Artificial intelligence (AI) has the potential to fundamentally alter the way medicine is
practised. AI platforms excel in recognizing complex patterns in medical data and provide a …

The goals of this review paper on deep learning (DL) in medical imaging and radiation
therapy are to (a) summarize what has been achieved to date;(b) identify common and …

The purpose of the EANM Dosimetry Committee is to provide recommendations and
guidance to scientists and clinicians on patient-specific dosimetry. Radiopharmaceuticals …

This review sets out to discuss the foremost applications of artificial intelligence (AI),
particularly deep learning (DL) algorithms, in single-photon emission computed tomography …

Medical imaging is a rich source of invaluable information necessary for clinical judgements.
However, the analysis of those exams is not a trivial assignment. In recent times, the use of …

Kidney cancer is a public health problem that affects thousands of people worldwide.
Accurate kidney tumor segmentation is an important task that helps doctors to reduce the …

Objective The aim of this study was to investigate whether a deep learning object detection
technique can automatically detect and classify radiolucent lesions in the mandible on …

Attenuation correction (AC) is essential for the generation of artifact-free and quantitatively
accurate positron emission tomography (PET) images. PET AC based on computed …

Segmentation of organs and structures, as either targets or organs‐at‐risk, has a significant
influence on the success of radiation therapy. Manual segmentation is a tedious and time …

Calculation of radiation dosimetry in targeted nuclear medicine therapies is traditionally
resource-intensive, requiring multiple posttherapy SPECT acquisitions. An alternative …