The application of artificial intelligence in nuclear cardiology

2022 ◽  
Author(s):  
Yuka Otaki ◽  
Robert J. H. Miller ◽  
Piotr J. Slomka
Keyword(s):  
Artificial Intelligence ◽  
Nuclear Cardiology

Artificial Intelligence in Nuclear Cardiology: Adding Value to Prognostication

2019 ◽  
Vol 12 (5) ◽  
Author(s):  
Karthik Seetharam ◽  
Sirish Shresthra ◽  
James D. Mills ◽  
Partho P. Sengupta
Keyword(s):  
Artificial Intelligence ◽  
Nuclear Cardiology

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.

Deep Learning and Artificial Intelligence in Nuclear Cardiology

2021 ◽  
pp. 741-762
Author(s):  
Erito Marques de Souza-Filho ◽  
Fernando de Amorim Fernandes
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Nuclear Cardiology

scholarly journals Future of Artificial Intelligence and Nuclear Cardiology

2018 ◽  
Vol 4 (1) ◽  
pp. 79-82
Author(s):  
Akihiro Kikuchi ◽  
Takashi Kawakami
Keyword(s):  
Artificial Intelligence ◽  
Nuclear Cardiology

scholarly journals Artificial Intelligence in Nuclear Cardiology

2019 ◽  
Vol 60 (8) ◽  
pp. 1042-1043 ◽  
Author(s):  
Javier Gomez ◽  
Rami Doukky
Keyword(s):  
Artificial Intelligence ◽  
Nuclear Cardiology

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.

Use of Artificial Intelligence Including Decision Support Systems in Cardiac Imaging

2015 ◽  
Author(s):  
Ernest V. Garcia
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Nuclear Cardiology ◽  
Decision Support Systems ◽  
Cardiac Imaging ◽  
Support Systems ◽  
Imaging Modality ◽  
Clinical Care ◽  
Clinical Decision Support Systems ◽  
High Level

Artificial intelligence methods, including clinical decision support systems will continue to evolve with, and adapt to, nuclear cardiology and the changing needs of physicians in specific, and to nuclear medicine technology and the health care system in general. The high level of automation already achieved in myocardial perfusion imaging is unmatched by any other cardiac imaging modality, and continues to be its major strength. In addition, strong statistical evaluations of the accuracy and validity of the various techniques have been made possible simply because of the large amount of objectivity and standardization in the automated processes. These strengths when applied to decision support systems that are affordable and easily accessible should allow most nuclear cardiology physicians to perform at a high level of expertise when interpreting imaging studies to demonstrate the value of nuclear cardiology in patient management, and most importantly, to maintain the highest quality clinical care.

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