To design and develop artificial intelligence (AI) hydrocephalus (HYC) imaging diagnostic model using a transfer learning algorithm and evaluate its application in the diagnosis of HYC by non-contrast material-enhanced head computed tomographic (CT) images. A training and validation dataset of non-contrast material-enhanced head CT examinations that comprised of 1000 patients with HYC and 1000 normal people with no HYC accumulating to 28,500 images. Images were pre-processed, and the feature variables were labeled. The feature variables were extracted by the neural network for transfer learning. AI algorithm performance was tested on a separate dataset containing 250 examinations of HYC and 250 of normal. Resident, attending and consultant in the department of radiology were also tested with the test sets, their results were compared with the AI model. Final model performance for HYC showed 93.6% sensitivity (95% confidence interval: 77%, 97%) and 94.4% specificity (95% confidence interval: 79%, 98%), with area under the characteristic curve of 0.93. Accuracy rate of model, resident, attending, and consultant were 94.0%, 93.4%, 95.6%, and 97.0%. AI can effectively identify the characteristics of HYC from CT images of the brain and automatically analyze the images. In the future, AI can provide auxiliary diagnosis of image results and reduce the burden on junior doctors.
Objective:Current methods for the diagnosis of ventriculoperitoneal (VP) shunt malfunction lack specific standards; therefore, it may be missed or misdiagnosed. Hence, providing a reliable diagnostic method will help improve the accuracy of preoperative decision-making. Therefore, the aim of the study was to provide a new method for the diagnosis of VP shunt malfunction.Methods:After in vitro testing, we enrolled a total of 12 patients with VP shunt malfunction. Before revision surgery, 0.1 mL of a 5% sodium valproate (SV) solution was injected into the reservoir; 0.1 mL of the cerebrospinal fluid (CSF) was withdrawn 20 minutes later from the reservoir to measure the SV concentration. The process was repeated on the seventh day after surgery and compared with the preoperative results.Results:The mean ± standard deviation preoperative SV concentration in the cerebrospinal fluid was greater than the postoperative concentration (5967.8 ± 1281.3 vs 391.1 ± 184.6 μg/mL, P = .001).Conclusion:The proposed method is a reliable, safe, and relatively simple alternative for the diagnosis of VP shunt malfunction and further provides a reference for treatment.
Background: Phase-contrast cine magnetic resonance imaging (PC-MRI) has been used to measure cerebrospinal fluid (CSF) flow dynamics, but the influence of the area of the aqueduct and region of interest (ROI) on quantification of stroke volume (SV) has not been assessed. Purpose: To assess the influence of the area of the ROI in quantifying the aqueductal SV measured with PC-MRI within the cerebral aqueduct. Material and Methods: Nine healthy volunteers (mean age = 29.6 years) were enrolled in the study, and brain MRI examinations were performed on a 3.0-T system. Quantitative analysis of the aqueductal CSF flow was performed using manual ROI placement. ROIs were separately drawn for each of the 12 phases of the cardiac cycle, and changes in aqueduct size during the cardiac cycle were determined. The SV was calculated using 12 different aqueductal ROIs and compared with the SV calculated using a fixed ROI size. Results: There was variation in the size of the aqueduct during the cardiac cycle. In addition, the measured SV increased with a greater area of the ROI. A significant difference in the calculated SVs with the 12 variable ROIs was observed compared with that using a fixed ROI throughout the cardiac cycle. Conclusion: To establish reliable reference values for the SV in future studies, a variable ROI should be considered.
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