Journals

DOI: 10.1097/MNM.0000000000001634 Abstract In the last few years, deep learning has made a breakthrough and established its position in machine learning classification problems in medical image analysis. Deep learning has recently displayed remarkable applicability in a range of different medical applications, as well as in nuclear cardiology. This paper implements a literature review protocol and reports the latest advances in artificial intelligence (AI)-based classification in SPECT myocardial […]

LINK: https://link.springer.com/article/10.1186/s40658-022-00522-7 Abstract Deep learning (DL) has a growing popularity and is a well-established method of artificial intelligence for data processing, especially for images and videos. Its applications in nuclear medicine are broad and include, among others, disease classification, image reconstruction, and image de-noising. Positron emission tomography (PET) and single-photon emission computerized tomography (SPECT) are

LINK: https://www.nature.com/articles/s41598-023-33500-9 Abstract The main goal driving this work is to develop computer-aided classification models relying on clinical data to identify coronary artery disease (CAD) instances with high accuracy while incorporating the expert’s opinion as input, making it a “man-in-the-loop” approach. CAD is traditionally diagnosed in a definite manner by Invasive Coronary Angiography (ICA). A

LINK: https://link.springer.com/article/10.1007/s12149-022-01762-4 Abstract Objective The exploration and the implementation of a deep learning method using a state-of-the-art convolutional neural network for the classification of polar maps represent myocardial perfusion for the detection of coronary artery disease. Subjects and methods In the proposed research, the dataset includes stress and rest polar maps in attenuation-corrected (AC) and