Journals

LINK: https://doi.org/10.3390/bioengineering11100957 Abstract Coronary artery disease (CAD) presents a significant global health burden, with early and accurate diagnostics crucial for effective management and treatment strategies. This study evaluates the efficacy of human evaluators compared to a Random Forest (RF) machine learning model in predicting CAD risk. It investigates the impact of incorporating human clinical judgments […]

LINK: https://doi.org/10.3390/bdcc8080085 Abstract This study explores a multi-modal machine-learning-based approach to classify solitary pulmonary nodules (SPNs). Non-small cell lung cancer (NSCLC), presenting primarily as SPNs, is the leading cause of cancer-related deaths worldwide. Early detection and appropriate management of SPNs are critical to improving patient outcomes, necessitating efficient diagnostic methodologies. While CT and PET scans

LINK: https://doi.org/10.3390/diseases12060115 Abstract The study investigates the efficiency of integrating Machine Learning (ML) in clinical practice for diagnosing solitary pulmonary nodules’ (SPN) malignancy. Patient data had been recorded in the Department of Nuclear Medicine, University Hospital of Patras, in Greece. A dataset comprising 456 SPN characteristics extracted from CT scans, the SUVmax score from the

LINK: https://doi.org/10.3390/bioengineering11020139 Abstract Fuzzy Cognitive Maps (FCMs) have become an invaluable tool for healthcare providers because they can capture intricate associations among variables and generate precise predictions. FCMs have demonstrated their utility in diverse medical applications, from disease diagnosis to treatment planning and prognosis prediction. Their ability to model complex relationships between symptoms, biomarkers, risk

LINK: https://doi.org/10.3390/make4040040 Abstract Background: Recent advances in Artificial Intelligence (AI) algorithms, and specifically Deep Learning (DL) methods, demonstrate substantial performance in detecting and classifying medical images. Recent clinical studies have reported novel optical technologies which enhance the localization or assess the viability of Parathyroid Glands (PG) during surgery, or preoperatively. These technologies could become complementary

LINK: https://doi.org/10.3390/diagnostics12102333 Abstract Deep learning (DL) is a well-established pipeline for feature extraction in medical and nonmedical imaging tasks, such as object detection, segmentation, and classification. However, DL faces the issue of explainability, which prohibits reliable utilisation in everyday clinical practice. This study evaluates DL methods for their efficiency in revealing and suggesting potential image

LINK: https://www.mdpi.com/2076-3417/13/15/8839 Abstract Greece is among the European Union members topping the list of deaths related to coronary artery disease. Myocardial Perfusion Imaging (MPI) with Single-Photon Emission Computed Tomography (SPECT) is a non-invasive test used to detect abnormalities in CAD screening. The study proposes an explainable deep learning (DL) method for characterising MPI SPECT Polar

LINK: https://www.mdpi.com/2076-3417/13/21/11953 Abstract Myocardial Perfusion Imaging (MPI) has played a central role in the non-invasive identification of patients with Coronary Artery Disease (CAD). Clinical factors, such as recurrent diseases, predisposing factors, and diagnostic tests, also play a vital role. However, none of these factors offer a straightforward and reliable indication, making the diagnosis of CAD

LINK: TBA Abstract In recent times, CAD prediction and diagnosis have been the subject of many Medical Decision Support Systems (MDSS) that make use of Machine Learning (ML) and Deep Learning (DL) algorithms. The common ground of the vast majority of such applications is that they function as black boxes. They reach a conclusion/diagnosis using

LINK: https://www.mdpi.com/2076-3417/12/15/7592 Abstract Background: This study targets the development of an explainable deep learning methodology for the automatic classification of coronary artery disease, utilizing SPECT MPI images. Deep learning is currently judged as non-transparent due to the model’s complex non-linear structure, and thus, it is considered a «black box», making it hard to gain a