Prediction of cerebral aneurysm rupture risk by machine learning algorithms: a systematic review and meta-analysis of 18,670 participants.

It is possible to identify unruptured intracranial aneurysms (UIA) using machine learning (ML) algorithms, which can be a life-saving strategy, especially in high-risk populations. To better understand the importance and effectiveness of ML algorithms in practice, a systematic review and meta-analysis were conducted to predict cerebral aneurysm rupture risk. PubMed, Scopus, Web of Science, and Embase were searched without restrictions until March 20, 2023. Eligibility criteria included studies that used ML approaches in patients with cerebral aneurysms confirmed by DSA, CTA, or MRI. Out of 35 studies included, 33 were cohort, and 11 used digital subtraction angiography (DSA) as their reference imaging modality. Middle cerebral artery (MCA) and anterior cerebral artery (ACA) were the commonest locations of aneurysmal vascular involvement-51% and 40%, respectively. The aneurysm morphology was saccular in 48% of studies. Ten of 37 studies (27%) used deep learning techniques such as CNNs and ANNs. Meta-analysis was performed on 17 studies: sensitivity of 0.83 (95% confidence interval (CI), 0.77-0.88); specificity of 0.83 (95% CI, 0.75-0.88); positive DLR of 4.81 (95% CI, 3.29-7.02) and the negative DLR of 0.20 (95% CI, 0.14-0.29); a diagnostic score of 3.17 (95% CI, 2.55-3.78); odds ratio of 23.69 (95% CI, 12.75-44.01). ML algorithms can effectively predict the risk of rupture in cerebral aneurysms with good levels of accuracy, sensitivity, and specificity. However, further research is needed to enhance their diagnostic performance in predicting the rupture status of IA.

Mucormycosis infection in patients with COVID-19: A systematic review.

INTRODUCTION: Several reports previously described mucormycosis co-infection in patients with COVID-19. As mucormycosis and COVID-19 co-infection might adversely affect patients' outcomes, we aimed to systematically review the related evidence and the subsequent outcomes. METHODS: We conducted a systematic review of relevant articles searching the keywords in the online databases of PubMed, Scopus, Embase, Cochrane, and Web of Science. All the records from the start of the pandemic until June 12th, 2021 underwent title/abstract and then full-text screening process, and the eligible studies were included. We did not include any language or time restrictions for the included studies. RESULTS: We found 31 eligible studies reporting 144 total cases of COVID-19 and mucormycosis co-infection. The nose, cranial sinuses, and orbital cavity were the most commonly involved organs, although the cerebrum, lungs, and heart were also involved in the studies. Pre-existing diabetes mellitus (DM), as well as corticosteroid use, were the most commonly identified risk factors, but other underlying conditions and immunomodulatory drug use were also present in several cases. Aspergillus was the most commonly reported micro-organism that caused further co-infections in patients with concurrent COVID-19 and mucormycosis. As most of the studies were case reports, no reliable estimate of the mortality rate could be made, but overall, 33.6% of the studied cases died. CONCLUSION: Early diagnosis of mucormycosis co-infection in COVID-19 patients and selecting the right treatment plan could be a challenge for physicians. Patients with underlying co-morbidities, immunocompromised patients, and those receiving corticosteroids are at higher risk of developing mucormycosis co-infection and it is crucial to have an eye examination for early signs and symptoms suggesting a fungal infection in these patients.