Clinical and Imaging Evaluation of COVID-19-Related Olfactory Dysfunction.

BACKGROUND: Olfactory dysfunction has been reported in 47.85% of COVID patients. It can be broadly categorized into conductive or sensorineural olfactory loss. Conductive loss occurs due to impaired nasal air flow, while sensorineural loss implies dysfunction of the olfactory epithelium or central olfactory pathways. OBJECTIVES: The aim of this study was to analyze the clinical and imaging findings in patients with COVID-related olfactory dysfunction. Additionally, the study aimed to investigate the possible mechanisms of COVID-related olfactory dysfunction. METHODS: The study included 110 patients with post-COVID-19 olfactory dysfunction, and a control group of 50 COVID-negative subjects with normal olfactory function. Endoscopic nasal examination was performed for all participants with special focus on the olfactory cleft. Smell testing was performed for all participants by using a smell diskettes test. Olfactory pathway magnetic resonance imaging (MRI) was done to assess the condition of the olfactory cleft and the dimensions and volume of the olfactory bulb. RESULTS: Olfactory dysfunction was not associated with nasal symptoms in 51.8% of patients. MRI showed significantly increased olfactory bulb dimensions and volume competed to controls. Additionally, it revealed olfactory cleft edema in 57.3% of patients. On the other hand, radiological evidence of sinusitis was detected in only 15.5% of patients. CONCLUSION: The average olfactory bulb volumes were significantly higher in the patients' group compared to the control group, indicating significant edema and swelling in the olfactory bulb in patients with COVID-related olfactory dysfunction. Furthermore, in most patients, no sinonasal symptoms such as nasal congestion or rhinorrhea were reported, and similarly, no radiological evidence of sinusitis was detected. Consequently, the most probable mechanism of COVID-related olfactory dysfunction is sensorineural loss through virus spread and damage to the olfactory epithelium and pathways.

Role of Imaging and AI in the Evaluation of COVID-19 Infection: A Comprehensive Survey.

Coronavirus disease 2019 (COVID-19) is a respiratory illness that started and rapidly became the pandemic of the century, as the number of people infected with it globally exceeded 253.4 million. Since the beginning of the pandemic of COVID-19, over two years have passed. During this hard period, several defies have been coped by the scientific society to know this novel disease, evaluate it, and treat affected patients. All these efforts are done to push back the spread of the virus. This article provides a comprehensive review to learn about the COVID-19 virus and its entry mechanism, its main repercussions on many organs and tissues of the body, identify its symptoms in the short and long terms, in addition to recognize the role of diagnosis imaging in COVID-19. Principally, the quick evolution of active vaccines act an exceptional accomplishment where leaded to decrease rate of death worldwide. However, some hurdels still have to be overcome. Many proof referrers that infection with CoV-19 causes neurological dis function in a substantial ratio of influenced patients, where these symptoms appear severely during the infection and still less is known about the potential long term consequences for the brain, where Loss of smell is a neurological sign and rudimentary symptom of COVID-19. Hence, we review the causes of olfactory bulb dysfunction and Anosmia associated with COVID-19, the latest appropriate therapeutic strategies for the COVID-19 treatment (e.g., the ACE2 strategy and the Ang II receptor), and the tests through the follow-up phases. Additionally, we discuss the long-term complications of the virus and thus the possibility of improving therapeutic strategies. Moreover, the main steps of artificial intelligence that have been used to foretell and early diagnose COVID-19 are presented, where Artificial intelligence, especially machine learning is emerging as an effective approach for diagnostic image analysis with performance in the discriminate diagnosis of injuries of COVID-19 on multiple organs, comparable to that of human practitioners. The followed methodology to prepare the current survey is to search the related work concerning the mentioned topic from different journals, such as Springer, Wiley, and Elsevier. Additionally, different studies have been compared, the results are collected and then reported as shown. The articles are selected based on the year (i.e., the last three years). Also, different keywords were checked (e.g., COVID-19, COVID-19 Treatment, COVID-19 Symptoms, and COVID-19 and Anosmia).

Diagnostic performance of chest computed tomography for COVID-19 in children: a systematic review and meta-analysis of clinical and computed tomography features in 987 patients.

Purpose: The outbreak of a new coronavirus is still spreading worldwide, affecting children and adults. However, COVID-19 in children shows distinctive characteristics in clinical and radiological presentation. We aimed to assess the diagnostic performance of chest CT and clarify the clinicoradiological CT features of COVID-19 among children with COVID-19. Material and methods: Adhering to PRISMA-DTA guidelines, we searched databases (PubMed, Google Scholar, and Web of Science) to identify relevant articles. The search keywords were: "Chest CT" AND "COVID-19" OR "coronavirus" OR "SARS-COV-2" AND "Children" OR "Pediatric". Published reports providing clinical and imaging findings of paediatric COVID-19 were included. Results: Twenty-eight studies were included, with 987 patients. Most of the patients were symptomatic (76.9%; 95% CI: 69.2-84.7%), with fever being the most frequent manifestation (64%; 95% CI: 58.0-71.2%). Only 2.3% of the cases were critical, and mortality was reported in one case. The proportion of COVID-19 detected by chest CT among children is relatively high (658/987), with ground-glass opacity (GGO) being the most prevalent feature (52.5%; 95% CI: 40.5-64.7%). The pooled sensitivity of chest CT in all patients was 67%; however, it was different between symptomatic and asymptomatic patients (71% and 33%, respectively). The pooled specificity was (67%), which was calculated after considering the symptomatic PCR-positive patients as the gold standard. Conclusions: Chest CT showed moderate pooled sensitivity and specificity among symptomatic children with COVID-19 and low sensitivity among asymptomatic children. This means that CT is not to be used as a screening tool or for confirmation of the diagnosis in children and should be reserved for specific clinical situations.