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.

Updates and Advances: Pediatric Musculoskeletal Infection Imaging Made Easier for Radiologists and Clinicians

2021 ◽  
Vol 25 (01) ◽  
pp. 167-175
Author(s):  
Michael S. Furman ◽  
Ricardo Restrepo ◽  
Supika Kritsaneepaiboon ◽  
Bernard F. Laya ◽  
Domen Plut ◽  
...  
Keyword(s):  
Clinical Practice ◽  
Imaging Techniques ◽  
Daily Clinical Practice ◽  
Infants And Children ◽  
Imaging Evaluation ◽  
Musculoskeletal Infection ◽  
Wide Range ◽  
Life Threatening ◽  
Optimal Patient Management

AbstractInfants and children often present with a wide range of musculoskeletal (MSK) infections in daily clinical practice. This can vary from relatively benign superficial infections such as cellulitis to destructive osseous and articular infections and life-threatening deep soft tissue processes such as necrotizing fasciitis. Imaging evaluation plays an essential role for initial detection and follow-up evaluation of pediatric MSK infections. Therefore, a clear and up-to-date knowledge of imaging manifestations in MSK infections in infants and children is imperative for timely and accurate diagnosis that, in turn, can result in optimal patient management. This article reviews an up-to-date practical imaging techniques, the differences between pediatric and adult MSK infections, the spectrum of pediatric MSK infections, and mimics of pediatric MSK infections encountered in daily clinical practice by radiologists and clinicians.

scholarly journals Neurological update: neuroimaging in dementia

2020 ◽  
Vol 267 (11) ◽  
pp. 3429-3435
Author(s):  
Timothy Rittman
Keyword(s):  
Artificial Intelligence ◽  
Positron Emission Tomography ◽  
Clinical Practice ◽  
Imaging Techniques ◽  
Emission Tomography ◽  
Structural Imaging ◽  
Regulatory Issues ◽  
Positron Emission ◽  
Remarkable Progress

Abstract Neuroimaging for dementia has made remarkable progress in recent years, shedding light on diagnostic subtypes of dementia, predicting prognosis and monitoring pathology. This review covers some updates in the understanding of dementia using structural imaging, positron emission tomography (PET), structural and functional connectivity, and using big data and artificial intelligence. Progress with neuroimaging methods allows neuropathology to be examined in vivo, providing a suite of biomarkers for understanding neurodegeneration and for application in clinical trials. In addition, we highlight quantitative susceptibility imaging as an exciting new technique that may prove to be a sensitive biomarker for a range of neurodegenerative diseases. There are challenges in translating novel imaging techniques to clinical practice, particularly in developing standard methodologies and overcoming regulatory issues. It is likely that clinicians will need to lead the way if these obstacles are to be overcome. Continued efforts applying neuroimaging to understand mechanisms of neurodegeneration and translating them to clinical practice will complete a revolution in neuroimaging.

scholarly journals Cardiovascular Imaging Applications in Clinical Management of Patients Treated with Cardiac Resynchronization Therapy

Hearts ◽  
2020 ◽  
Vol 1 (3) ◽  
pp. 166-180
Author(s):  
Cinzia Valzania ◽  
Fredrik Gadler ◽  
Eva Maret ◽  
Maria J. Eriksson
Keyword(s):  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Nuclear Cardiology ◽  
Cardiac Resynchronization Therapy ◽  
Imaging Techniques ◽  
Cardiovascular Imaging ◽  
Cardiac Resynchronization ◽  
Reverse Remodeling ◽  
Resynchronization Therapy

Cardiovascular imaging techniques, including echocardiography, nuclear cardiology, multi-slice computed tomography, and cardiac magnetic resonance, have wide applications in cardiac resynchronization therapy (CRT). Our aim was to provide an update of cardiovascular imaging applications before, during, and after implantation of a CRT device. Before CRT implantation, cardiovascular imaging techniques may integrate current clinical and electrocardiographic selection criteria in the identification of patients who may most likely benefit from CRT. Assessment of myocardial viability by ultrasound, nuclear cardiology, or cardiac magnetic resonance may guide optimal left ventricular (LV) lead positioning and help to predict LV function improvement by CRT. During implantation, echocardiographic techniques may guide in the identification of the best site of LV pacing. After CRT implantation, cardiovascular imaging plays an important role in the assessment of CRT response, which can be defined according to LV reverse remodeling, function and dyssynchrony indices. Furthermore, imaging techniques may be used for CRT programming optimization during follow-up, especially in patients who turn out to be non-responders. However, in the clinical settings, the use of proposed functional indices for different imaging techniques is still debated, due to their suboptimal feasibility and reproducibility. Moreover, identifying CRT responders before implantation and turning non-responders into responders at follow-up remain challenging issues.

scholarly journals Big data, machine learning and artificial intelligence: a neurologist’s guide

2020 ◽  
pp. practneurol-2020-002688
Author(s):  
Stephen D Auger ◽  
Benjamin M Jacobs ◽  
Ruth Dobson ◽  
Charles R Marshall ◽  
Alastair J Noyce
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Big Data ◽  
Clinical Practice ◽  
Clinical Data ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Basic Principles ◽  
Biological Neural Networks

Modern clinical practice requires the integration and interpretation of ever-expanding volumes of clinical data. There is, therefore, an imperative to develop efficient ways to process and understand these large amounts of data. Neurologists work to understand the function of biological neural networks, but artificial neural networks and other forms of machine learning algorithm are likely to be increasingly encountered in clinical practice. As their use increases, clinicians will need to understand the basic principles and common types of algorithm. We aim to provide a coherent introduction to this jargon-heavy subject and equip neurologists with the tools to understand, critically appraise and apply insights from this burgeoning field.

scholarly journals Prevalence and Imaging Characteristics of Palatine Tonsilloliths Detected by CT in 2,873 Consecutive Patients

2014 ◽  
Vol 2014 ◽  
pp. 1-4 ◽  
Author(s):  
Akira Takahashi ◽  
Chieko Sugawara ◽  
Takaharu Kudoh ◽  
Daisuke Uchida ◽  
Tetsuya Tamatani ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Clinical Practice ◽  
Soft Tissue ◽  
Diagnostic Imaging ◽  
Size Distribution ◽  
Clinical Experience ◽  
Daily Clinical Practice ◽  
Palatine Tonsil ◽  
Imaging Characteristics ◽  
Number Of Patients

Aim.Tonsilloliths are calcified structures that develop in tonsillar crypts. They are commonly detected in daily clinical practice. The prevalence of tonsilloliths was 16 to 24% in previous reports, but it is inconsistent with clinical experience. The aim of this study is to clarify the prevalence, number, and size distribution of tonsilloliths using computed tomography (CT) in a relatively large number of patients.Materials and Methods.We retrospectively reviewed the scans of 2,873 patients referred for CT examinations with regard to tonsilloliths.Results.Palatine tonsilloliths were found in 1,145 out of 2,873 patients (39.9%). The prevalence of tonsilloliths increased with age, and most commonly in patients of ages 50–69. The prevalence in the 30s and younger was statistically lower than in the 40s and older (P< 0.05). The number of tonsilloliths per palatine tonsil ranged from one to 18. The size of the tonsilloliths ranged from 1 to 10 mm. For the patients with multiple CT examinations,　the number of tonsilloliths increased in 51 (3.9%) and decreased in 84 (6.5%) of the tonsils.Conclusions.As palatine tonsilloliths are common conditions, screenings for tonsilloliths during the diagnosis of soft tissue calcifications should be included in routine diagnostic imaging.

scholarly journals A Survey on Artificial Intelligence in Chest Imaging of COVID-19

2020 ◽  
Vol 1 (3) ◽  
pp. 137-146
Author(s):  
Yun Chen ◽  
Gongfa Jiang ◽  
Yue Li ◽  
Yutao Tang ◽  
Yanfang Xu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computed Tomography ◽  
Imaging Techniques ◽  
Chest Imaging ◽  
Imaging Analysis ◽  
X Ray ◽  
The Past ◽  
Analysis Methods ◽  
Improve Accuracy ◽  
Assisted Diagnosis

Abstract The coronavirus disease 2019 (COVID-19) has infected more than 9.3 million people and has caused over 0.47 million deaths worldwide as of June 24, 2020. Chest imaging techniques including computed tomography and X-ray scans are indispensable tools in COVID-19 diagnosis and its management. The strong infectiousness of this disease brings a huge burden for radiologists. In order to overcome the difficulty and improve accuracy of the diagnosis, artificial intelligence (AI)-based imaging analysis methods are explored. This survey focuses on the development of chest imaging analysis methods based on AI for COVID-19 in the past few months. Specially, we first recall imaging analysis methods of two typical viral pneumonias, which can provide a reference for studying the disease on chest images. We further describe the development of AI-assisted diagnosis and assessment for the disease, and find that AI techniques have great advantage in this application.

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