—UNISINOS, São Leopoldo 93022-750, Brazil
- Technology in Automation and Electronics Laboratory—TECAE Lab, University of Vale do Rio dos Sinos—UNISINOS, São Leopoldo 93022-750, Brazil
| | - Rodrigo Marques de Figueiredo
- Polytechnic School, University of Vale do Rio dos Sinos—UNISINOS, São Leopoldo 93022-750, Brazil
- Technology in Automation and Electronics Laboratory—TECAE Lab, University of Vale do Rio dos Sinos—UNISINOS, São Leopoldo 93022-750, Brazil
| | - Samuel Armbrust Freitas
- Department of Applied Computing, University of Vale do Rio dos Sinos—UNISINOS, São Leopoldo 93022-750, Brazil
| | - Giovanna Nunes Machado
- Polytechnic School, University of Vale do Rio dos Sinos—UNISINOS, São Leopoldo 93022-750, Brazil
| | - Juliana Romanini
- Oral Medicine, Otorhynolaringology Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre 90035-003, Brazil
| | - Vinicius Coelho Carrard
- Department of Oral Pathology, Faculdade de Odontologia, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre 90035-003, Brazil
- Oral Medicine, Otorhynolaringology Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre 90035-003, Brazil
- TelessaudeRS, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre 91501-970, Brazil
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Apostolos A, Gerakaris A, Tsoni E, Pappelis K, Vasilagkos G, Bousoula E, Moulias A, Konstantinou K, Dimitriadis K, Karamasis GV, Aminian A, Toutouzas K, Davlouros P, Tsigkas G. Imaging of Left Main Coronary Artery; Untangling the Gordian Knot. Rev Cardiovasc Med 2023; 24:26. [PMID: 39076882 PMCID: PMC11270402 DOI: 10.31083/j.rcm2401026] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 12/02/2022] [Accepted: 12/02/2022] [Indexed: 07/31/2024] Open
Abstract
Left Main Coronary Artery (LMCA) disease is considered a standout manifestation of coronary artery disease (CAD), because it is accompanied by the highest mortality. Increased mortality is expected, because LMCA is responsible for supplying up to 80% of total blood flow to the left ventricle in a right-dominant coronary system. Due to the significant progress of biomedical technology, the modern drug-eluting stents have remarkably improved the prognosis of patients with LMCA disease treated invasively. In fact, numerous randomized trials provided similar results in one- and five-year survival of patients treated with percutaneous coronary interventions (PCI) -guided with optimal imaging and coronary artery bypass surgery (CABG). However, interventional treatment requires optimal imaging of the LMCA disease, such as intravascular ultrasound (IVUS) and optical coherence tomography (OCT). The aim of this manuscript is to review the main pathophysiological characteristics, to present the imaging techniques of LMCA, and, last, to discuss the future directions in the depiction of LMCA disease.
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Affiliation(s)
- Anastasios Apostolos
- Department of Cardiology, University Hospital of Patras, 26504 Patras, Greece
- First Department of Cardiology, Medical School, National and Kapodistrian University of Athens, Hippokration Hospital, 11527 Athens, Greece
| | - Andreas Gerakaris
- Department of Cardiology, University Hospital of Patras, 26504 Patras, Greece
| | - Evropi Tsoni
- Department of Cardiology, University Hospital of Patras, 26504 Patras, Greece
| | - Konstantinos Pappelis
- Department of Ophthalmology, University Medical Center Groningen, University of Groningen, 9700 Groningen, The Netherlands
| | - Georgios Vasilagkos
- Department of Cardiology, University Hospital of Patras, 26504 Patras, Greece
| | - Elena Bousoula
- Cardiology Department, Tzaneio Hospital, 18536 Pireaus, Greece
| | - Athanasios Moulias
- Department of Cardiology, University Hospital of Patras, 26504 Patras, Greece
| | - Konstantinos Konstantinou
- First Department of Cardiology, Medical School, National and Kapodistrian University of Athens, Hippokration Hospital, 11527 Athens, Greece
| | - Kyriakos Dimitriadis
- First Department of Cardiology, Medical School, National and Kapodistrian University of Athens, Hippokration Hospital, 11527 Athens, Greece
| | - Grigoris V. Karamasis
- Second Department of Cardiology, Medical School, National and Kapodistrian University of Athens, Attikon University Hospital, 12462 Athens, Greece
| | - Adel Aminian
- Department of Cardiology, Centre Hospitalier Universitaire de Charleroi, 6042 Charleroi, Belgium
| | - Konstantinos Toutouzas
- First Department of Cardiology, Medical School, National and Kapodistrian University of Athens, Hippokration Hospital, 11527 Athens, Greece
| | - Periklis Davlouros
- Department of Cardiology, University Hospital of Patras, 26504 Patras, Greece
| | - Grigorios Tsigkas
- Department of Cardiology, University Hospital of Patras, 26504 Patras, Greece
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Grewal HK, Bansal M. Echocardiographic Differentiation of Pericardial Constriction and Left Ventricular Restriction. Curr Cardiol Rep 2022; 24:1599-1610. [PMID: 36040551 DOI: 10.1007/s11886-022-01774-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/12/2022] [Indexed: 01/11/2023]
Abstract
PURPOSE OF REVIEW Overlapping hemodynamics in constrictive pericarditis (CP) and restrictive cardiomyopathy (RCM) often pose difficulties in establishing accurate diagnosis. Echocardiography is the first-line imaging modality used for this purpose, but no single echocardiographic parameter is sufficiently robust for distinguishing between the two conditions. The newer developments may improve the diagnostic accuracy of echocardiography in this setting. RECENT FINDINGS Recent studies have validated multiparametric algorithms, based on conventional echocardiographic parameters, which enable high sensitivity and specificity for distinguishing between CP and RCM. In addition, myocardial deformation analysis using speckle-tracking echocardiography has revealed distinct pattern of abnormalities in the two conditions. CP is characterized by impaired left ventricular apical rotation with relatively preserved longitudinal strain, esp. of ventricular and atrial septum. In contrast, RCM results in global and marked impairment of left ventricular longitudinal strain with initially preserved circumferential mechanics. Combining multiple echocardiographic parameters into step-wise algorithms and incorporation of myocardial deformation analysis help improve the diagnostic accuracy of echocardiography for distinguishing between CP and RCM. The use of machine-learning may allow easy integration of a wide range of echocardiographic and clinical parameters to permit accurate, automated diagnosis, with less dependence on the user expertise.
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Affiliation(s)
- Hardeep Kaur Grewal
- Medanta Heart Institute, Medanta - The Medicity, Gurgaon, Haryana, 122001, India
| | - Manish Bansal
- Medanta Heart Institute, Medanta - The Medicity, Gurgaon, Haryana, 122001, India.
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Abstract
In this digital era, artificial intelligence (AI) is establishing a strong foothold in commercial industry and the field of technology. These effects are trickling into the healthcare industry, especially in the clinical arena of cardiology. Machine learning (ML) algorithms are making substantial progress in various subspecialties of cardiology. This will have a positive impact on patient care and move the field towards precision medicine. In this review article, we explore the progress of ML in cardiovascular imaging, electrophysiology, heart failure, and interventional cardiology.
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Gupta MD, Kunal S, Girish M, Gupta A, Yadav R. Artificial intelligence in Cardiology: the past, present and future. Indian Heart J 2022; 74:265-269. [PMID: 35917970 PMCID: PMC9453051 DOI: 10.1016/j.ihj.2022.07.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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Dai H, Younis A, Kong JD, Puce L, Jabbour G, Yuan H, Bragazzi NL. Big Data in Cardiology: State-of-Art and Future Prospects. Front Cardiovasc Med 2022; 9:844296. [PMID: 35433868 PMCID: PMC9010556 DOI: 10.3389/fcvm.2022.844296] [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: 12/27/2021] [Accepted: 02/24/2022] [Indexed: 11/23/2022] Open
Abstract
Cardiological disorders contribute to a significant portion of the global burden of disease. Cardiology can benefit from Big Data, which are generated and released by different sources and channels, like epidemiological surveys, national registries, electronic clinical records, claims-based databases (epidemiological Big Data), wet-lab, and next-generation sequencing (molecular Big Data), smartphones, smartwatches, and other mobile devices, sensors and wearable technologies, imaging techniques (computational Big Data), non-conventional data streams such as social networks, and web queries (digital Big Data), among others. Big Data is increasingly having a more and more relevant role, being highly ubiquitous and pervasive in contemporary society and paving the way for new, unprecedented perspectives in biomedicine, including cardiology. Big Data can be a real paradigm shift that revolutionizes cardiological practice and clinical research. However, some methodological issues should be properly addressed (like recording and association biases) and some ethical issues should be considered (such as privacy). Therefore, further research in the field is warranted.
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Affiliation(s)
- Haijiang Dai
- Department of Cardiology, The Third Xiangya Hospital, Central South University, Changsha, China
- Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON, Canada
| | - Arwa Younis
- Clinical Cardiovascular Research Center, University of Rochester Medical Center, Rochester, New York, NY, United States
| | - Jude Dzevela Kong
- Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON, Canada
| | - Luca Puce
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Georges Jabbour
- Physical Education Department, College of Education, Qatar University, Doha, Qatar
| | - Hong Yuan
- Department of Cardiology, The Third Xiangya Hospital, Central South University, Changsha, China
- Hong Yuan
| | - Nicola Luigi Bragazzi
- Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON, Canada
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- Postgraduate School of Public Health, Department of Health Sciences, University of Genoa, Genoa, Italy
- Section of Musculoskeletal Disease, Leeds Institute of Molecular Medicine, NIHR Leeds Musculoskeletal Biomedical Research Unit, University of Leeds, Chapel Allerton Hospital, Leeds, United Kingdom
- *Correspondence: Nicola Luigi Bragazzi
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