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.

Awareness of Iranian Medical Sciences Students Towards Basic Life Support; a Cross-Sectional study

Introduction

Augmentation of the number of trained basic life support (BLS) providers can remarkably reduce the number of cardiac arrest victims. The aim of this study was to evaluate the level of BLS awareness among students of medical sciences in Iran.

Methods

This multicenter cross-sectional study was performed on medical students at the 4 major medical schools in Tehran, the capital of Iran, between Jan 2018 and Feb 2019, using convenience sampling method. The level of medical sciences students' awareness of BLS was measured using an international questionnaire.

Results

Finally, 1210 students with the mean age of 21.2 ± 2.3 years completed the survey (79% female). 133 (10.9%) students had CPR experience and none had received any formal training. None of the responders could answer all questions correctly. The mean awareness score of participants was 11.93 ± 2.87 (range: 10.13 -17.25). The awareness score of participants was high in 49 (4.04 %) participants, moderate in 218 (18.01%), and low in 943 (77.93%) of studied cases.

Conclusion

Based on the findings of this study, more than 70% of the studied medical sciences students obtained a low score on BLS awareness.