Identification of individual therapy targets is critical for traumatic brain injury (TBI) patients. Clinical outcomes depend on cerebrovascular autoregulation (CA) impairment. Here, we compare the effectiveness of optimal cerebral perfusion pressure (CPPopt)-targeted therapy in younger (0.5 within 24 h after admission. The modeled accuracies for favorable and unfavorable outcome prediction were 86.5% and 90.9%, respectively. Age above 45 years and averaged ICP during all monitoring time above 21.3 mm Hg was associated with unfavorable outcome of an individual patient. Averaged CPP values close to CPPopt were associated with a better outcome in younger patients. Averaged ΔCPPopt <−5.0 mm Hg, averaged PRx >0.36, and LCAI >100 min were significantly associated with mortality for the younger patients. The critical values of averaged PRx >0.26 and LCAI >61 min were significantly associated with mortality for the elderly group. Autoregulation-guided treatment was important for individual TBI management, especially in younger patients. Further randomized multi-center studies are needed to prove final benefit.

The aim of this study was to explore the association of cerebrovascular autoregulation (CA) and optimal cerebral perfusion pressure (CPP) managing conditions with the outcome of traumatic brain injury (TBI) patients including additional information about the patients' age and grade of diffuse axonal injury (DAI). The CA monitoring of 28 TBI patients was performed by using ICM+ software (Cambridge, UK). The CA status estimating pressure reactivity indexes (PRx) and CPP data were processed in order to obtain information on the patient-specific treatment conditions by calculating the optimal CPP. There was a negative correlation between the Glasgow outcome scale (GOS) score and PRx (r=-0.448 at hospital discharge and r=-0.402 after 6 months). The estimated threshold value PRx of >0.24 was associated with mortality. The correlation coefficients between the GOS score and the difference CPP-optimal CPP were 0.549 at hospital discharge and 0.484 after 6 months. The threshold value of CPP declination from ΔCPPopt per -6mmHg was associated with mortality. Poorer outcome was predicted for elderly TBI patients (aged >47 years) and patients having a DAI grade of 3. The association of the GOS score with CPP, CA impairment conditions, age and diffuse axonal injury (DAI) grade showed that the outcomes of TBI patients were associated with patient-specific CPP management and better outcomes were obtained for younger patients, for patients having lower DAI grade and for patients whose CPP was kept within the range from the optimal CPP to the optimal CPP+10mmHg.

Objectives: Subarachnoid Hemorrhage (SAH) is a serious neurological emergency case with a higher mortality rate. An automatic SAH detection is needed to expedite and improve identification, aiding timely and efficient treatment pathways. The existence of noisy and dissimilar anatomical structures in NCCT images, limited availability of labeled SAH data, and ineffective training causes the issues of irrelevant features, overfitting, and vanishing gradient issues that make SAH detection a challenging task. Methods: In this work, the water waves dynamic factor and wandering strategy-based Sand Cat Swarm Optimization, namely DWSCSO, are proposed to ensure optimum feature selection while a Parametric Rectified Linear Unit with a Stacked Convolutional Neural Network, referred to as PRSCNN, is developed for classifying grades of SAH. The DWSCSO and PRSCNN surpass current practices in SAH detection by improving feature selection and classification accuracy. DWSCSO is proposed to ensure optimum feature selection, avoiding local optima issues with higher exploration capacity and avoiding the issue of overfitting in classification. Firstly, in this work, a modified region-growing method was employed on the patient Non-Contrast Computed Tomography (NCCT) images to segment the regions affected by SAH. From the segmented regions, the wide range of patterns and irregularities, fine-grained textures and details, and complex and abstract features were extracted from pre-trained models like GoogleNet, Visual Geometry Group (VGG)-16, and ResNet50. Next, the PRSCNN was developed for classifying grades of SAH which helped to avoid the vanishing gradient issue. Results: The DWSCSO-PRSCNN obtained a maximum accuracy of 99.48%, which is significant compared with other models. The DWSCSO-PRSCNN provides an improved accuracy of 99.62% in CT dataset compared with the DL-ICH and GoogLeNet + (GLCM and LBP), ResNet-50 + (GLCM and LBP), and AlexNet + (GLCM and LBP), which confirms that DWSCSO-PRSCNN effectively reduces false positives and false negatives. Conclusions: the complexity of DWSCSO-PRSCNN was acceptable in this research, for while simpler approaches appeared preferable, they failed to address problems like overfitting and vanishing gradients. Accordingly, the DWSCSO for optimized feature selection and PRSCNN for robust classification were essential for handling these challenges and enhancing the detection in different clinical settings.

Optimal cerebral perfusion pressure (CPPopt)-targeted treatment of traumatic brain injury (TBI) patients requires 2–8 h multi-modal monitoring data accumulation to identify CPPopt value for individual patient. Minimizing the time required for monitoring data accumulation is needed to improve the efficacy of CPPopt-targeted therapy. A retrospective analysis of multimodal physiological monitoring data from 87 severe TBI patients was performed by separately representing cerebrovascular autoregulation (CA) indices in relation to CPP, arterial blood pressure (ABP), and intracranial pressure (ICP) to improve the existing CPPopt identification algorithms. Machine learning (ML)-based algorithms were developed for automatic identification of informative data segments that were used for reliable CPPopt, ABPopt, ICPopt and the lower/upper limits of CA (LLCA/ULCA) identification. The reference datasets of the informative data segments and, artifact-distorted segments, and the datasets of different clinical situations were used for training the ML-based algorithms, allowing us to choose the appropriate individualized CPP-, ABP- or ICP-guided management for 79% of the full monitoring time for the studied population. The developed ML-based algorithms allow us to recognize informative physiological ABP/ICP variations within 24 min intervals with an accuracy up to 79% (compared to the initial accuracy of 74%) and use these segments for timely optimal value identification or CA limits determination in CPP, ABP or ICP data. Prospective clinical studies are needed to prove the efficiency of the developed algorithms.

Biophysics-based, patient-specific modeling remains challenging for clinical translation, particularly for cerebrospinal fluid (CSF) flow where anatomical detail and computational cost are tightly coupled. We present a computational framework for steady net CSF redistribution in an MRI-derived cranial CSF domain reconstructed from T₂-weighted imaging, including the ventricular system, cranial subarachnoid space, and periarterial pathways, to the extent resolvable by clinical MRI. Cranial CSF spaces were segmented in 3D Slicer and a steady Darcy formulation with prescribed CSF production/absorption was solved in COMSOL Multiphysics^®. Geometrical and flow descriptors were quantified using region-based projection operations. We assessed discretization cost–accuracy trade-offs by comparing first- and second-order finite elements. First-order elements produced a 1.4% difference in transmantle pressure and a <10% difference in element-wise mass-weighted velocity metric for 90% of elements, while reducing computation time by 75% (20 to 5 min) and peak memory usage five-fold (150 to 30 GB). This proof-of-concept framework provides a computationally tractable baseline for studying steady net CSF pathway redistribution and sensitivity to boundary assumptions, and may support future patient-specific investigations in pathological conditions such as subarachnoid hemorrhage, hydrocephalus and brain tumors.