Abstract
Traumatic brain injury (TBI) often results in midline shift (MLS) that is a critical
indicator of the severity and prognosis of head injuries. Automated analysis of MLS
from head computed tomography (CT) scans using artificial intelligence (AI) techniques
has gained much attention in the past decade and has shown promise in improving diagnostic
efficiency and accuracy. This review aims to summarize the current state of research
on AI-based approaches for MLS analysis in TBI cases, identify the methodologies employed,
evaluate the performance of the algorithms, and draw conclusions regarding their potential
clinical applicability. A comprehensive literature search was conducted, identifying
15 distinctive publications. The identified articles were analyzed for their focus
on MLS detection and quantification using AI techniques, including their choice of
AI algorithms, dataset characteristics, and methodology. The reviewed articles covered
various aspects related to MLS detection and quantification, employing deep neural
networks trained on two-dimensional or three-dimensional CT imaging datasets. The
dataset sizes ranged from 11 patients' CT scans to 25,000 CT images. The performance
of the AI algorithms exhibited variations in accuracy, sensitivity, and specificity,
with sensitivity ranging from 70 to 100%, and specificity ranging from 73 to 97.4%.
AI-based approaches utilizing deep neural networks have demonstrated potential in
the automated detection and quantification of MLS in TBI cases. However, different
researchers have used different techniques; hence, critical comparison is difficult.
Further research and standardization of evaluation protocols are needed to establish
the reliability and generalizability of these AI algorithms for MLS detection and
quantification in clinical practice. The findings highlight the importance of AI techniques
in improving MLS diagnosis and guiding clinical decision-making in TBI management.
Keywords
midline shift - deep learning - traumatic brain injury - automated detection - computed
tomography - artificial intelligence
