Evaluating the impact of dose monitoring software alerts on radiation dose reduction in computed tomography: A systematic review.

Introduction: Radiation Dose Monitoring Software (DMS) tools have been developed to monitor doses and alert computed tomography (CT) users of high radiation exposure. However, the causal factors for alerts and the impact of DMS in dose optimisation are poorly understood.

Aim: This review aims to identify high-dose CT examinations triggering alerts and their determinants, and to assess if the alerts from DMS help to reduce CT dose levels.

Misdiagnosed for 14 Years: Adenosine Deaminase 2 (ADA2) Deficiency in a Teen Mimicking Polyarteritis Nodosa.

The deficiency of adenosine deaminase 2 (DADA2) is an autosomal recessive disorder caused by loss of function mutations in the ADA2 gene (previously the CECR1 gene) on chromosome 22q11. The clinical spectrum of the disease is remarkably broad, and its presentations mimic features of polyarteritis nodosa, such as livedoid rash, hematological abnormalities (e.g.

Biophysical analyses of various interval training sets at critical swimming velocity.

Background: Critical swimming velocity (CV) is widely used as an index for setting intensity in endurance training (IT). This study aimed to examine the effects of varying repetitive swimming distances on physiological and stroke parameters during IT at CV.

Methods: Eleven male national-level collegiate swimmers participated in all-out 200 and 400 m front crawl swims to determine the CV.

Automated quality control analysis for American College of Radiology (ACR) digital mammography (DM) phantom images.

Purpose: To develop and validate an automated software analysis method for mammography image quality assessment of the American College of Radiology (ACR) digital mammography (DM) phantom images.

Methods: Twenty-seven DICOM images were acquired using Fuji mammography systems. All images were evaluated by three expert medical physicists using the Royal Australian and New Zealand College of Radiologists (RANZCR) mammography quality control guideline.

AI for interpreting screening mammograms: implications for missed cancer in double reading practices and challenging-to-locate lesions.

Although the value of adding AI as a surrogate second reader in various scenarios has been investigated, it is unknown whether implementing an AI tool within double reading practice would capture additional subtle cancers missed by both radiologists who independently assessed the mammograms. This paper assesses the effectiveness of two state-of-the-art Artificial Intelligence (AI) models in detecting retrospectively-identified missed cancers within a screening program employing double reading practices. The study also explores the agreement between AI and radiologists in locating the lesions, considering various levels of concordance among the radiologists in locating the lesions.