Infections contracted during healthcare delivery in a hospital or ambulatory setting are collectively referred to as healthcare-associated infections (HAIs). Healthcare workers and patients alike are vulnerable to serious problems as a result of the risk of HAIs. In the healthcare system, HAIs are considered among the most common and serious health problems. However, the occurrence of HAIs differs between different types of clinical departments within the hospital. Recently, the risk of HAIs has been increasing in radiology departments globally due to the central role of radiology in guiding clinical decisions for the diagnosis, treatment, and monitoring of different diseases from almost all medical specialties. The radiology department is particularly vulnerable to HAIs because it serves as a transit hub for infected patients, non-infected patients, and healthcare workers. Furthermore, as the number of patients referred to radiology and the length of patient contact time has increased, thanks to modern imaging techniques such as computed tomography and magnetic resonance imaging, the risk of HAIs has also increased significantly. With the increasing use of interventional radiological procedures, patients and healthcare workers face a potentially greater risk of contracting HAIs due to the invasive nature of such procedures. Although not exhaustive, we attempted through a literature search to provide a general overview of infection prevention and control practices, address HAIs in the radiology departments, and highlight the challenges and measures taken to control infection transmission in the radiology departments.

Endoscopic sinus surgery (ESS) is now the most often utilized surgical procedure for treating chronic sinonasal disorders. Therefore, anatomical knowledge of its variations is required to avoid serious postoperative complications. Thus, careful preoperative examination for patients with a paranasal sinus CT scan is necessary. Our study aims to evaluate Keros types and their incidence by evaluating the olfactory fossa (OF) depth according to the Keros classification on paranasal sinus CT scans in the Qassim region. A cross-sectional study was conducted between January 2018 and January 2021 on 148 patients with an average age of 32.59 ± 6.1 who had a non-enhanced paranasal sinus CT scan evaluated by a consultant radiologist using the PACS (picture archiving and communication system) software. Statistical analysis was performed using the statistical software package SPSS version 25 (IBM Corp., Armonk, NY). The chi-square test was used to analyze the relationship between findings and patient characteristics. Also, a p-value of < 0.05 was kept in mind to indicate statistical significance. The average depth of the right olfactory fossa (OF) was 5.1 mm with a standard deviation of 1.756 while it was 5.28 on the left side with a standard deviation of 1.66. According to the Keros classification, out of a total of 296 OF, type 1 was found in 84 (28.4%), type 2 in 188 (63.5%), and type 3 in 24 (8.1%). Consequently, the majority of cases were of type 2. Also, we found that type 2 was the most common on both sides in males, whereas, in females, type 2 was the most common on the left side and type 1 on the right side. The study of the Keros classification is significantly important to evaluate the anatomy of the anterior skull base and give the surgeon knowledge about the depth of the olfactory fossa. Thus, a preoperative CT scan of the paranasal sinus is critical to ensure that the surgical approach is properly planned and possible surgical complications related to the anatomy of this area can be prevented. Our study showed that Keros type II is the most common, followed by type I and then type III.

Digital mammography (DM) is the cornerstone of breast cancer detection. Digital breast tomosynthesis (DBT) is an advanced imaging technique used for diagnosing and screening breast lesions, particularly in dense breasts. This study aimed to evaluate the impact of combining DBT with DM on the BI-RADS categorization of equivocal breast lesions. We prospectively evaluated 148 females with equivocal BI-RADS breast lesions (BI-RADS 0, 3, and 4) with DM. All patients underwent DBT. Two experienced radiologists analyzed the lesions. They then assigned a BI-RADS category for each lesion according to the BI-RADS 2013 lexicon, using DM, DBT, and integrated DM and DBT. We compared the results based on major radiological characteristics, BI-RADS classification, and diagnostic accuracy, using the histopathological examination of the lesions as a reference standard. The total number of lesions was 178 on DBT and 159 on DM. Nineteen lesions were discovered using DBT and were missed by DM. The final diagnoses of 178 lesions were malignant (41.6%) and benign (58.4%). Compared to DM, DBT produced 34.8% downgrading and 32% upgrading of breast lesions. Compared with DM, DBT decreased the number of BI-RADS 4 and 3. All the upgraded BI-RADS 4 lesions were confirmed to be malignant. The combination of DM and DBT improves the diagnostic accuracy of BI-RADS for evaluating and characterizing mammographic equivocal breast lesions and allows for proper BI-RADS categorization.

There has been a notable increase in rhino-orbito-cerebral mucormycosis (ROCM) post-coronavirus disease 2019 (COVID-19), which is an invasive fungal infection with a fatal outcome. Magnetic resonance imaging (MRI) is a valuable tool for early diagnosis of ROCM and assists in the proper management of these cases. This study aimed to describe the characteristic MRI findings of ROCM in post-COVID-19 patients to help in the early diagnosis and management of these patients. This retrospective descriptive study was conducted at a single hospital and included 52 patients with COVID-19 and a histopathologically proven ROCM infection who were referred for an MRI of the paranasal sinuses (PNS) due to sino-orbital manifestations. Two radiologists reviewed all the MR images in consensus. The diagnosis was confirmed by histopathological examination. The maxillary sinus was the most commonly affected PNS (96.2%). In most patients (57.7%), multiple sinuses were involved with the black turbinate sign on postcontrast images. Extrasinus was evident in 43 patients with orbital involvement. The pterygopalatine fossa was involved in four patients. Three patients had cavernous sinus extension, two had pachymeningeal enhancement, and one had epidural collection. The alveolar margin was affected in two patients, and five patients had an extension to the cheek. The awareness of radiologists by the characteristic MRI features of ROCM in post-COVID-19 patients helps in early detection, early proper management, and prevention of morbid complications.

Understanding the consistency of pituitary macroadenomas is crucial for neurosurgeons planning surgery. This retrospective study aimed to evaluate the utility of diffusion-weighted imaging (DWI) and the apparent diffusion coefficient (ADC) as non-invasive imaging modalities for predicting the consistency of pituitary macroadenomas. This could contribute to appropriate surgical planning and therefore reduce the likelihood of incomplete resections. The study included 45 patients with pathologically confirmed pituitary macroadenomas. Conventional MRI sequences, DWIs, ADC maps, and pre- and post-contrast MRIs were performed. Two neuroradiologists assessed all of the images. Neurosurgeons assessed the consistency of the tumor macroscopically, and histopathologists examined it microscopically. The MRI findings were compared with postoperative data. According to the operative data, macroadenomas were divided into the two following categories based on their consistency: aspirable (n = 27) and non-aspirable tumors (n = 18). A statistically significant difference in DWI findings was found when comparing macroadenomas of different consistencies (p < 0.001). Most aspirable macroadenomas (66.7%) were hyperintense according to DWI and hypointense on ADC maps, whereas most non-aspirable macroadenomas (83.3%) were hypointense for DWI and hyperintense on ADC maps. At a cut-off value of 0.63 × 10⁻³ mm²/s, the ADC showed a sensitivity of 85.7% and a specificity of 75% for the detection of non-aspirable macroadenomas (AUC, 0.946). The study concluded that DWI should be routinely performed in conjunction with ADC measurements in the preoperative evaluation of pituitary macroadenomas. This approach may aid in surgical planning, ensure that appropriate techniques are utilized, and reduce the risk of incomplete resection.

COVID-19 is a profoundly contagious pandemic that has taken the world by storm and has afflicted different fields of life with negative effects. It has had a substantial impact on education which is evident from the transition from Face-to-Face (F2F) teaching to online mode of education and the rigid implementation of lockdown across the globe. This study examines the factors impacting the performance of teachers during the lockdown period of COVID-19 using various feature selection algorithms and Artificial Intelligence techniques. In this paper, we have proposed a novel multilevel feature selection for the prediction of the factors affecting the teachers’ satisfaction with online teaching and learning in COVID-19. The proposed multilevel feature selection is composed of the Fast Correlation Based Filter (FCBF), Mutual Information (MI), Relieff, and Particle Swarm Optimization (PSO) feature selection. The performance of the proposed feature selection approach is evaluated through the teachers’ benchmark dataset. We used a range of measures like accuracy, precision, f-measure, and recall to evaluate the performance of the proposed approach. We applied different machine learning approaches (SVM, LGBM, and ANN) with the proposed multilevel feature selection technique. The performance of the proposed approach is also compared with existing feature selection algorithms, and the results show the improvement in the performance of feature selection in terms of accuracy, precision, recall, and F-Measure. Proposed feature selection provides more than 80% accuracy with Light Weight Gradient Boosting Machine (LGBM).