To appraise the literature for studies involving the use of elastography to diagnose thyroid nodule pathology. Two independent reviewers performed a systematic review of the English medical literature for studies involving elastography diagnosing thyroid nodule pathology. Data gleaned from this process was used in a meta-analysis to summarise the results. Thirty-eight studies were used in the meta-analysis totalling 5,942 thyroid nodules examined with elastography. The pooled results were sensitivity = 87.0 % (95 % confidence intervals (CI) = 86.2-87.9 %), specificity = 80.6 % (CI = 79.5-81.6 %), positive predictive value (PPV) = 48.9 % (CI = 47.6-50.2 %), negative predictive value (NPV) = 96.7 % (CI = 96.2-97.1 %), diagnostic accuracy = 81.7 % (CI = 80.7-82.7 %). Subgroup analysis of the data is also presented. Elastography has its limitations in the diagnosis of thyroid nodules; however, its high NPV is increasingly being used as an important investigation and may allow a reduction in the number of hemi-thyroidectomies with benign pathology. Subgroup analysis suggests that elastography techniques where compressive force is performed in a non-user-dependant method results in improved final results.

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Chest X-rays (CXRs) are the first-line investigation in patients presenting to emergency departments (EDs) with dyspnoea and are a valuable adjunct to clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to facilitate rapid triage of CXRs for further patient testing and/or isolation. In this work we develop an AI algorithm, CovIx, to differentiate normal, abnormal, non-COVID-19 pneumonia, and COVID-19 CXRs using a multicentre cohort of 293,143 CXRs. The algorithm is prospectively validated in 3289 CXRs acquired from patients presenting to ED with symptoms of COVID-19 across four sites in NHS Greater Glasgow and Clyde. CovIx achieves area under receiver operating characteristic curve for COVID-19 of 0.86, with sensitivity and F1-score up to 0.83 and 0.71 respectively, and performs on-par with four board-certified radiologists. AI-based algorithms can identify CXRs with COVID-19 associated pneumonia, as well as distinguish non-COVID pneumonias in symptomatic patients presenting to ED. Pre-trained models and inference scripts are freely available at https://github.com/beringresearch/bravecx-covid.

In this case report, we present a case of a hitherto undescribed "pseudoembryo" appearance in a fluid-filled endometrial cavity in ectopic pregnancy. Knowledge of this sonographic finding is clinically important, since the presence of a "pseudoembryo" could lead to the misidentification of a pseudogestational sac as an intrauterine pregnancy in the setting of ectopic pregnancy. This paper discuss reviews the pseudogestational sac and imaging findings which differentiate it from a true intrauterine gestation.