Tuberculosis (TB) is a bacterial infection with Mycobacterium tuberculosis; it is a public health problem worldwide and one of the leading causes of mortality. Since December 2019, the COVID-19 pandemic has created unprecedented health challenges and disrupted the TB health services, especially in high-burden countries with ever-increasing prevalence. Extrapulmonary and even pulmonary TB are an important cause of nonspecific clinical and radiological manifestations and can masquerade as any benign or malignant medical case, thus causing disastrous conditions and diagnostic dilemmas. Clinical manifestations and routine laboratory tests have limitations in directing physicians to diagnose TB. Medical-imaging examinations play an essential role in detecting tissue abnormalities and early suspecting diagnosis of TB in different organs. Radiologists and physicians should be familiar with and aware of the radiological manifestations of TB to contribute to the early suspicion and diagnosis of TB. The purpose of this article is to illustrate the common radiologic patterns of pulmonary and extrapulmonary TB. This article will be beneficial for radiologists, medical students, chest physicians, and infectious-disease doctors who are interested in the diagnosis of TB.

To understand and report the perceived impact of the COVID-19 pandemic on radiology residents in Saudi Arabia with respect to their education, clinical activities, and personal well-being. The survey questionnaire was designed by a team of experts based on a review of the literature and was distributed electronically through the Saudi Commission for Health Specialties to residents registered in all radiology residency training programs in Saudi Arabia during the academic year 2019 to 2020. Categorical variables were presented as counts and percentage. Numerical variables were presented as mean and standard deviation if normally distributed. Chi-square testing was used to compare categorical variables with the perceived impact of the COVID-19 pandemic. Spearman correlation was used to correlate numerical variables at the level of significance p-value < 0.05. A total of 109 residents completed the online survey during the study period, with a response rate of 32.2% (109/337). The mean age was 27.3 years (standard deviation, 1.86). The majority of respondents (71.5%, 78/109) reported either a severe or moderate negative impact on educational activities. Also, the majority (73.4%, 80/109) reported either a minimal or moderate negative impact on clinical activities. Residents training in the western province perceived a statistically higher negative impact on educational activities compared to their peers in other regions (p = 0.01). Residents in their second year of residency training perceived a statistically higher negative impact on their participation in clinical activities (p = 0.014). Less than half of the respondents (45.9%, 50/109) reported that they were redeployed to work in another department. The majority (80%, 40/50) reported a negative impact on their well-being. The majority of radiology residents in Saudi Arabia reported a negative impact of the COVID-19 pandemic on their education, clinical activities, and personal well-being. Our study also identified and explored some of the innovative solutions and strategies implemented by the training programs and the SCFHS to mitigate the negative effects on trainees.

Objectives: The brain and spinal cord, constituting the central nervous system (CNS), could be impacted by an inflammatory disease known as multiple sclerosis (MS). The convolutional neural networks (CNN), a machine learning method, can detect lesions early by learning patterns on brain magnetic resonance image (MRI). We performed this study to investigate the diagnostic performance of CNN based MRI in the identification, classification, and segmentation of MS lesions. Methods: PubMed, Web of Science, Embase, the Cochrane Library, CINAHL, and Google Scholar were used to retrieve papers reporting the use of CNN based MRI in MS diagnosis. The accuracy, the specificity, the sensitivity, and the Dice Similarity Coefficient (DSC) were evaluated in this study. Results: In total, 2174 studies were identified and only 15 articles met the inclusion criteria. The 2D-3D CNN presented a high accuracy (98.81, 95% CI: 98.50–99.13), sensitivity (98.76, 95% CI: 98.42–99.10), and specificity (98.67, 95% CI: 98.22–99.12) in the identification of MS lesions. Regarding classification, the overall accuracy rate was significantly high (91.38, 95% CI: 83.23–99.54). A DSC rate of 63.78 (95% CI: 58.29–69.27) showed that 2D-3D CNN-based MRI performed highly in the segmentation of MS lesions. Sensitivity analysis showed that the results are consistent, indicating that this study is robust. Conclusion: This metanalysis revealed that 2D-3D CNN based MRI is an automated system that has high diagnostic performance and can promptly and effectively predict the disease.

Brain tumors are among the most common malignant tumors of the central nervous system, with high mortality and recurrence rates. Radiomics extracts quantitative features from medical images, converting them into predictive biomarkers for tumor diagnosis, prognosis, and survival analysis. Despite the invasiveness and heterogeneity of brain tumors, even with timely treatment, the overall survival time or survival probability is not necessarily favorable. Therefore, accurate prediction of brain tumor grade and survival outcomes is important for personalized treatment. In this study, we propose a radiomic model for the non-invasive prediction of brain tumor grade and patient survival outcomes. We used four magnetic resonance imaging (MRI) sequences from 159 patients with glioma. Four classifiers were employed based on whether feature selection was applied. The features were derived from regions of interest identified and corrected either manually or automatically. The extreme gradient boosting (XGB) model with 3860 radiomic features achieved the highest classification performance, with an AUC of 98.20%, in distinguishing LGG from GBM images using manually corrected labels. Similarly, the Random Forest (RF) model exhibits the best discrimination between short-term and long-term survival groups with a p-value < 0.0003, a hazard ratio (HR) value of 3.24, and a 95% confidence interval (CI) of 1.63–4.43 based on the ICC features. The experimental findings demonstrate strong classification accuracy and effectively predict survival outcomes in glioma patients.