Background:
Traumatic brain injury (TBI) is a leading cause of death and disability worldwide. The use of machine learning (ML) has emerged as a key advancement in TBI management. This study aimed to identify ML models with demonstrated effectiveness in predicting TBI outcomes.
Methods:
We conducted a systematic review in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis statement. In total, 15 articles were identified using the search strategy. Patient demographics, clinical status, ML outcome variables, and predictive characteristics were extracted. A small meta-analysis of mortality prediction was performed, and a meta-analysis of diagnostic accuracy was conducted for ML algorithms used across multiple studies.
Results:
ML algorithms including support vector machine (SVM), artificial neural networks (ANN), random forest, and Naïve Bayes were compared to logistic regression (LR). Thirteen studies found significant improvement in prognostic capability using ML versus LR. The accuracy of the above algorithms was consistently over 80% when predicting mortality and unfavorable outcome measured by Glasgow Outcome Scale. Receiver operating characteristic curves analyzing the sensitivity of ANN, SVM, decision tree, and LR demonstrated consistent findings across studies. Lower admission Glasgow Coma Scale (GCS), older age, elevated serum acid, and abnormal glucose were associated with increased adverse outcomes and had the most significant impact on ML algorithms.
Conclusion:
ML algorithms were stronger than traditional regression models in predicting adverse outcomes. Admission GCS, age, and serum metabolites all have strong predictive power when used with ML and should be considered important components of TBI risk stratification.
Background
Benign enlargement of subarachnoid spaces (BESS) is a common cause of macro crania in infancy and may present with developmental delay due to enlargement of the brain. The self-limiting nature of this condition allows most children to regain developmental milestones as they grow older. The aetiology of developmental delay in full-term infants with BESS is unclear and prognosis varies since only some children have been reported to have long-term deficit in gross motor and language domains.
Case presentation:
This case report illustrates the clinical profile and findings on brain imaging in a full-term infant who presented with BESS along with delay in attaining age-appropriate gross motor and language milestones. Assessment of developmental milestones at 15 months of age supported the benign nature of this condition although the head circumference remained above the 95th percentile for age.
Conclusions
Despite the self-resolving nature of this condition, regular evaluation of developmental milestones is necessary to ensure timely intervention in appropriate cases. Further research on the aetiology could also aid clinicians in determining which children are at risk for developing long-term developmental delay.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.