Automated identification and quantification of traumatic brain injury from CT scans: Are we there yet?

Abstract

Background: The purpose of this study was to conduct a systematic review for understanding the availability and limitations of artificial intelligence (AI) approaches that could automatically identify and quantify computed tomography (CT) findings in traumatic brain injury (TBI). Methods: Systematic review, in accordance with PRISMA 2020 and SPIRIT-AI extension guidelines, with a search of 4 databases (Medline, Embase, IEEE Xplore, and Web of Science) was performed to find AI studies that automated the clinical tasks for identifying and quantifying CT findings of TBI-related abnormalities. Results: A total of 531 unique publications were reviewed, which resulted in 66 articles that met our inclusion criteria. The following components for identification and quantification regarding TBI were covered and automated by existing AI studies: identification of TBI-related abnormalities; classification of intracranial hemorrhage types; slice-, pixel-, and voxel-level localization of hemorrhage; measurement of midline shift; and measurement of hematoma volume. Automated identification of obliterated basal cisterns was not investigated in the existing AI studies. Most of the AI algorithms were based on deep neural networks that were trained on 2- or 3-dimensional CT imaging datasets. Conclusion: We identified several important TBI-related CT findings that can be automatically identified and quantified with AI. A combination of these techniques may provide useful tools to enhance reproducibility of TBI identification and quantification by supporting radiologists and clinicians in their TBI assessments and reducing subjective human factors.

Automated Midline Shift Detection and Quantification in Traumatic Brain Injury: A Comprehensive Review

“…Building upon our previous work on intracranial hemorrhage (ICH) detection in TBI, which outlined future research directions, 15 this review aims to address this gap by specifically examining the detection and quantification of MLS in TBI cases. 14 The field of automatic MLS measurement has witnessed significant advancements, with several approaches being proposed to quantify MLS from head CT scans. These automatic methods leverage the symmetry of the brain or utilize specific anatomical landmarks, such as the falx cerebri, frontal horns of the lateral ventricles, and the third ventricle.…”

“…Building upon our previous work on intracranial hemorrhage (ICH) detection in TBI, which outlined future research directions, 15 this review aims to address this gap by specifically examining the detection and quantification of MLS in TBI cases. 14…”

“…Recent studies in CT image recognition have heavily relied on deep neural network frameworks, particularly those employing convolutional neural network (CNN) architecture. 14 The aim is achieved by studying the available literature linking automation, CT scans, and TBI.…”

Key Findings on Computed Tomography of the Head that Predict Death or the Need for Neurosurgical Intervention From Traumatic Brain Injury

“…Various algorithms have been developed that are successful in identifying certain abnormalities, including hemorrhage, hematoma volume, midline shift, and localization of abnormal findings. 15,16 Although these algorithms are powerful, it is difficult to design algorithms that can identify the wide range of abnormal findings that can be seen on CTH after TBI. 15 Machine learning models for CTH interpretation may currently be useful for triage purposes, where abnormal findings can be brought to the attention of radiologists for definitive diagnosis.…”

“…15,16 Although these algorithms are powerful, it is difficult to design algorithms that can identify the wide range of abnormal findings that can be seen on CTH after TBI. 15 Machine learning models for CTH interpretation may currently be useful for triage purposes, where abnormal findings can be brought to the attention of radiologists for definitive diagnosis. 16 Future innovations may significantly expand the capabilities of automated CTH interpretation, but the simplicity of our current schema allows for rapid manual interpretation of CTH findings by clinicians.…”