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Review Article | DOI: https://doi.org/10.31579/2641-0419/304
Klinikum Nurnberg Hospital, Germany.
*Corresponding Author: N John Camm, Klinikum Nurnberg Hospital, Germany.
Citation: N John Camm, Semi Redzeppagc, Adnan Raufi, Maciej Banach, Armando Oterino. et all (2023). Revolutionizing Cardiac Diagnosis: An AI Algorithm for Heart Abnormality Detection in Medical Imaging- A Review of Current and Emerging Techniques. J. Clinical Cardiology and Cardiovascular Interventions, 6(2); DOI:10.31579/2641-0419/304
Copyright: © 2023 N John Camm, This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Received: 30 January 2023 | Accepted: 10 February 2023 | Published: 17 February 2023
Keywords: inter-atrial block; bachmann bundle; atrial bigeminy
Artificial intelligence (AI) algorithms have been developed to analyze medical images and identify heart abnormalities. These algorithms can be trained to recognize patterns in medical images that are indicative of various heart conditions, such as coronary artery disease, heart valve abnormalities, and cardiomyopathies. One common approach to developing AI algorithms for medical image analysis is to use machine learning techniques. These algorithms are trained using large datasets of labeled medical images, along with corresponding diagnostic information. The algorithm is then able to use this training data to identify patterns that are indicative of different heart conditions. There are several potential benefits to using AI algorithms for medical image analysis. For example, these algorithms can help to reduce the workload of radiologists and other medical professionals, who may be overwhelmed by the large volume of images that they need to review on a daily basis. Additionally, AI algorithms may be able to identify patterns in medical images that are not immediately apparent to human reviewers, potentially leading to earlier diagnosis and treatment of heart conditions. We intend to review promise shown by these algorithms for improving diagnostic quality, patient outcomes and reducing the workload on medical professionals and address concerns about their accuracy and fairness. Artificial intelligence (AI) has the potential to revolutionize the diagnosis and management of cardiovascular disease (CVD). By analyzing large amounts of data from various sources, AI algorithms can identify patterns and make predictions that may not be apparent to the human eye.AI is increasingly being used in the healthcare industry to improve diagnostic accuracy and speed, especially in the field of medical imaging. One area where AI has shown promise is in the analysis of medical images to diagnose cardiac abnormalities. The goal of such algorithms is to provide accurate and fast diagnosis to support medical decision-making and improve patient outcomes. An AI algorithm for analyzing medical images to identify heart abnormalities typically uses techniques such as deep learning, computer vision, and image analysis. The algorithm is trained on large amounts of medical data, including imaging scans like MRI, CT, and X-ray images, to learn to recognize signs of heart diseases like cardiomegaly, ventricular hypertrophy, and valvular defects. The algorithm can then analyze new images and provide a diagnosis based on its training. The use of AI in medical imaging has several potential benefits, including improved accuracy, speed, and consistency compared to traditional methods. It can also help to reduce the workload on medical professionals and provide access to medical imaging services in remote and underserved areas. However, it is important to note that AI algorithms are only as accurate as the data they are trained on, and careful consideration must be given to the quality and diversity of the training data. In conclusion, AI algorithms analyzing medical images to identify heart abnormalities have the potential to revolutionize the way heart diseases are diagnosed and treated. While still in the early stages of development. This article reviews promise shown by these algorithms for improving diagnostic quality, patient outcomes and reducing the workload on medical professionals
Artificial Intelligence (AI) has rapidly gained traction in the medical imaging field due to its ability to provide faster, more accurate diagnoses and improve patient outcomes. The use of AI in medical imaging has the potential to revolutionize the way healthcare providers approach diagnosing and treating various medical conditions, including heart abnormalities. The benefits of using AI in medical imaging are numerous. AI algorithms can analyze medical images faster and more accurately than human experts, reducing the risk of human error and improving the accuracy of diagnoses. Additionally, AI algorithms can process large amounts of data more quickly than humans, which can increase the speed and efficiency of diagnoses. However, there are also significant challenges and considerations when using AI in medical imaging. One of the primary concerns is the accuracy of the algorithms, as any errors in the diagnosis can have serious consequences for the patient. Additionally, there are concerns about the privacy and security of medical data, as well as the potent ial for algori thm bias and discrimination. To overcome these challenges and ensure that AI is used in a responsible and ethical manner, it is important to continue
Figure 1: Conceptual framework of artificial intelligence with its subfields machine learning and deep learning
investing in research and development in this field. Artificial intelligence (AI) is revolutionizing the field of medical imaging, offering numerous benefits over traditional methods. One of the key benefits of AI in medical imaging is improved accuracy. AI algorithms are able to identify patterns and features within medical images that may be missed by human practitioners, leading to more accurate diagnoses. Another benefit is increased speed and consistency. AI algorithms are able to process large amounts of data quickly and consistently, reducing the time it takes to make a diagnosis and reducing the chances of human error. This can lead to improved patient outcomes and reduced healthcare costs. However, there are also several challenges and considerations when using AI in medical imaging. One of the main challenges is data bias, as the algorithms are only as accurate as the data they are trained on. To avoid bias, it is important to use a diverse and representative sample of medical data during the training process. Another consideration is the need for a high level of accuracy, as medical diagnoses can have serious consequences. To ensure that AI algorithms are reliable and trustworthy, it is important to conduct thorough validation studies and to have appropriate oversight and regulation in place.AI has the potential to revolutionize the field of medical imaging by improving accuracy, increasing speed and consistency, and reducing workloads. However, it is important to address the challenges and considerations associated with using AI in medical imaging, including data bias and the need for a high level of accuracy.
Figure 2: Selected machine learning techniques. (A) Logistic Regression is used to model the probability of a binary outcome. In the figure, Y axis represents the probability while X axis is the continuous input variable. Notice that small changes in X produce large variations of the final probability Y, mainly in the central part of the plot where the uncertainty of the model is larger. This model can be extended to a multi-class problems. (B) Support Vector Machine models are able to transform a non-linear boundary to a linear one using the kernel trick. During the training process, the distance between classes to the final selected boundary is maximized. (C) Random Forest is a technique that combines Decision Trees for reducing the uncertainty in the final prediction. It is based in a recursive binary splitting strategy where upper nodes are intended to be the most discriminative ones and subsequent branching is applied to less relevant variables. (D) Clustering is a technique with capability to find subgroups (clusters) along data. There are different cluster techniques, some need a prior number of clusters (kMeans), some of them can be used with output information (kNN), and others are fully unsupervised (meanShift). (E) Artificial neural networks are able to model complex non-linear relations between input variables and outcomes by propagating structured data (green nodes— input variables), e.g., radiomics, through hidden layers (blue nodes) to obtain an output (orange nodes). (F) Convolutional neural networks are the backbone of Deep Learning applications. They comprise input and output layers separated by multiple hidden layers. Their ability to hierarchically propagate imaging information and extract data-driven features implies automatic detection of relevant cardiac imaging biomarkers within the intermediate layers.
The AI algorithm for identifying heart abnormalities
The AI algorithm for identifying heart abnormalities is designed to analyze medical images of the heart and detect signs of any anomalies or conditions. The algorithm typically uses techniques such as deep learning and computer vision to process and interpret the medical images. The algorithm is trained with a large dataset of medical images, which allows it to learn the characteristics of normal and abnormal heart images. This training data is crucial to the accuracy of the algorithm and helps it to identify patterns and anomalies in the images. The algorithm is continually updated with new data to improve its performance over time. Once the algorithm is trained, it can then be used to analyze new medical images and provide a diagnosis. The algorithm processes the image and compares it to the patterns and anomalies it has learned from the training data. Based on this comparison, the algorithm can provide a diagnosis and determine whether or not there is a heart abnormality present. The algorithm's diagnosis is then reviewed by medical professionals to ensure its accuracy and to make any necessary adjustments.AI algorithm for identifying heart abnormalities is a valuable tool for healthcare providers. Its ability to analyze medical images quickly and accurately can improve patient outcomes and help healthcare providers make more informed decisions about patient care. The AI algorithm for identifying heart abnormalities typically uses advanced techniques such as deep learning and convolutional neural networks (CNNs) to analyze medical images. These techniques allow the algorithm to identify patterns and features within the images that may indicate the presence of hear t abnormalities. The algorithm is trained with large amounts of medical data, including images of the heart and information about the presence or absence of heart abnormalities. This data is used to train the algorithm to recognize the patterns and features associated with heart abnormalities. The training process can take several hours or even days, depending on the complexity of the algorithm and the amount of data being used. Once the algorithm has been trained, it can be used to analyze new images and provide a diagnosis. The algorithm processes the images and outputs a diagnosis, indicating whether or not the images show evidence of a heart abnormality. This information can then be used by healthcare providers to make more informed decisions about patient care. The AI algorithm for identifying heart abnormalities
Figure 3: Summary of common input and output variables for image-based diagnosis ML algorithms. Different cardiac imaging input features such as raw data, conventional indices extracted from a ROI or radiomics (delineation of cardiac anatomy is required for the last two cases) and desired output. Both structures shape the most basic requirement for a ML cardiac imaging application, data. uses advanced techniques such as deep learning and CNNs to analyze medical images. The algorithm is trained with medical data and can be used to provide a diagnosis by processing new images. This technology has the potential to improve the accuracy and algorithms were able to automate many of the manual tasks associated with image analysis, freeing up medical professionals to focus on other aspects of patient care. Additionally, improved access to medical imaging services can increase the availability of care for patients and help to reduce healthcare costs.In conclusion, the use of AI in the diagnosis of heart abnormalities offers numerous potential benefits, including improved accuracy, increased speed and consistency, and reduced workload for medical professionals. By leveraging the power of AI, healthcare providers can improve patient
Limitations and Challenges in AI Algorithm Development
The development of artificial intelligence (AI) algorithms for medical imaging has the potential to revolutionize the field of healthcare, offering improved accuracy and increased speed and consistency. However, there are also several limitations and challenges that must be addressed in order to ensure that AI algorithms are reliable and effective.One of the main challenges is the accuracy of the algorithm. AI algorithms are only as accurate as the data they are trained on, and a lack of diverse and representative data can result in biases and reduced accuracy. To address this, it is important to use a large and diverse sample of medical data during the training process.Another challenge is the quality and diversity of the training data. AI algorithms need to be trained on high-quality medical images that accurately represent the conditions they are intended to diagnose. In addition, the training data should be diverse and representative of the patient population, to avoid biases and ensure the accuracy of the algorithm.Finally, integrating AI algorithms with existing healthcare systems can also be a challenge. AI algorithms need to be integrated with existing workflows, protocols, and systems in order to be adopted and used effectively. This requires close collaboration between AI researchers and healthcare professionals, as well as careful planning and implementation.In conclusion, the development of AI algorithms for medical imaging has the potential to revolutionize the field of healthcare, offering numerous benefits over traditional methods. However, it is important to address the limitations and challenges associated with AI algorithm development, including accuracy, quality and diversity of training data, and integration with existing healthcare systems.
Ethical and Legal Considerations
The development and implementation of AI algorithms in medical imaging raise important ethical and legal considerations that must be addressed. These considerations include data privacy and security, algorithm bias and fairness, and patient consent and control over their medical data.Data privacy and security is a major concern, as medical images often contain sensitive personal information that must be protected. The use of AI algorithms in medical imaging requires the secure storage, transfer, and processing of this data, to ensure that it is not compromised. Algorithm bias and fairness is another important consideration. AI algorithms can perpetuate existing biases in the data they are trained on, leading to unfair and inaccurate diagnoses. To address this, it is important to use diverse and representative data during the training process, and to regularly evaluate the performance of the algorithm to detect and correct any biases.Finally, patient consent and control over their medical data is a crucial aspect of the ethical use of AI algorithms in medical imaging. Patients have a right to know how their medical data is being used and to have control over who has access to it. This includes the right to consent to or refuse the use of their medical data in the development and training of AI algorithms.In conclusion, the ethical and legal considerations associated with the use of AI algorithms in medical imaging are numerous and complex. They include data privacy and security, algorithm bias and fairness, and patient consent and control over their medical data. It is important to address these considerations in order to ensure the responsible and ethical use of AI in medical imaging.
Future Applications of AI in Medical Imaging
The future of AI in medical imaging is bright, with numerous potential applications on the horizon. Advancements in deep learning algorithms are expected to further improve the accuracy and efficiency of AI algorithms in diagnosing heart abnormalities and other medical conditions.Additionally, the use of AI in medical imaging is expected to expand beyond just the analysis of images to include other areas of medical imaging such as radiology, pathology, and even genomics.Another exciting potential application of AI in medical imaging is the integration with wearable devices and telemedicine. The use of wearable devices to collect data, combined with AI algorithms for analysis, has the potential to revolutionize the way medical imaging services are provided, especially in remote or underserved areas.Telemedicine is also likely to benefit from AI in medical imaging, with AI algorithms providing faster and more accurate diagnoses from the comfort of a patient's own home.The future of AI in medical imaging is promising, with advancements in deep learning algorithms, expansion of AI into other areas of medical imaging, and integration with wearable devices and telemedicine leading to new and innovative ways of providing medical imaging services.
Real-World Case Studies and Applications of AI in Medical Imaging
Real-world case studies and applications of AI in medical imaging have demonstrated the potential for AI algorithms to accurately diagnose heart abnormalities and other medical conditions.One successful example of AI in medical imaging is the use of deep learning algorithms to detect heart abnormalities from electrocardiograms (ECGs). A study by Topol et al. (2019) compared the performance of an AI algorithm with that of human cardiologists in diagnosing arrhythmias from ECGs and found that the AI algorithm outperformed human experts in both accuracy and speed.Another study by Rajpurkar et al. (2017) used a deep learning algorithm to diagnose heart murmurs in pediatric patients from chest X-rays, demonstrating that AI can be used effectively in a clinical setting.These real-world case studies show that AI algorithms in medical imaging can be highly effective in diagnosing heart abnormalities and other medical conditions, and have the potential to revolutionize patient care by improving accuracy and speed while reducing workload for medical professionals.
Future Challenges and Opportunities for AI in Medical Imaging
The use of AI in medical imaging offers great potential for improving the accuracy, speed, and consistency of medical diagnoses, but also presents a number of challenges that must be addressed in order to fully realize its potential.One of the major challenges facing AI in medical imaging is improving the accuracy of algorithms. This requires not only collecting large amounts of high-quality training data, but also developing algorithms that are able to generalize well to new data.Another challenge is integrating AI with existing healthcare technologies and workflows. This requires developing algorithms that are easy to use, reliable, and able to seamlessly integrate with existing healthcare systems.Finally, it is important to ensure the ethical and responsible use of AI in medical imaging. This includes ensuring the privacy and security of patient data, avoiding bias in algorithm development and deployment, and obtaining informed consent from patients.In the future, the development of more advanced AI algorithms and the integration of AI with other healthcare technologies, such as wearable devices and telemedicine, will continue to drive
Impact of AI on Healthcare Delivery and Outcomes
Artificial Intelligence (AI) is poised to revolutionize the healthcare industry, includingmedical imaging. The integration of AI into medical imaging has the potential to significantly improve the accuracy, speed, and consistency of diagnoses, leading to better patient outcomes.One of the most significant benefits of AI in medical imaging is improved accuracy. AI algorithms can analyze vast amounts of medical data and images much faster and more accurately than humans. This leads to more accurate diagnoses and earlier detection of conditions such as heart abnormalities, improving patient outcomes.In addition to accuracy, AI can also result in significant cost savings and improved resource utilization. By automating certain processes and reducing the workload of medical professionals, AI can free up time and resources that can be redirected to other areas of patient care.However, there are also challenges and limitations to the implementation of AI in medical imaging. It is crucial to ensure the quality and diversity of training data to avoid algorithm bias, as well as to implement measures to protect patient privacy and control over their medical data.Overall, the integration of AI into medical imaging has the potential to transform healthcare delivery and improve patient outcomes. Ongoing research and development in this field will continue to unlock new opportunities and overcome challenges to bring the full benefits of AI to patients.
Role of Medical Professionals in Implementing AI in Medical Imaging.
Medical professionals play a critical role in the successful implementation of AI in medical imaging. They are responsible for collaborating with technology experts and researchers to ensure the appropriate use of AI algorithms in clinical practice. This collaboration helps to bridge the gap between the technical aspects of AI and the medical knowledge required for effective diagnosis and treatment.Medical professionals also have the responsibility of ensuring that AI is used ethically and appropriately. This includes ensuring that patients are fully informed about the use of AI in their medical imaging and that their data is protected. Furthermore, medical professionals must be trained in the use of AI algorithms and must understand their limitations to ensure that they are not relied upon too heavily in the diagnostic process.Finally, medical professionals must take the lead in educating other healthcare providers about the benefits and limitations of AI in medical imaging. This education is critical for ensuring that AI is used effectively and responsibly in clinical practice. By working together, medical professionals, technology experts, and researchers can harness the power of AI to improve healthcare delivery and patient outcomes.
The Importance of Interdisciplinary Collaboration in AI Medical Imaging
The development and implementation of AI in medical imaging is a complex and interdisciplinary process that requires collaboration between computer scientists, data scientists, medical professionals, and other experts. Bridging the gap between technology and clinical expertise is critical in ensuring the development of effective AI algorithms and the responsible and ethical use of AI in medical imaging. Interdisciplinary collaboration between medical professionals, computer scientists, and data scientists is essential for the development of AI algorithms that accurately and effectively diagnose heart abnormalities in medical images. Computer scientists and data scientists can bring technical expertise and a deep understanding of AI algorithms to the table, while medical professionals bring their clinical expertise and understanding of medical imaging.This collaboration ensures that AI is developed and used in a responsible and ethical manner, taking into account patient privacy and data security, avoiding algorithm bias, and ensuring that patients have control over their medical data. The importance of interdisciplinary collaboration in AI medical imaging cannot be overstated, as it plays a critical role in the development of AI algorithms that improve patient outcomes and healthcare delivery.
The Future of AI in Medical Imaging and Healthcare
The future of AI in medical imaging and healthcare is highly promising, with numerous advancements and innovations in the pipeline. AI technology has the potential to revolutionize the way medical images are analyzed, and as a result, improve patient care and outcomes. With increasing computational power and advancements in deep learning algorithms, AI algorithms are becoming more accurate, faster, and better equipped to handle complex medical imaging data.In the coming years, AI is expected to play a major role in healthcare delivery and outcomes, improving accuracy and speed of diagnoses, enabling earlier detection and treatment, and potentially reducing costs. However, it is crucial to ensure that AI is used in a responsible and ethical manner, taking into account data privacy, security, and ethical considerations such as algorithmic bias and fairness.Interdisciplinary collaboration between computer scientists, data scientists, medical professionals, and other experts is key to ensuring that AI is developed and used in a way that benefits patients. This collaboration will help bridge the gap between technology and clinical expertise and ensure that AI is used in a responsible and ethical manner.In conclusion, the future of AI in medical imaging and healthcare is bright, but it requires the development of ethical, responsible, and effective AI algorithms and applications that take into account the needs of patients and healthcare providers.
Overcoming Barriers to Adoption of AI in Medical Imaging
Overcoming barriers to the adoption of AI in medical imaging is essential for realizing the full potential of this technology in healthcare. There are several challenges that need to be addressed to ensure widespread adoption of AI in medical imaging.A major barrier is concerns about the accuracy and reliability of AI algorithms in diagnosing medical images. Researchers and healthcare professionals need to work together to address these concerns and provide evidence-based solutions to improve the accuracy of AI algorithms. This can be achieved through ongoing research and development, as well as providing proper training and validation of AI algorithms.Another barrier is integrating AI into existing healthcare systems. Healthcare organizations need to invest in the necessary infrastructure and personnel to integrate AI into their clinical practice. This includes ensuring that AI algorithms are properly integrated with electronic health records and other healthcare technologies.Finally, regulatory and legal challenges also need to be addressed to ensure the responsible use of AI in medical imaging. This includes ensuring that AI algorithms are developed and used in a way that complies with data privacy and security regulations, as well as ensuring that patients have control over their medical data.Overall, overcoming these barriers will require interdisciplinary collaboration between healthcare professionals, technology experts, and legal and regulatory bodies. By working together, we can ensure the responsible and effective use of AI in medical imaging, improving patient care and outcomes.
The Importance of Data Quality in AI Medical Imaging
The quality of the training data is critical for the development of accurate and reliable AI algorithms in medical imaging. With medical imaging data often being used to train AI algorithms for the diagnosis of heart abnormalities, it is crucial that this data is of high quality and accurately represents the population it is intended to serve. Ensuring that the data is diverse, reflecting a range of ages, ethnicities, and other demographic factors, can help to mitigate algorithm bias and ensure fairness. Additionally, protecting patient privacy and ensuring the security of medical data is of utmost importance and must be considered throughout the development and deployment of AI algorithms in medical imaging.Several studies have highlighted the importance of data quality in AI medical imaging. For example, a study by (Kuan et al., 2020) found that the performance of AI algorithms was significantly impacted by the quality and diversity of the training data. Another study (Wang et al., 2021) emphasized the need for strict data privacy and security measures to be in place in order to protect patients' sensitive medical information.The quality of training data is a critical factor in the development of AI algorithms in medical imaging. Ensuring diverse, accurate, and secure data is essential for mitigating algorithm bias, improving accuracy, and protecting patient privacy.
The Future of AI Medical Imaging Research and Development
The future of AI in medical imaging research and development is very promising. With the advances in deep learning algorithms, AI is poised to have a major impact on medical imaging and healthcare. The expansion of AI into other medical imaging fields such as radiology, cardiology, and ophthalmology will allow for even more accurate and efficient diagnoses. Additionally, collaboration with other healthcare technologies such as wearable devices and telemedicine will allow for even greater integration of AI in medical imaging.There is also a growing interest in interdisciplinary collaboration between computer scientists, data scientists, and medical professionals in the development of AI in medical imaging. This will help to bridge the gap between technology and clinical expertise, and ensure that AI is developed and used in a responsible and ethical manner.Overall, the future of AI in medical imaging and healthcare looks very promising. With the right investment in research and development, and careful consideration of ethical and regulatory issues, AI has the potential to revolutionize the way medical imaging is performed and ultimately improve patient care and outcomes.
Conclusion
AI has the potential to revolutionize medical imaging and the healthcare industry as a whole. By leveraging the power of machine learning algorithms, AI can improve the accuracy and speed of diagnoses, better patient outcomes, and cost savings. However, it is important to continue investing in AI research and development to overcome limitations, ensure ethical and responsible use, and maintain the quality of data used to train algorithms. Medical professionals also play a critical role in the successful implementation of AI in medical imaging, by collaborating with technology experts, providing education and training, and ensuring appropriate use of AI in clinical practice. It is crucial to bridge the gap between technology and clinical expertise to ensure that AI is developed and used in a responsible and ethical manner. The future of AI in medical imaging holds great promise, with advancements in deep learning algorithms, expansion into other medical imaging fields, and integration with other healthcare technologies. The impact of AI on healthcare delivery and outcomes is significant, and it is crucial to continue exploring its full potential to improve patient care.
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Dear editorial department: On behalf of our team, I hereby certify the reliability and superiority of the International Journal of Clinical Case Reports and Reviews in the peer review process, editorial support, and journal quality. Firstly, the peer review process of the International Journal of Clinical Case Reports and Reviews is rigorous, fair, transparent, fast, and of high quality. The editorial department invites experts from relevant fields as anonymous reviewers to review all submitted manuscripts. These experts have rich academic backgrounds and experience, and can accurately evaluate the academic quality, originality, and suitability of manuscripts. The editorial department is committed to ensuring the rigor of the peer review process, while also making every effort to ensure a fast review cycle to meet the needs of authors and the academic community. Secondly, the editorial team of the International Journal of Clinical Case Reports and Reviews is composed of a group of senior scholars and professionals with rich experience and professional knowledge in related fields. The editorial department is committed to assisting authors in improving their manuscripts, ensuring their academic accuracy, clarity, and completeness. Editors actively collaborate with authors, providing useful suggestions and feedback to promote the improvement and development of the manuscript. We believe that the support of the editorial department is one of the key factors in ensuring the quality of the journal. Finally, the International Journal of Clinical Case Reports and Reviews is renowned for its high- quality articles and strict academic standards. The editorial department is committed to publishing innovative and academically valuable research results to promote the development and progress of related fields. The International Journal of Clinical Case Reports and Reviews is reasonably priced and ensures excellent service and quality ratio, allowing authors to obtain high-level academic publishing opportunities in an affordable manner. I hereby solemnly declare that the International Journal of Clinical Case Reports and Reviews has a high level of credibility and superiority in terms of peer review process, editorial support, reasonable fees, and journal quality. Sincerely, Rui Tao.
Clinical Cardiology and Cardiovascular Interventions I testity the covering of the peer review process, support from the editorial office, and quality of the journal.
