Association between cranial asymmetry severity and chronic subdural hematoma laterality

AI-Based Approach to One-Click Chronic Subdural Hematoma Segmentation Using Computed Tomography Images

This paper presents a computer vision-based approach to chronic subdural hematoma segmentation that can be performed by one click. Chronic subdural hematoma is estimated to occur in 0.002-0.02% of the general population each year and the risk increases with age, with a high frequency of about 0.05-0.06% in people aged 70 years and above. In our research, we developed our own dataset, which includes 53 series of CT scans collected from 21 patients with one or two hematomas. Based on the dataset, we trained two neural network models based on U-Net architecture to automate the manual segmentation process. One of the models performed segmentation based only on the current frame, while the other additionally processed multiple adjacent images to provide context, a technique that is more similar to the behavior of a doctor. We used a 10-fold cross-validation technique to better estimate the developed models' efficiency. We used the Dice metric for segmentation accuracy estimation, which was 0.77. Also, for testing our approach, we used scans from five additional patients who did not form part of the dataset, and created a scenario in which three medical experts carried out a hematoma segmentation before we carried out segmentation using our best model. We developed the OsiriX DICOM Viewer plugin to implement our solution into the segmentation process. We compared the segmentation time, which was more than seven times faster using the one-click approach, and the experts agreed that the segmentation quality was acceptable for clinical usage.

Cortical atrophy in chronic subdural hematoma from ultra-structures to physical properties

Several theories have tried to elucidate the mechanisms behind the pathophysiology of chronic subdural hematoma (CSDH). However, this process is complex and remains mostly unknown. In this study we performed a retrospective randomised analysis comparing the cortical atrophy of 190 patients with unilateral CSDH, with 190 healthy controls. To evaluate the extent of cortical atrophy, CT scan images were utilised to develop an index that is the ratio of the maximum diameter sum of 3 cisterns divided by the maximum diameter of the skull at the temporal lobe level. Also, we reported, for the first time, the ultrastructural analyses of the CSDH using a combination of immunohistochemistry methods and transmission electron microscopy techniques. Internal validation was performed to confirm the assessment of the different degrees of cortical atrophy. Relative Cortical Atrophy Index (RCA index) refers to the sum of the maximum diameter of three cisterns (insular cistern, longitudinal cerebral fissure and cerebral sulci greatest) with the temporal bones' greatest internal distance. This index, strongly related to age in healthy controls, is positively correlated to the preoperative and post-operative maximum diameter of hematoma and the midline shift in CSDH patients. On the contrary, it negatively correlates to the Karnofsky Performance Status (KPS). The Area Under the Receiver Operating Characteristics (AUROC) showed that RCA index effectively differentiated cases from controls. Immunohistochemistry analysis showed that the newly formed CD-31 positive microvessels are higher in number than the CD34-positive microvessels in the CSDH inner membrane than in the outer membrane. Ultrastructural observations highlight the presence of a chronic inflammatory state mainly in the CSDH inner membrane. Integrating these results, we have obtained an etiopathogenetic model of CSDH. Cortical atrophy appears to be the triggering factor activating the cascade of transendothelial cellular filtration, inflammation, membrane formation and neovascularisation leading to the CSDH formation.