Comprehensive Summary
In this review conducted by Babikar et al., various studies that were carried out in order to determine the effectiveness of AI models in the prediction of intracranial hypertension (ICH) are compared. An all-encompassing search is completed across multiple databases, looking for studies that assess algorithms created with the intention of predicting ICH in patients suffering traumatic brain injuries. The process in which studies are collected involves two reviewers searching across different publications, and finding studies that match their needs; The finds of both the reviewers are then combined into a master list to be further filtered for duplicates and specifications. Data from each study, such as, design, sample size, algorithm used, and outcome measured, are pulled from each research study chosen, and put against each other in a table. This allows the reviewers to look at the performance metrics of each of the AI models on a scale. Reviewers, then, compare the prediction horizon, complexity, and clinical readiness of each algorithm. They are able to determine that elaborate ensemble models achieve the most success in their predictions of ICH, but show less promise in clinical readiness and application than simpler AI models with lower predictive ability.
Outcomes and Implications
The usage of AI to predict ICH could be life-changing - and life-saving - for patients with traumatic brain injuries. In order to minimize the lasting trauma that comes with ICH, it needs to be identified and dealt with in a timely manner. However, in spite of how important this field is to neurology as a whole, there has not been much development past the research stage of this project. By reviewing the various studies that have explored the use of AI in regards to intracranial hypertension (ICH), Babikar et al. reveals why, despite an abundance of research on the subject, traumatic brain injury care has not seen much of AI models. They are able to show where current AI models are falling short - their inability to be implemented in clinical settings - and, in turn, direct future research in this field.