A Robust, Fully Automatic Detection Method and Calculation Technique of Midline Shift in Intracranial Hemorrhage and Its Clinical Application

Abstract

A midline shift (MLS) is an important clinical indicator for intracranial hemorrhage. In this study, we proposed a robust, fully automatic neural network-based model for the detection of MLS and compared it with MLSs drawn by clinicians; we also evaluated the clinical applications of the fully automatic model. We recruited 300 consecutive non-contrast CT scans consisting of 7269 slices in this study. Six different types of hemorrhage were included. The automatic detection of MLS was based on modified Keypoint R-CNN with keypoint detection followed by training on the ResNet-FPN-50 backbone. The results were further compared with manually drawn outcomes and manually defined keypoint calculations. Clinical parameters, including Glasgow coma scale (GCS), Glasgow outcome scale (GOS), and 30-day mortality, were also analyzed. The mean absolute error for the automatic detection of an MLS was 0.936 mm compared with the ground truth. The interclass correlation was 0.9899 between the automatic method and MLS drawn by different clinicians. There was high sensitivity and specificity in the detection of MLS at 2 mm (91.7%, 80%) and 5 mm (87.5%, 96.7%) and MLSs greater than 10 mm (85.7%, 97.7%). MLS showed a significant association with initial poor GCS and GCS on day 7 and was inversely correlated with poor 30-day GOS (p < 0.001). In conclusion, automatic detection and calculation of MLS can provide an accurate, robust method for MLS measurement that is clinically comparable to the manually drawn method.

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

“…Another study that aimed to measure the midline shift in TBI patients was demonstrated by Wei et al, [ 63 ] where the proposed CNN-based model estimated the extent of midline shift with average distance errors of 1.1 ± 70.72 mm and 4.15 ± 3.97 mm on CQ500 and the internal dataset, respectively. Another recent study [ 64 ] utilized CNN-based architecture to predict several imaging landmarks to predict the extent of midline. We noted that no ML approaches existed that identified cerebellar tonsillar herniation.…”

“…•Any hematoma [32,33] •ICH [34][35][36][37] •Normal/abnormal [38][39][40] •ICP level (high/low) [41][42][43] •Hematoma expansion [44] •Any hematoma [45][46][47][48] •ICH [49][50][51][52][53][54][55] •SDH [56,57] •Normal/abnormal [58] •ICH [59] •SDH [60] •Normal/Abnormal [61] Others •Midline delineation [62][63][64] •Cisterns [59] •Midline [59]…”

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

“…The straightforward definition of MLS can be a deviation from the actual/IML of the brain. 8 Various authors often describe MLS in terms of the displacement of the SP, relative to the IML observed on CT images. 9,10 Deviation of the midline structure, whether it be the pineal gland, third ventricle, or SP, from the IML is also labeled as a MLS.…”

“…Implementing an automated detection system can promptly flag cases requiring urgent review, facilitating timely care provided by clinicians. 8 Although the automated system is not intended to surpass the expertise of specialists such as neurosurgeons, neurologists, or neuroradiologists, it can save their time and offer valuable objective information, especially in emergency settings. Careful screening by the automated system might also provide insights for further management, enabling first-line physicians to reconsider injury severity and prioritize accordingly.…”

“…Therefore, while an expert's diagnosis remains irreplaceable, the use of automated standardized image-based systems is recommended due to their objectivity and generally faster results. 8 In our review paper, we have also addressed the limitations of current AI methods and highlighted future research directions for enhancing automated CT identification and quantification of TBI-related lesions. Recent studies in CT image recognition have heavily relied on deep neural network frameworks, particularly those employing convolutional neural network (CNN) architecture.…”