Background And Purpose: Patients with mild cognitive impairment and subcortical cerebrovascular disease (svMCI) can be isolated using criteria modified from those of Erkinjuntti et al. for subcortical vascular dementia and have poorer outcomes (cognitive deterioration, disability, institutionalization, and mortality). The aim of this study was to test which of the core (dysexecutive syndrome with relative sparing of memory, gait disorders and extrapyramidal signs) and supporting (urinary and behavioral symptoms) clinical features are most useful to recognize patients with svMCI and discriminate them from those with amnestic MCI (aMCI).
Methods: Twenty-nine svMCI and 14 aMCI patients were seen in a memory clinic. Tests and scales assessing core and supporting features that independently contributed to the discrimination between svMCI and aMCI were identified with stepwise logistic regression analysis. The accuracy of the discrimination was estimated with area under the receiver operating characteristic curve and 95% confidence intervals (CIs).
Results: The most accurate scales were the extrapyramidal sign scale by Richards et al. (0.75, 95% CI 0.61-0.89), letter fluency (0.75, 95% CI 0.61-0.90), irritability of the Neuropsychiatric Inventory and urinary dependence (0.66, 95% CI 0.49-0.82 for both), and digit span forward (0.59, 95% CI 0.41-0.77). The overall accuracy of a model compounding information from main and supporting features was 0.98, 95% CI 0.94-1.0.
Conclusions: All the domains that are included in the clinical criteria for svMCI independently contribute to the identification of the condition. These criteria can be useful to recognize svMCI patients in clinical settings.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1159/000083499 | DOI Listing |
JMIR Form Res
January 2025
Department of Psychology, The University of Texas at San Antonio, San Antonio, TX, United States.
Background: Perception-related errors comprise most diagnostic mistakes in radiology. To mitigate this problem, radiologists use personalized and high-dimensional visual search strategies, otherwise known as search patterns. Qualitative descriptions of these search patterns, which involve the physician verbalizing or annotating the order he or she analyzes the image, can be unreliable due to discrepancies in what is reported versus the actual visual patterns.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Univ Rennes, CHU Rennes, INSERM, LTSI - UMR 1099, F-35000 Rennes, France.
Background: To reduce the mortality related to bladder cancer, efforts need to be concentrated on early detection of the disease for more effective therapeutic intervention. Strong risk factors (eg, smoking status, age, professional exposure) have been identified, and some diagnostic tools (eg, by way of cystoscopy) have been proposed. However, to date, no fully satisfactory (noninvasive, inexpensive, high-performance) solution for widespread deployment has been proposed.
View Article and Find Full Text PDFSouth Asia has high prevalence rates of type 2 diabetes (T2D). Until the 1990s, the prevalence of T2D within South Asia was low but much higher in the South Asian diaspora living abroad. Today, high prevalence rates of T2D are reported among those living in South Asia.
View Article and Find Full Text PDFRetina
January 2025
Shiley Eye Institute, University of California, San Diego, CA, USA.
Purpose: To characterize retinal vessel whitening (RVW) in Retinitis Pigmentosa (RP).
Methods: Single-center cross-sectional study. Review of clinical notes of clinically confirmed RP patients was performed followed by grading ultra-widefield imaging.
Biomed Phys Eng Express
January 2025
Faculty of Information Technology, Beijing University of Technology, Beijing, People's Republic of China.
In fundus images, precisely segmenting retinal blood vessels is important for diagnosing eye-related conditions, such as diabetic retinopathy and hypertensive retinopathy or other eye-related disorders. In this work, we propose an enhanced U-shaped network with dual-attention, named DAU-Net, divided into encoder and decoder parts. Wherein, we replace the traditional convolutional layers with ConvNeXt Block and SnakeConv Block to strengthen its recognition ability for different forms of blood vessels while lightweight the model.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!