Applications of Machine Learning in Prognostication of mTBI: A Systematic Review.

Abstract

To review the literature regarding the current state and clinical applicability of machine learning (ML) models in prognosticating the outcomes of patients with mild traumatic brain injury (mTBI) in the early clinical presentation. Databases were searched for studies including ML and mTBI from inception to March 10, 2023. Included studies had a primary outcome of predicting post-mTBI prognosis or sequalae. The Prediction model study Risk of Bias for Predictive Models assessment tool (PROBAST) was used for assessing the risk of bias and applicability of included studies. Out of 1235 articles, 10 met the inclusion criteria, including data from 127,929 patients. The most frequently used modeling techniques were Support Vector Machine (SVM) and Artificial Neural Network (NN) and Area Under the Curve (AUC) ranged from 0.66-0.889. Despite promise, several limitations to studies exist such as low sample sizes, database restrictions, inconsistencies in patient presentation definitions and lack of comparison to traditional clinical judgment or tools. ML models show potential in early stage mTBI prognostication, but to achieve widespread adoption, future clinical studies prognosticating mTBI using ML need to reduce bias, provide clarity and consistency in defining patient populations targeted, and validate against established benchmarks.