Artificial intelligence reading digital mammogram: enhancing detection and differentiation of suspicious microcalcifications.

Objectives: To investigate the impact of artificial intelligence (AI) on enhancing the sensitivity of digital mammograms in the detection and specification of grouped microcalcifications.

Methods: The study is a retrospective analysis of grouped microcalcifications for 447 patients. Grouped microcalcifications detected were correlated with AI, which was applied to the initial mammograms.

Validation of artificial intelligence contrast mammography in diagnosis of breast cancer: Relationship to histopathological results.

Introduction: Contrast-enhanced mammography (CEM) is used for characterization of breast lesions with increased diagnostic accuracy compared to digital mammography (DM). Artificial intelligence (AI) approaches are emerging with accuracies equal to an average radiologist. However, most studies trained deep learning (DL) models on DM images and there is a paucity in literature for discovering the application of AI using CEM.

Workplace violence at emergency departments, Ain Shams University Hospitals, Cairo, Egypt.

Background: The present study aimed to determine the prevalence and forms of workplace violence (WPV) at the emergency departments (EDs) of Ain Shams University Hospitals (ASUH), Cairo and identify risk factors for WPV.

Methods: A cross-sectional study was conducted at the EDs of ASUH comprising attending physicians and nurses using a self-administered structured questionnaire. Interviews were conducted with patients and relatives attending these departments to explore attitudes toward WPV against healthcare workers.

Categorized contrast enhanced mammography dataset for diagnostic and artificial intelligence research.

Contrast-enhanced spectral mammography (CESM) is a relatively recent imaging modality with increased diagnostic accuracy compared to digital mammography (DM). New deep learning (DL) models were developed that have accuracies equal to that of an average radiologist. However, most studies trained the DL models on DM images as no datasets exist for CESM images.

Production of photochromic nanocomposite film via spray-coating of rare-earth strontium aluminate for anti-counterfeit applications.

New photochromic film was developed toward the preparation of anti-counterfeiting documents utilizing inorganic/organic nanocomposite enclosing a photoluminescent inorganic pigment and a polyacrylic binder polymer. To generate a translucent film from pigment/polyacrylic nanocomposite, the phosphorescent strontium aluminum oxide pigment should be well-dispersed in the solution of the polyacrylic-based binder without agglomeration. The photochromic nanocomposite was applied efficiently onto commercial cellulose paper documents utilizing the effective and economical spray-coating technology followed with thermofixation.