In this review, we focused on the applicability of artificial intelligence (AI) for opportunistic abdominal aortic aneurysm (AAA) detection in computed tomography (CT). We used the academic search system PubMed as the primary source for the literature search and Google Scholar as a supplementary source of evidence. We searched through 2 February 2022. All studies on automated AAA detection or segmentation in noncontrast abdominal CT were included. For bias assessment, we developed and used an adapted version of the QUADAS-2 checklist. We included eight studies with 355 cases, of which 273 (77%) contained AAA. The highest risk of bias and level of applicability concerns were observed for the “patient selection” domain, due to the 100% pathology rate in the majority (75%) of the studies. The mean sensitivity value was 95% (95% CI 100–87%), the mean specificity value was 96.6% (95% CI 100–75.7%), and the mean accuracy value was 95.2% (95% CI 100–54.5%). Half of the included studies performed diagnostic accuracy estimation, with only one study having data on all diagnostic accuracy metrics. Therefore, we conducted a narrative synthesis. Our findings indicate high study heterogeneity, requiring further research with balanced noncontrast CT datasets and adherence to reporting standards in order to validate the high sensitivity value obtained.

Ultrasound Doppler twinkling artifact is analyzed. It usually appears as a frequent color alteration in the region of hyperechoic objects. Its noiselike spectrum can also be seen in spectral Doppler...

Phantoms are used to validate diagnostic imaging methods or improve the skills of medical professionals. For instance, they allow conducting an unlimited number of imaging studies during medical training, assessing image quality, optimizing radiation dose, and testing novel techniques and equipment. Researchers in breast imaging use anthropomorphic models to validate, assess, and optimize new methods for diagnosing breast diseases. Such models also facilitate control over the quality of diagnostic systems, help optimize clinical protocols, and improve image reconstruction algorithms. Realistic simulation of the breast tissue is essential to address the challenges of advancing X-ray mammary gland studies. The review aimed to describe phantoms currently available for diagnostic imaging and the way they were fabricated. In this literature review, PubMed, eLIBRARY, and Google Scholar databases were screened for relevant articles. Thus, 72 articles and 13 conference papers were included. The study two major types of breast phantoms: computational and physical. Specifically, computational phantoms are classified into subgroups depending on the data they use. These include mathematical models, tissue samples, and medical images of the breast. The classification of the physical phantoms is based on their manufacturing process: casting silicone-like substances, 3D printing with resins and plastics, or printing on paper using X-ray contrast ink. Computational phantoms are generally advantageous with respect to versatility, efficiency, precision, and safety and allow the generation of large amounts of virtual data. Physical phantoms provide the most realistic diagnostic images without the need for a patient and allow performing an unlimited number of radiological studies.