Background CT is the most common imaging modality in traumatic brain injury (TBI). However, its conventional use requires expert clinical interpretation and does not provide detailed quantitative outputs, which may have prognostic importance. We aimed to use deep learning to reliably and efficiently quantify and detect different lesion types.Methods Patients were recruited between Dec 9, 2014, and Dec 17, 2017, in 60 centres across Europe. We trained and validated an initial convolutional neural network (CNN) on expert manual segmentations (dataset 1). This CNN was used to automatically segment a new dataset of scans, which we then corrected manually (dataset 2). From this dataset, we used a subset of scans to train a final CNN for multiclass, voxel-wise segmentation of lesion types. The performance of this CNN was evaluated on a test subset. Performance was measured for lesion volume quantification, lesion progression, and lesion detection and lesion volume classification. For lesion detection, external validation was done on an independent set of 500 patients from India. Findings 98 scans from one centre were included in dataset 1. Dataset 2 comprised 839 scans from 38 centres: 184 scans were used in the training subset and 655 in the test subset. Compared with manual reference, CNN-derived lesion volumes showed a mean difference of 0•86 mL (95% CI -5•23 to 6•94) for intraparenchymal haemorrhage, 1•83 mL (-12•01 to 15•66) for extra-axial haemorrhage, 2•09 mL (-9•38 to 13•56) for perilesional oedema, and 0•07 mL (-1•00 to 1•13) for intraventricular haemorrhage.Interpretation We show the ability of a CNN to separately segment, quantify, and detect multiclass haemorrhagic lesions and perilesional oedema. These volumetric lesion estimates allow clinically relevant quantification of lesion burden and progression, with potential applications for personalised treatment strategies and clinical research in TBI.

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Neurological manifestations in pandemics frequently cause short and long-term consequences which are frequently overlooked. Despite advances in the treatment of infectious diseases, nervous system involvement remains a challenge, with limited treatments often available. The under-recognition of neurological manifestations may lead to an increase in the burden of acute disease as well as secondary complications with long-term consequences. Nervous system infection or dysfunction during pandemics is common and its enduring consequences, especially among vulnerable populations, are frequently forgotten. An improved understanding the possible mechanisms of neurological damage during epidemics, and increased recognition of the possible manifestations is fundamental to bring insights when dealing with future outbreaks. To reverse this gap in knowledge, we reviewed all the pandemics, large and important epidemics of human history in which neurological manifestations are evident, and described the possible physiological processes that leads to the adverse sequelae caused or triggered by those pathogens.

Background: Failure of cerebral autoregulation and progression of intracranial lesion have both been shown to contribute to poor outcome in patients with acute traumatic brain injury (TBI), but the interplay between the two phenomena has not been investigated. Preliminary evidence leads us to hypothesize that brain tissue adjacent to primary injury foci may be more vulnerable to large fluctuations in blood flow in the absence of intact autoregulatory mechanisms. The goal of this study was therefore to assess the influence of cerebrovascular reactivity measures on radiological lesion expansion in a cohort of patients with acute TBI. Methods: We conducted a retrospective cohort analysis on 50 TBI patients who had undergone high-frequency multimodal intracranial monitoring and for which at least two brain computed tomography (CT) scans had been performed in the acute phase of injury. We first performed univariate analyses on the full cohort to identify nonneurophysiological factors (i.e., initial lesion volume, timing of scan, coagulopathy) associated with traumatic lesion growth in this population. In a subset analysis of 23 patients who had intracranial recording data covering the period between the initial and repeat CT scan, we then correlated changes in serial volumetric lesion measurements with cerebrovascular reactivity metrics derived from the pressure reactivity index (PRx), pulse amplitude index (PAx), and RAC (correlation coefficient between the pulse amplitude of intracranial pressure and cerebral perfusion pressure). Using multivariate methods, these results were subsequently adjusted for the non-neurophysiological confounders identified in the univariate analyses. Results: We observed significant positive linear associations between the degree of cerebrovascular reactivity impairment and progression of pericontusional edema. The strongest correlations were observed between edema progression and the following indices of cerebrovascular reactivity between sequential scans: % time PRx > 0.25 (r = 0.69, p = 0.002) and % time PAx > 0.25 (r = 0.64, p = 0.006). These associations remained significant after adjusting for initial lesion volume and mean cerebral perfusion pressure. In contrast, progression of the hemorrhagic core and extra-axial hemorrhage volume did not appear to be strongly influenced by autoregulatory status. Conclusions: Our preliminary findings suggest a possible link between autoregulatory failure and traumatic edema progression, which warrants re-evaluation in larger-scale prospective studies.

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BACKGROUND:Blood biomarkers are of increasing importance in the diagnosis and assessment of traumatic brain injury (TBI). However, the relationship between them and lesions seen on imaging remains unclear.OBJECTIVE:To perform a systematic review of the relationship between blood biomarkers and intracranial lesion types, intracranial lesion injury patterns, volume/number of intracranial lesions, and imaging classification systems.METHODS:We searched Medical Literature Analysis and Retrieval System Online, Excerpta Medica dataBASE, and Cumulative Index to Nursing and Allied Health Literature from inception to May 2021, and the references of included studies were also screened. Heterogeneity in study design, biomarker types, imaging modalities, and analyses inhibited quantitative analysis, with a qualitative synthesis presented.RESULTS:Fifty-nine papers were included assessing one or more biomarker to imaging comparisons per paper: 30 assessed imaging classifications or injury patterns, 28 assessed lesion type, and 11 assessed lesion volume or number. Biomarker concentrations were associated with the burden of brain injury, as assessed by increasing intracranial lesion volume, increasing numbers of traumatic intracranial lesions, and positive correlations with imaging classification scores. There were inconsistent findings associating different biomarkers with specific imaging phenotypes including diffuse axonal injury, cerebral edema, and intracranial hemorrhage.CONCLUSION:Blood-based biomarker concentrations after TBI are consistently demonstrated to correlate burden of intracranial disease. The relation with specific injury types is unclear suggesting a lack of diagnostic specificity and/or is the result of the complex and heterogeneous nature of TBI.

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Background We aimed to understand the relationship between serum biomarker concentration and lesion type and volume found on computed tomography (CT) following all severities of TBI.Methods Concentrations of six serum biomarkers (GFAP, NFL, NSE, S100B, t-tau and UCH-L1) were measured in samples obtained <24 hours post-injury from 2869 patients with all severities of TBI, enrolled in the CENTER-TBI prospective cohort study (NCT02210221). Imaging phenotypes were defined as intraparenchymal haemorrhage (IPH), oedema, subdural haematoma (SDH), extradural haematoma (EDH), traumatic subarachnoid haemorrhage (tSAH), diffuse axonal injury (DAI), and intraventricular haemorrhage (IVH). Multivariable polynomial regression was performed to examine the association between biomarker levels and both distinct lesion types and lesion volumes. Hierarchical clustering was used to explore imaging phenotypes; and principal component analysis and kmeans clustering of acute biomarker concentrations to explore patterns of biomarker clustering.Findings 2869 patient were included, 68% (n=1946) male with a median age of 49 years (range 2-96). All severities of TBI (mild, moderate and severe) were included for analysis with majority (n=1946, 68%) having a mild injury . Patients with severe diffuse injury (Marshall III/IV) showed significantly higher levels of all measured biomarkers, with the exception of NFL, than patients with focal mass lesions (Marshall grades V/VI). Patients with either DAI+IVH or SDH+IPH+tSAH, had significantly higher biomarker concentrations than patients with EDH. Higher biomarker concentrations were associated with greater volume of IPH (GFAP, S100B, t-tau;adj r2 range:0¢48-0¢49; p<0¢05), oedema (GFAP, NFL, NSE, t-tau, UCH-L1;adj r2 range:0¢44-0¢44; p<0¢01), IVH (S100B;adj r2 range:0.48-0.49; p<0.05), Unsupervised k-means biomarker clustering revealed two clusters explaining 83¢9% of variance, with phenotyping characteristics related to clinical injury severity.