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Almansouri NE, Awe M, Rajavelu S, Jahnavi K, Shastry R, Hasan A, Hasan H, Lakkimsetti M, AlAbbasi RK, Gutiérrez BC, Haider A. Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review. Cureus 2024; 16:e55869. [PMID: 38595869 PMCID: PMC11002715 DOI: 10.7759/cureus.55869] [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: 03/09/2024] [Indexed: 04/11/2024] Open
Abstract
Cardiovascular diseases (CVDs) are significant health issues that result in high death rates globally. Early detection of cardiovascular events may lower the occurrence of acute myocardial infarction and reduce death rates in people with CVDs. Traditional data analysis is inadequate for managing multidimensional data related to the risk prediction of CVDs, heart attacks, medical image interpretations, therapeutic decision-making, and disease prognosis due to the complex pathological mechanisms and multiple factors involved. Artificial intelligence (AI) is a technology that utilizes advanced computer algorithms to extract information from large databases, and it has been integrated into the medical industry. AI methods have shown the ability to speed up the advancement of diagnosing and treating CVDs such as heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease, and more. In clinical settings, AI has shown usefulness in diagnosing cardiovascular illness, improving the efficiency of supporting tools, stratifying and categorizing diseases, and predicting outcomes. Advanced AI algorithms have been intricately designed to analyze intricate relationships within extensive healthcare data, enabling them to tackle more intricate jobs compared to conventional approaches.
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Affiliation(s)
| | - Mishael Awe
- Internal Medicine, Crimea State Medical University named after S.I Georgievsky, Simferopol, UKR
| | - Selvambigay Rajavelu
- Internal Medicine, Sri Ramachandra Institute of Higher Education and Research, Chennai, IND
| | - Kudapa Jahnavi
- Internal Medicine, Pondicherry Institute of Medical Sciences, Puducherry, IND
| | - Rohan Shastry
- Internal Medicine, Vydehi Institute of Medical Sciences and Research Center, Bengaluru, IND
| | - Ali Hasan
- Internal Medicine, University of Illinois at Chicago, Chicago, USA
| | - Hadi Hasan
- Internal Medicine, University of Illinois, Chicago, USA
| | | | | | - Brian Criollo Gutiérrez
- Health Sciences, Instituto Colombiano de Estudios Superiores de Incolda (ICESI) University, Cali, COL
| | - Ali Haider
- Allied Health Sciences, The University of Lahore, Gujrat, PAK
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2
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Amin S, Baron SJ, Galper BZ. Aortic valve replacement today: Outcomes, costs, and opportunities for improvement. CARDIOVASCULAR REVASCULARIZATION MEDICINE 2024:S1553-8389(24)00025-3. [PMID: 38388246 DOI: 10.1016/j.carrev.2024.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 01/19/2024] [Accepted: 02/07/2024] [Indexed: 02/24/2024]
Abstract
The introduction of transcatheter aortic valve replacement (TAVR) just two decades ago has transformed the treatment of severe symptomatic aortic stenosis. TAVR has not only extended the option of aortic valve replacement to patients deemed ineligible for surgery, it has also demonstrated similar or better short- and intermediate-term clinical outcomes compared with surgical aortic valve replacement (SAVR) in patients at all levels of surgical risk. These benefits have been achieved with similar or lower costs compared with SAVR, at least in the first 1-2 years for intermediate- and low-risk patients. Longer-term data will further inform clinical and shared decision-making. SUMMARY FOR ANNOTATED TABLE OF CONTENTS: In just over two decades, transcatheter aortic valve replacement has emerged as a frontline approach for appropriately selected patients with severe aortic stenosis. A growing body of evidence documents similar or better clinical outcomes and cost-effectiveness for transcatheter compared with surgical aortic valve replacement. Whether the mode is transcatheter or surgical, aortic valve replacement remains underutilized in patients with clear indications for intervention.
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Affiliation(s)
- Sameer Amin
- L.A. Care Health Plan, 1055 W. 7th St, 10th Floor, Los Angeles, CA 90017, United States
| | - Suzanne J Baron
- Interventional Cardiovascular Research, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, United States; Outcomes Research, Baim Institute for Clinical Research, 930 W. Commonwealth Ave., Boston, MA 02215, United States
| | - Benjamin Z Galper
- Structural Heart Disease Program, Mid-Atlantic Permanente Medical Group, 8008 Westpark Dr., McLean, VA 22102, United States; Cardiac Catheterization Laboratory, Virginia Hospital Center, 1701 N. George Mason Dr., Arlington, VA 22205, United States.
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Iqbal J, Cortés Jaimes DC, Makineni P, Subramani S, Hemaida S, Thugu TR, Butt AN, Sikto JT, Kaur P, Lak MA, Augustine M, Shahzad R, Arain M. Reimagining Healthcare: Unleashing the Power of Artificial Intelligence in Medicine. Cureus 2023; 15:e44658. [PMID: 37799217 PMCID: PMC10549955 DOI: 10.7759/cureus.44658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/04/2023] [Indexed: 10/07/2023] Open
Abstract
Artificial intelligence (AI) has opened new medical avenues and revolutionized diagnostic and therapeutic practices, allowing healthcare providers to overcome significant challenges associated with cost, disease management, accessibility, and treatment optimization. Prominent AI technologies such as machine learning (ML) and deep learning (DL) have immensely influenced diagnostics, patient monitoring, novel pharmaceutical discoveries, drug development, and telemedicine. Significant innovations and improvements in disease identification and early intervention have been made using AI-generated algorithms for clinical decision support systems and disease prediction models. AI has remarkably impacted clinical drug trials by amplifying research into drug efficacy, adverse events, and candidate molecular design. AI's precision and analysis regarding patients' genetic, environmental, and lifestyle factors have led to individualized treatment strategies. During the COVID-19 pandemic, AI-assisted telemedicine set a precedent for remote healthcare delivery and patient follow-up. Moreover, AI-generated applications and wearable devices have allowed ambulatory monitoring of vital signs. However, apart from being immensely transformative, AI's contribution to healthcare is subject to ethical and regulatory concerns. AI-backed data protection and algorithm transparency should be strictly adherent to ethical principles. Vigorous governance frameworks should be in place before incorporating AI in mental health interventions through AI-operated chatbots, medical education enhancements, and virtual reality-based training. The role of AI in medical decision-making has certain limitations, necessitating the importance of hands-on experience. Therefore, reaching an optimal balance between AI's capabilities and ethical considerations to ensure impartial and neutral performance in healthcare applications is crucial. This narrative review focuses on AI's impact on healthcare and the importance of ethical and balanced incorporation to make use of its full potential.
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Affiliation(s)
| | - Diana Carolina Cortés Jaimes
- Epidemiology, Universidad Autónoma de Bucaramanga, Bucaramanga, COL
- Medicine, Pontificia Universidad Javeriana, Bogotá, COL
| | - Pallavi Makineni
- Medicine, All India Institute of Medical Sciences, Bhubaneswar, Bhubaneswar, IND
| | - Sachin Subramani
- Medicine and Surgery, Employees' State Insurance Corporation (ESIC) Medical College, Gulbarga, IND
| | - Sarah Hemaida
- Internal Medicine, Istanbul Okan University, Istanbul, TUR
| | - Thanmai Reddy Thugu
- Internal Medicine, Sri Padmavathi Medical College for Women, Sri Venkateswara Institute of Medical Sciences (SVIMS), Tirupati, IND
| | - Amna Naveed Butt
- Medicine/Internal Medicine, Allama Iqbal Medical College, Lahore, PAK
| | | | - Pareena Kaur
- Medicine, Punjab Institute of Medical Sciences, Jalandhar, IND
| | | | | | - Roheen Shahzad
- Medicine, Combined Military Hospital (CMH) Lahore Medical College and Institute of Dentistry, Lahore, PAK
| | - Mustafa Arain
- Internal Medicine, Civil Hospital Karachi, Karachi, PAK
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Kodeboina M, Piayda K, Jenniskens I, Vyas P, Chen S, Pesigan RJ, Ferko N, Patel BP, Dobrin A, Habib J, Franke J. Challenges and Burdens in the Coronary Artery Disease Care Pathway for Patients Undergoing Percutaneous Coronary Intervention: A Contemporary Narrative Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:ijerph20095633. [PMID: 37174152 PMCID: PMC10177939 DOI: 10.3390/ijerph20095633] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 03/24/2023] [Accepted: 04/20/2023] [Indexed: 05/15/2023]
Abstract
Clinical and economic burdens exist within the coronary artery disease (CAD) care pathway despite advances in diagnosis and treatment and the increasing utilization of percutaneous coronary intervention (PCI). However, research presenting a comprehensive assessment of the challenges across this pathway is scarce. This contemporary review identifies relevant studies related to inefficiencies in the diagnosis, treatment, and management of CAD, including clinician, patient, and economic burdens. Studies demonstrating the benefits of integration and automation within the catheterization laboratory and across the CAD care pathway were also included. Most studies were published in the last 5-10 years and focused on North America and Europe. The review demonstrated multiple potentially avoidable inefficiencies, with a focus on access, appropriate use, conduct, and follow-up related to PCI. Inefficiencies included misdiagnosis, delays in emergency care, suboptimal testing, longer procedure times, risk of recurrent cardiac events, incomplete treatment, and challenges accessing and adhering to post-acute care. Across the CAD pathway, this review revealed that high clinician burnout, complex technologies, radiation, and contrast media exposure, amongst others, negatively impact workflow and patient care. Potential solutions include greater integration and interoperability between technologies and systems, improved standardization, and increased automation to reduce burdens in CAD and improve patient outcomes.
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Affiliation(s)
- Monika Kodeboina
- Cardiovascular Center Aalst, OLV Clinic, 9300 Aalst, Belgium
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80138 Naples, Italy
- Clinic for Internal Medicine and Cardiology, Marien Hospital, 52066 Aachen, Germany
| | - Kerstin Piayda
- Cardiovascular Center Frankfurt, 60389 Frankfurt, Germany
- Department of Cardiology and Vascular Medicine, Medical Faculty, Justus-Liebig-University Giessen, 35392 Giessen, Germany
| | | | | | | | | | | | | | | | | | - Jennifer Franke
- Cardiovascular Center Frankfurt, 60389 Frankfurt, Germany
- Philips Chief Medical Office, 22335 Hamburg, Germany
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Vodanović M, Subašić M, Milošević D, Savić Pavičin I. Artificial Intelligence in Medicine and Dentistry. Acta Stomatol Croat 2023; 57:70-84. [PMID: 37288152 PMCID: PMC10243707 DOI: 10.15644/asc57/1/8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 03/01/2023] [Indexed: 09/14/2023] Open
Abstract
INTRODUCTION Artificial intelligence has been applied in various fields throughout history, but its integration into daily life is more recent. The first applications of AI were primarily in academia and government research institutions, but as technology has advanced, AI has also been applied in industry, commerce, medicine and dentistry. OBJECTIVE Considering that the possibilities of applying artificial intelligence are developing rapidly and that this field is one of the areas with the greatest increase in the number of newly published articles, the aim of this paper was to provide an overview of the literature and to give an insight into the possibilities of applying artificial intelligence in medicine and dentistry. In addition, the aim was to discuss its advantages and disadvantages. CONCLUSION The possibilities of applying artificial intelligence to medicine and dentistry are just being discovered. Artificial intelligence will greatly contribute to developments in medicine and dentistry, as it is a tool that enables development and progress, especially in terms of personalized healthcare that will lead to much better treatment outcomes.
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Affiliation(s)
- Marin Vodanović
- Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Croatia
- University Hospital Centre Zagreb, Croatia
| | - Marko Subašić
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - Denis Milošević
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - Ivana Savić Pavičin
- Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Croatia
- University Hospital Centre Zagreb, Croatia
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Ghanayim T, Lupu L, Naveh S, Bachner-Hinenzon N, Adler D, Adawi S, Banai S, Shiran A. Artificial Intelligence-Based Stethoscope for the Diagnosis of Aortic Stenosis. Am J Med 2022; 135:1124-1133. [PMID: 35640698 DOI: 10.1016/j.amjmed.2022.04.032] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 04/10/2022] [Accepted: 04/30/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND The diagnostic accuracy of the stethoscope is limited and highly dependent on clinical expertise. Our purpose was to develop an electronic stethoscope, based on artificial intelligence (AI) and infrasound, for the diagnosis of aortic stenosis (AS). METHODS We used an electronic stethoscope (VoqX; Sanolla, Nesher, Israel) with subsonic capabilities and acoustic range of 3-2000 Hz. The study had 2 stages. In the first stage, using the VoqX, we recorded heart sounds from 100 patients referred for echocardiography (derivation group), 50 with moderate or severe AS and 50 without valvular disease. An AI-based supervised learning model was applied to the auscultation data from the first 100 patients used for training, to construct a diagnostic algorithm that was then tested on a validation group (50 other patients, 25 with AS and 25 without AS). In the second stage, conducted at a different medical center, we tested the device on 106 additional patients referred for echocardiography, which included patients with other valvular diseases. RESULTS Using data collected at the aortic and pulmonic auscultation points from the derivation group, the AI-based algorithm identified moderate or severe AS with 86% sensitivity and 100% specificity. When applied to the validation group, the sensitivity was 84% and specificity 92%; and in the additional testing group, 90% and 84%, respectively. The sensitivity was 55% for mild, 76% for moderate, and 93% for severe AS. CONCLUSION Our initial findings show that an AI-based stethoscope with infrasound capabilities can accurately diagnose AS. AI-based electronic auscultation is a promising new tool for automatic screening and diagnosis of valvular heart disease.
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Affiliation(s)
- Tamer Ghanayim
- Department of Cardiology, Lady Davis Carmel Medical Center, Haifa, Israel
| | - Lior Lupu
- Department of Cardiology, Tel Aviv Medical Center, affiliated to the Sackler Faculty of Medicine, Tel Aviv University, Israel
| | - Sivan Naveh
- Department of Cardiology, Tel Aviv Medical Center, affiliated to the Sackler Faculty of Medicine, Tel Aviv University, Israel
| | | | | | - Salim Adawi
- Department of Cardiology, Lady Davis Carmel Medical Center, Haifa, Israel; The Ruth and Bruce Rappaport Faculty of Medicine, Technion, Israel Institute of Technology, Haifa
| | - Shmuel Banai
- Department of Cardiology, Tel Aviv Medical Center, affiliated to the Sackler Faculty of Medicine, Tel Aviv University, Israel
| | - Avinoam Shiran
- Department of Cardiology, Lady Davis Carmel Medical Center, Haifa, Israel; The Ruth and Bruce Rappaport Faculty of Medicine, Technion, Israel Institute of Technology, Haifa.
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7
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Garvick S, Gillette C, Gao H, Bates N, Waynick J, Crandall S. Can cardiac auscultation accuracy be improved with an additional app-based learning tool? CLINICAL TEACHER 2022; 19:112-120. [PMID: 35137534 PMCID: PMC9303325 DOI: 10.1111/tct.13462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 12/22/2021] [Accepted: 01/11/2022] [Indexed: 12/01/2022]
Abstract
Introduction Many institutions use simulation ‘events’ to instruct cardiac auscultation. Research shows that these ‘one and done’ events limit repetition, are costly and do not incorporate learning science techniques, such as spaced learning and retrieval practice. The Littmann Learning™ mobile app, which has unlimited access to a large library of real patient heart sounds, is a cost‐effective tool that we considered could be leveraged by educators to provide this training. Methods This was a quasi‐experimental pre‐ and post‐design consisting of an intervention group (PA22) and a non‐equivalent comparator group (PA21). The intervention group used a novel mobile app cardiac auscultation curriculum (MACAC), while the comparator group received standard didactic instruction. One‐way analyses of variance were used to analyse the data. Results A total of 174 PA students participated in the study. There was a significant (p < 0.001) difference in knowledge and auscultation scores between those who did and did not complete the MACAC. PA22 didactic year knowledge scores were 4.11 and 2.96 points higher than PA21 didactic and clinical year knowledge scores (p < 0.001, d = 1.61 and p < 0.001, d = 1.32), respectively. On average, PA22 didactic year auscultation scores were 0.83 points higher than PA21 clinical year scores (p < 0.001, d = 0.6). Conclusion Results indicate that students in their didactic year achieved proficiency in clinically identifying heart sounds, despite not having access to a mannequin simulator and not having an opportunity to identify these sounds bedside. Overall, a MACAC may be an effective method to teach cardiac auscultation to medical learners.
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Affiliation(s)
- Sarah Garvick
- PA Studies, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Chris Gillette
- PA Studies, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Hong Gao
- PA Studies, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Nathan Bates
- PA Studies, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Joshua Waynick
- PA Studies, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Sonia Crandall
- PA Studies, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
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Radiomics in Cardiovascular Disease Imaging: from Pixels to the Heart of the Problem. CURRENT CARDIOVASCULAR IMAGING REPORTS 2022. [DOI: 10.1007/s12410-022-09563-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Abstract
Purpose of Review
This review of the literature aims to present potential applications of radiomics in cardiovascular radiology and, in particular, in cardiac imaging.
Recent Findings
Radiomics and machine learning represent a technological innovation which may be used to extract and analyze quantitative features from medical images. They aid in detecting hidden pattern in medical data, possibly leading to new insights in pathophysiology of different medical conditions. In the recent literature, radiomics and machine learning have been investigated for numerous potential applications in cardiovascular imaging. They have been proposed to improve image acquisition and reconstruction, for anatomical structure automated segmentation or automated characterization of cardiologic diseases.
Summary
The number of applications for radiomics and machine learning is continuing to rise, even though methodological and implementation issues still limit their use in daily practice. In the long term, they may have a positive impact in patient management.
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Xie Y, Lu L, Gao F, He SJ, Zhao HJ, Fang Y, Yang JM, An Y, Ye ZW, Dong Z. Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare. Curr Med Sci 2021; 41:1123-1133. [PMID: 34950987 PMCID: PMC8702375 DOI: 10.1007/s11596-021-2485-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 12/03/2021] [Indexed: 12/19/2022]
Abstract
Chronic diseases are a growing concern worldwide, with nearly 25% of adults suffering from one or more chronic health conditions, thus placing a heavy burden on individuals, families, and healthcare systems. With the advent of the "Smart Healthcare" era, a series of cutting-edge technologies has brought new experiences to the management of chronic diseases. Among them, smart wearable technology not only helps people pursue a healthier lifestyle but also provides a continuous flow of healthcare data for disease diagnosis and treatment by actively recording physiological parameters and tracking the metabolic state. However, how to organize and analyze the data to achieve the ultimate goal of improving chronic disease management, in terms of quality of life, patient outcomes, and privacy protection, is an urgent issue that needs to be addressed. Artificial intelligence (AI) can provide intelligent suggestions by analyzing a patient's physiological data from wearable devices for the diagnosis and treatment of diseases. In addition, blockchain can improve healthcare services by authorizing decentralized data sharing, protecting the privacy of users, providing data empowerment, and ensuring the reliability of data management. Integrating AI, blockchain, and wearable technology could optimize the existing chronic disease management models, with a shift from a hospital-centered model to a patient-centered one. In this paper, we conceptually demonstrate a patient-centric technical framework based on AI, blockchain, and wearable technology and further explore the application of these integrated technologies in chronic disease management. Finally, the shortcomings of this new paradigm and future research directions are also discussed.
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Affiliation(s)
- Yi Xie
- Department of Orthopedic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Lin Lu
- Department of Orthopedic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Fei Gao
- Department of Orthopedic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Shuang-Jiang He
- Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Hui-Juan Zhao
- Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Ying Fang
- Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Jia-Ming Yang
- Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Ying An
- Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Wuhan Fourth Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430032, China
| | - Zhe-Wei Ye
- Department of Orthopedic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Zhe Dong
- School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
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