Objectives: The aim is to review current literature related to the diagnosis, management, and follow-up of suspected and confirmed Covid-19 cases. Key findings: Medical Imaging plays an important auxiliary role in the diagnosis of Covid-19 patients, mainly those most seriously affected. Practice differs widely among different countries, mainly due to the variability of access to resources (viral testing and imaging equipment, specialised staff, protective equipment). It has been now well-documented that chest radiographs should be the first-line imaging tool and chest CT should only be reserved for critically ill patients, or when chest radiograph and clinical presentation may be inconclusive. Conclusion:As radiographers work on the frontline, they should be aware of the potential risks associated with Covid-19 and engage in optimal strategies to reduce these. Their role in vetting, conducting and often reporting the imaging examinations is vital, as well as their contribution in patient safety and care. Medical Imaging should be limited to critically ill patients, and where it may have an impact on the patient management plan. Implications for practice: At the time of publication, this review offers the most up-to-date recommendations for clinical practitioners in radiology departments, including radiographers. Radiography practice has to significantly adjust to these new requirements to support optimal and safe imaging practices for the diagnosis of Covid-19. The adoption of low dose CT, rigorous infection control protocols and optimal use of personal protective equipment may reduce the potential risks of radiation exposure and infection, respectively, within Radiology departments.

Magnetic resonance imaging is widely used for different diagnostic examinations involving autistic patients. The noisy, narrow, isolating magnetic resonance imaging environment and long scan times may not be suitable for autistic individuals, given their communication challenges, sensory sensitivities and often heightened anxiety. This systematic review aims to reveal any reasonable and feasible radiography-based adjustments to facilitate magnetic resonance imaging scanning without the use of sedation or general anaesthesia. Nine electronic databases were systematically searched. Out of 4442 articles screened, 53 were deemed directly relevant; when assessed against eligibility criteria, only 21 were finally included in this systematic review. Customising communication was found to be a key adjustment, as well as scan-based optimisation and environmental adaptations. The importance of distraction techniques and use of technology for familiarisation with the processes was also highlighted. The results of this study can inform recommendations to improve magnetic resonance imaging practice and patient experience, without the use of sedation or anaesthesia, where feasible. They can also inform the basis of dedicated training for magnetic resonance imaging radiographers. Lay abstract Autistic patients often undergo magnetic resonance imaging examinations. Within this environment, it is usual to feel anxious and overwhelmed by noises, lights or other people. The narrow scanners, the loud noises and the long examination time can easily cause panic attacks. This review aims to identify any adaptations for autistic individuals to have a magnetic resonance imaging scan without sedation or anaesthesia. Out of 4442 articles screened, 53 more relevant were evaluated and 21 were finally included in this study. Customising communication, different techniques to improve the environment, using technology for familiarisation and distraction have been used in previous studies. The results of this study can be used to make suggestions on how to improve magnetic resonance imaging practice and the autistic patient experience. They can also be used to create training for the healthcare professionals using the magnetic resonance imaging scanners.

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Background Cardiomegaly is a relatively common incidental finding on chest X-rays; if left untreated, it can result in significant complications. Using Artificial Intelligence for diagnosing cardiomegaly could be beneficial, as this pathology may be underreported, or overlooked, especially in busy or under-staffed settings. Purpose To explore the feasibility of applying four different transfer learning methods to identify the presence of cardiomegaly in chest X-rays and to compare their diagnostic performance using the radiologists’ report as the gold standard. Material and Methods Two thousand chest X-rays were utilized in the current study: 1000 were normal and 1000 had confirmed cardiomegaly. Of these exams, 80% were used for training and 20% as a holdout test dataset. A total of 2048 deep features were extracted using Google’s Inception V3, VGG16, VGG19, and SqueezeNet networks. A logistic regression algorithm optimized in regularization terms was used to classify chest X-rays into those with presence or absence of cardiomegaly. Results Diagnostic accuracy is reported by means of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), with the VGG19 network providing the best values of sensitivity (84%), specificity (83%), PPV (83%), NPV (84%), and overall accuracy (84,5%). The other networks presented sensitivity at 64.1%–82%, specificity at 77.1%–81.1%, PPV at 74%–81.4%, NPV at 68%–82%, and overall accuracy at 71%–81.3%. Conclusion Deep learning using transfer learning methods based on VGG19 network can be used for the automatic detection of cardiomegaly on chest X-ray images. However, further validation and training of each method is required before application to clinical cases.