These collected and analyzed data demonstrate that porous HA is a suitable material to produce custom-made prostheses to repair craniolacunia. It is a biomimetic implant well-tolerated in both adult and pediatric patients and has been shown to be an effective and good alternative for cranial reconstruction.

Background and Objectives:Declines in stroke admission, intravenous thrombolysis, and mechanical thrombectomy volumes were reported during the first wave of the COVID-19 pandemic. There is a paucity of data on the longer-term effect of the pandemic on stroke volumes over the course of a year and through the second wave of the pandemic. We sought to measure the impact of the COVID-19 pandemic on the volumes of stroke admissions, intracranial hemorrhage (ICH), intravenous thrombolysis (IVT), and mechanical thrombectomy over a one-year period at the onset of the pandemic (March 1, 2020, to February 28, 2021) compared with the immediately preceding year (March 1, 2019, to February 29, 2020).Methods:We conducted a longitudinal retrospective study across 6 continents, 56 countries, and 275 stroke centers. We collected volume data for COVID-19 admissions and 4 stroke metrics: ischemic stroke admissions, ICH admissions, intravenous thrombolysis treatments, and mechanical thrombectomy procedures. Diagnoses were identified by their ICD-10 codes or classifications in stroke databases.Results:There were 148,895 stroke admissions in the one-year immediately before compared to 138,453 admissions during the one-year pandemic, representing a 7% decline (95% confidence interval [95% CI 7.1, 6.9]; p<0.0001). ICH volumes declined from 29,585 to 28,156 (4.8%, [5.1, 4.6]; p<0.0001) and IVT volume from 24,584 to 23,077 (6.1%, [6.4, 5.8]; p<0.0001). Larger declines were observed at high volume compared to low volume centers (all p<0.0001). There was no significant change in mechanical thrombectomy volumes (0.7%, [0.6,0.9]; p=0.49). Stroke was diagnosed in 1.3% [1.31,1.38] of 406,792 COVID-19 hospitalizations. SARS-CoV-2 infection was present in 2.9% ([2.82,2.97], 5,656/195,539) of all stroke hospitalizations.Discussion:There was a global decline and shift to lower volume centers of stroke admission volumes, ICH volumes, and IVT volumes during the 1st year of the COVID-19 pandemic compared to the prior year. Mechanical thrombectomy volumes were preserved. These results suggest preservation in the stroke care of higher severity of disease through the first pandemic year.Trial Registration Information:This study is registered underNCT04934020.

BackgroundIn recent years, machine learning (ML) has had notable success in providing automated analyses of neuroimaging studies, and its role is likely to increase in the future. Thus, it is paramount for clinicians to understand these approaches, gain facility with interpreting ML results, and learn how to assess algorithm performance.ObjectiveTo provide an overview of ML, present its role in acute stroke imaging, discuss methods to evaluate algorithms, and then provide an assessment of existing approaches.MethodsIn this review, we give an overview of ML techniques commonly used in medical imaging analysis and methods to evaluate performance. We then review the literature for relevant publications. Searches were run in November 2021 in Ovid Medline and PubMed. Inclusion criteria included studies in English reporting use of artificial intelligence (AI), machine learning, or similar techniques in the setting of, and in applications for, acute ischemic stroke or mechanical thrombectomy. Articles that included image-level data with meaningful results and sound ML approaches were included in this discussion.ResultsMany publications on acute stroke imaging, including detection of large vessel occlusion, detection and quantification of intracranial hemorrhage and detection of infarct core, have been published using ML methods. Imaging inputs have included non-contrast head CT, CT angiograph and MRI, with a range of performances. We discuss and review several of the most relevant publications.ConclusionsML in acute ischemic stroke imaging has already made tremendous headway. Additional applications and further integration with clinical care is inevitable. Thus, facility with these approaches is critical for the neurointerventional clinician.

OBJECTIVE Machine learning algorithms have shown groundbreaking results in neuroimaging. Herein, the authors evaluate the performance of a newly developed convolutional neural network (CNN) to detect and quantify the thickness, volume, and midline shift (MLS) of subdural hematoma (SDH) from noncontrast head CT (NCHCT). METHODS NCHCT studies performed for the evaluation of head trauma in consecutive patients between July 2018 and April 2021 at a single institution were retrospectively identified. Ground truth determination of SDH, thickness, and MLS was established by the neuroradiology report. The primary outcome was performance of the CNN in detecting SDH in an external validation set, as measured using area under the receiver operating characteristic curve analysis. Secondary outcomes included accuracy for thickness, volume, and MLS. RESULTS Among 263 cases with valid NCHCT according to the study criteria, 135 patients (51%) were male, the mean (± standard deviation) age was 61 ± 23 years, and 70 patients were diagnosed with SDH on neuroradiologist evaluation. The median SDH thickness was 11 mm (IQR 6 mm), and 16 patients had a median MLS of 5 mm (IQR 2.25 mm). In the independent data set, the CNN performed well, with sensitivity of 91.4% (95% CI 82.3%–96.8%), specificity of 96.4% (95% CI 92.7%–98.5%), and accuracy of 95.1% (95% CI 91.7%–97.3%); sensitivity for the subgroup with an SDH thickness above 10 mm was 100%. The maximum thickness mean absolute error was 2.75 mm (95% CI 2.14–3.37 mm), whereas the MLS mean absolute error was 0.93 mm (95% CI 0.55–1.31 mm). The Pearson correlation coefficient computed to determine agreement between automated and manual segmentation measurements was 0.97 (95% CI 0.96–0.98). CONCLUSIONS The described Viz.ai SDH CNN performed exceptionally well at identifying and quantifying key features of SDHs in an independent validation imaging data set.

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