Challenges for the effective management of interstitial lung diseases (ILDs) include
difficulties with the early detection of disease, accurate prognostication with baseline data …

Abstract Machine Learning (ML) applications are making a considerable impact on
healthcare. ML is a subtype of Artificial Intelligence (AI) technology that aims to improve the …

The use of deep learning in medical imaging has increased rapidly over the past few years,
finding applications throughout the entire radiology pipeline, from improved scanner …

Although the utility of the machine learning (ML) techniques is established in occupational
accident domain using reactive data, its exploration in predicting injury severity using both …

As the use of machine learning in the Internet-of-Medical Things (IoMT) settings increases,
so do the data privacy concerns. Therefore, in this article, we propose an efficient privacy …

In the last decade, a large number of clinical trials have been deployed using Cardiac
Magnetic Resonance (CMR) to evaluate cardioprotective strategies aiming at reducing the …

Spine segmentation poses significant challenges due to the complex anatomical structure of
the spine and the variability in imaging modalities, which often results in unclear boundaries …

Image classification has rapidly gained interest in the medical field with the ability to assist
practitioners to diagnose a variety of conditions. Due to the critical nature of the application …

Placenta previa (PP) and Placenta Accreta Spectrum (PAS) are obstetric pathologies whose
early detection is fundamental for an appropriate patient management. In this paper …

Technology is currently a crucial benchmark in any application area. In general, society is
immersed in the era of digitalization; therefore, incorporating digital technology in different …