Artificial Neural Networks in Cardiovascular Diseases and its Potential for Clinical Application in Molecular Imaging

2020 ◽  
Vol 13 ◽  
Author(s):  
Riccardo Laudicella ◽  
Albert Comelli ◽  
Alessandro Stefano ◽  
Monika Szostek ◽  
Ludovica Crocè ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Cardiovascular Diseases ◽  
Molecular Imaging ◽  
Nuclear Cardiology ◽  
Patient Care ◽  
Clinical Medicine ◽  
Imaging Techniques ◽  
Technical Aspect ◽  
Prognostic Models ◽  
Nuclear Medicine Imaging

Background:: In medical imaging, Artificial Intelligence is described as the ability of a system to properly interpret and learn from external data, acquiring knowledge to achieve specific goals and tasks through flexible adaptation. The number of possible applications of Artificial Intelligence is huge also in clinical medicine and in cardiovascular diseases. Objective: To describe for the first time in literature, the main results of articles about Artificial Intelligence potential for clinical applications in molecular imaging techniques, and to describe its advancements in cardiovascular diseases assessed with nuclear medicine imaging modalities. Methods: A comprehensive search strategy was used based on SCOPUS and PubMed databases. From all studies published in English, we selected the most relevant articles that evaluated the technological insights of AI in nuclear cardiology applications. Results: Artificial Intelligence may improve the patient care on many different fields, from the semi-automatization of the medical work, through the technical aspect of image preparation, interpretation, the calculation of additional factors based on data obtained during scanning, to the prognostic prediction and risk-group selection. Conclusion: Myocardial implementation of Artificial Intelligence algorithms in nuclear cardiology can improve and facilitate the diagnostic and predictive process, and global patient care. Building large databases containing clinical and image data is a first but essential step to create and train automated diagnostic/prognostic models able to help the clinicians to make unbiased and faster decisions for precision healthcare.

Methods and clinical applications in nuclear cardiology: a position statement

2002 ◽  
Vol 41 (01) ◽  
pp. 3-13 ◽  
Author(s):  
M. Schäfers
Keyword(s):  
Nuclear Cardiology ◽  
Clinical Medicine ◽  
Imaging Techniques ◽  
Cost Savings ◽  
Cost Benefit ◽  
Position Statement ◽  
Electron Beam Tomography ◽  
Partial Functions ◽  
Cost Benefit Ratio ◽  
Medicine Today

SummaryNuclear cardiological procedures have paved the way for non-invasive diagnostics of various partial functions of the heart. Many of these functions cannot be visualised for diagnosis by any other method (e. g. innervation). These techniques supplement morphological diagnosis with regard to treatment planning and monitoring. Furthermore, they possess considerable prognostic relevance, an increasingly important issue in clinical medicine today, not least in view of the cost-benefit ratio.Our current understanding shows that effective, targeted nuclear cardiology diagnosis – in particular for high-risk patients – can contribute toward cost savings while improving the quality of diagnostic and therapeutic measures.In the future, nuclear cardiology will have to withstand mounting competition from other imaging techniques (magnetic resonance imaging, electron beam tomography, multislice computed tomography). The continuing development of these methods increasingly enables measurement of functional aspects of the heart. Nuclear radiology methods will probably develop in the direction of molecular imaging.

scholarly journals Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT

2021 ◽  
Author(s):  
Riemer H. J. A. Slart ◽  
Michelle C. Williams ◽  
Luis Eduardo Juarez-Orozco ◽  
Christoph Rischpler ◽  
Marc R. Dweck ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computed Tomography ◽  
Clinical Practice ◽  
Nuclear Cardiology ◽  
European Association ◽  
Imaging Techniques ◽  
Cardiovascular Imaging ◽  
Position Paper ◽  
Daily Clinical Practice

AbstractIn daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.

scholarly journals Radiolabeling of Nucleic Acid Aptamers for Highly Sensitive Disease-Specific Molecular Imaging

2018 ◽  
Vol 11 (4) ◽  
pp. 106 ◽  
Author(s):  
Leila Hassanzadeh ◽  
Suxiang Chen ◽  
Rakesh Veedu
Keyword(s):  
Molecular Imaging ◽  
Single Photon ◽  
Imaging Techniques ◽  
Photon Emission ◽  
Three Dimensional ◽  
Nuclear Imaging ◽  
Spect Imaging ◽  
Emission Computed Tomography ◽  
Nuclear Medicine Imaging ◽  
Highly Sensitive

Aptamers are short single-stranded DNA or RNA oligonucleotide ligand molecules with a unique three-dimensional shape, capable of binding to a defined molecular target with high affinity and specificity. Since their discovery, aptamers have been developed for various applications, including molecular imaging, particularly nuclear imaging that holds the highest potential for the clinical translation of aptamer-based molecular imaging probes. Their easy laboratory production without any batch-to-batch variations, their high stability, their small size with no immunogenicity and toxicity, and their flexibility to incorporate various functionalities without compromising the target binding affinity and specificity make aptamers an attractive class of targeted-imaging agents. Aptamer technology has been utilized in nuclear medicine imaging techniques, such as single photon emission computed tomography (SPECT) and positron emission tomography (PET), as highly sensitive and accurate biomedical imaging modalities towards clinical diagnostic applications. However, for aptamer-targeted PET and SPECT imaging, conjugation of appropriate radionuclides to aptamers is crucial. This review summarizes various strategies to link the radionuclides to chemically modified aptamers to accomplish aptamer-targeted PET and SPECT imaging.

scholarly journals Multimodality Molecular Imaging of Stem Cells Therapy for Stroke

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Fangfang Chao ◽  
Yehua Shen ◽  
Hong Zhang ◽  
Mei Tian
Keyword(s):  
Stem Cells ◽  
Molecular Imaging ◽  
Multimodality Imaging ◽  
Imaging Techniques ◽  
Potential Method ◽  
Nuclear Medicine Imaging ◽  
Therapeutic Benefits ◽  
Stem Cells Therapy ◽  
Magnetic Resonance Imaging Mri

Stem cells have been proposed as a promising therapy for treating stroke. While several studies have demonstrated the therapeutic benefits of stem cells, the exact mechanism remains elusive. Molecular imaging provides the possibility of the visual representation of biological processes at the cellular and molecular level. In order to facilitate research efforts to understand the stem cells therapeutic mechanisms, we need to further develop means of monitoring these cells noninvasively, longitudinally and repeatedly. Because of tissue depth and the blood-brain barrier (BBB), in vivo imaging of stem cells therapy for stroke has unique challenges. In this review, we describe existing methods of tracking transplanted stem cells in vivo, including magnetic resonance imaging (MRI), nuclear medicine imaging, and optical imaging (OI). Each of the imaging techniques has advantages and drawbacks. Finally, we describe multimodality imaging strategies as a more comprehensive and potential method to monitor transplanted stem cells for stroke.

scholarly journals Accessing Artificial Intelligence for Clinical Decision-Making

2021 ◽  
Vol 3 ◽  
Author(s):  
Chris Giordano ◽  
Meghan Brennan ◽  
Basma Mohamed ◽  
Parisa Rashidi ◽  
François Modave ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Patient Care ◽  
Clinical Decision Making ◽  
Clinical Medicine ◽  
Clinical Decision ◽  
Disruptive Innovation ◽  
Decision Makers ◽  
Data Sets ◽  
Sequential Decision

Advancements in computing and data from the near universal acceptance and implementation of electronic health records has been formative for the growth of personalized, automated, and immediate patient care models that were not previously possible. Artificial intelligence (AI) and its subfields of machine learning, reinforcement learning, and deep learning are well-suited to deal with such data. The authors in this paper review current applications of AI in clinical medicine and discuss the most likely future contributions that AI will provide to the healthcare industry. For instance, in response to the need to risk stratify patients, appropriately cultivated and curated data can assist decision-makers in stratifying preoperative patients into risk categories, as well as categorizing the severity of ailments and health for non-operative patients admitted to hospitals. Previous overt, traditional vital signs and laboratory values that are used to signal alarms for an acutely decompensating patient may be replaced by continuously monitoring and updating AI tools that can pick up early imperceptible patterns predicting subtle health deterioration. Furthermore, AI may help overcome challenges with multiple outcome optimization limitations or sequential decision-making protocols that limit individualized patient care. Despite these tremendously helpful advancements, the data sets that AI models train on and develop have the potential for misapplication and thereby create concerns for application bias. Subsequently, the mechanisms governing this disruptive innovation must be understood by clinical decision-makers to prevent unnecessary harm. This need will force physicians to change their educational infrastructure to facilitate understanding AI platforms, modeling, and limitations to best acclimate practice in the age of AI. By performing a thorough narrative review, this paper examines these specific AI applications, limitations, and requisites while reviewing a few examples of major data sets that are being cultivated and curated in the US.

ROS-responsive nanomedicine: Towards targeting the breast tumor microenvironment

2020 ◽  
Vol 28 ◽  
Author(s):  
RamaRao Malla ◽  
Mohammad Amjad Kamal
Keyword(s):  
Reactive Oxygen Species ◽  
Gene Therapy ◽  
Drug Resistance ◽  
Molecular Imaging ◽  
Tumor Microenvironment ◽  
Immune Cells ◽  
Breast Tumor ◽  
Imaging Techniques ◽  
Oxygen Species ◽  
Massive Accumulation

: The breast tumor microenvironment (TME) promotes drug resistance through an elaborated interaction of TME components mediated by reactive oxygen species (ROS). Despite a massive accumulation of data concerning the targeting the ROS, but little is known about the ROS-responsive nanomedicine for targeting breast TME. This review submits the ROS landscape in breast TME, including ROS biology, ROS mediated carcinogenesis, reprogramming of stromal and immune cells of TME. We also discussed ROS-based precision strategies for imaging TME, including molecular imaging techniques with advanced probes, multiplexed methods, and multi-omic profiling strategies. ROS-responsive nanomedicine also describes various therapies, such as chemo-dynamic, photodynamic, photothermal, sono-dynamic, immune, and gene therapy for BC. We expound ROS-responsive primary delivery systems for chemotherapeutics, phytochemicals, and immunotherapeutics. This review also presents recent updates on nano-theranostics for simultaneous diagnosis and treatment of BCs. We assume that review on this advancing field will be beneficial to the development of ROS-based nanotheranostics for BC.

Top 100 most-cited articles on medical imaging applications of artificial intelligence: a bibliometric analysis (Preprint)

2020 ◽  
Author(s):  
Ying Liu ◽  
Ziyan Yu ◽  
Shuolan Jing ◽  
Honghu Jiang ◽  
Chunxia Wang
Keyword(s):  
Artificial Intelligence ◽  
Medical Imaging ◽  
Bibliometric Analysis ◽  
Clinical Medicine ◽  
Medical Physics ◽  
Medical Image Analysis ◽  
The United States ◽  
Independent Learning ◽  
Learning Ability ◽  
Core Technology

BACKGROUND Artificial intelligence (AI) has penetrated into almost every aspect of our lives and is rapidly changing our way of life. Recently, the new generation of AI taking machine learning and particularly deep convolutional neural network theories as the core technology, has stronger learning ability and independent learning evolution ability, combined with a large amount of learning data, breaks through the bottleneck limit of model accuracy, and makes the model efficient use. OBJECTIVE To identify the 100 most cited papers in artificial intelligence in medical imaging, we performed a comprehensive bibliometric analysis basing on the literature search on Web of Science Core Collection (WoSCC). METHODS The 100 top-cited articles published in “AI, Medical imaging” journals were identified using the Science Citation Index Database. The articles were further reviewed, and basic information was collected, including the number of citations, journals, authors, publication year, and field of study. RESULTS The highly cited articles in AI were cited between 72 and 1,554 times. The majority of them were published in three major journals: IEEE Transactions on Medical Imaging, Medical Image Analysis and Medical Physics. The publication year ranged from 2002 to 2019, with 66% published in a three-year period (2016 to 2018). Publications from the United States (56%) were the most heavily cited, followed by those from China (15%) and Netherlands (10%). Radboud University Nijmegen from Netherlands, Harvard Medical School in USA, and The Chinese University of Hong Kong in China produced the highest number of publications (n=6). Computer science (42%), clinical medicine (35%), and engineering (8%) were the most common fields of study. CONCLUSIONS Citation analysis in the field of artificial intelligence in medical imaging reveals interesting information about the topics and trends negotiated by researchers and elucidates which characteristics are required for a paper to attain a “classic” status. Clinical science articles published in highimpact specialized journals are most likely to be cited in the field of artificial intelligence in medical imaging.

scholarly journals SARS-CoV-2 diagnosis using medical imaging techniques and artificial intelligence: A review

2021 ◽  
Vol 76 ◽  
pp. 6-14
Author(s):  
Narjes Benameur ◽  
Ramzi Mahmoudi ◽  
Soraya Zaid ◽  
Younes Arous ◽  
Badii Hmida ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Medical Imaging ◽  
Imaging Techniques
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