Notes

Consistent information established terminology actions regarding bvFTD along with PPA diagnosis along with monitoring.
ent of OP.
Creation of an arteriovenous fistula (AVF), compared with an arteriovenous graft (AVG), is associated with longer initial catheter dependence after starting hemodialysis (HD) but longer access survival and lower long-term catheter dependence. The extent of these potential long-term benefits in elderly patients is unknown. We assessed catheter dependence after AVF or AVG placement among elderly patients who initiated HD without a permanent access in place.

Retrospective cohort study.

Patients≥67 years of age identified in the US Renal Data System who had a first AVF (n=14,532) or AVG (n=3,391) placed within 1 year after HD initiation between May 2012 and May2017.

AVF versus AVG placement in the first year of HD.

Catheter dependence after AVF or AVG placement assessed using CROWNWeb data.

Generalized estimating equations and negative binomial regression for catheter use over time and Cox proportional hazards models for mortality.

Creation of an AVF versus AVG placement was associated with greater y population initiating HD without a permanent access. As the long-term benefits in terms of catheter dependence of an AVF are not realized in many elderly patients, specific patient characteristics should be considered when making decisions regarding vascular access.
Evaluation of the lamina terminalis (LT) is crucial for non-invasive evaluation of the CSF diversion for the treatment of hydrocephalus. Together with deep learning algorithms, morphological and physiological analyses of the LT may play an important role in the management of hydrocephalus.

We aim to show that exploiting the motion of LT can contribute to the evaluation of hydrocephalus using deep learning algorithms.

The dataset contains 61 True-fisp data with routine sequences 37 of which are labeled as 'hydrocephalus' and the others as 'normal condition'. A fifteen-year experienced neuroradiologist divided data into two groups. The first group, 'hydrocephalus', consists of patients with typical MRI findings (ventriculomegaly, enlargement of the third ventricular recesses and lateral ventricular horns, decreased mamillo-pontine distance, reduced frontal horn angle, thinning/elevation of the corpus callosum, and non-dilated convexity sulci), and the second group contains samples that did not show any symptoms or neurologic abnormality and labeled as 'normal condition'. The region of interest was determined by the radiologist supervisor to cover the LT. To achieve our purpose, we used both spatial and spatio-temporal analysis with two different deep learning architectures. We utilized Convolutional Neural Networks (CNN) for spatial and Convolutional Long Short-Term Memory (ConvLSTM) models for spatio-temporal analysis using an ROI around LT on sagittal True-fisp images.

Our results show that 80.7% classification accuracy was achieved with the ConvLSTM model exploiting LT motion, whereas 76.5% and 71.6% accuracies were obtained by the 2D CNN model using all frames, and only the first frame from only spatial information, respectively.

We suggest that the motion of the LT can be used as an additional attribute to the spatial information to evaluate the hydrocephalus.
We suggest that the motion of the LT can be used as an additional attribute to the spatial information to evaluate the hydrocephalus.
Deep learning using convolutional neural networks (CNNs) has shown great promise in advancing neuroscience research. However, the ability to interpret the CNNs lags far behind, confounding their clinical translation.

We interrogated 3 heatmap-generating techniques that have increasing generalizability for CNN interpretation class activation mapping (CAM), gradient (Grad)-CAM, and Grad-CAM++. To investigate the impact of CNNs on heatmap generation, we also examined 6 different models trained to classify brain magnetic resonance imaging into 3 types relapsing-remitting multiple sclerosis (RRMS), secondary progressive MS (SPMS), and control. Further, we designed novel methods to visualize and quantify the heatmaps to improve interpretability.

Grad-CAM showed the best heatmap localizing ability, and CNNs with a global average pooling layer and pretrained weights had the best classification performance. Based on the best-performing CNN model, called VGG19, the 95th percentile values of Grad-CAM in SPMS were significantly higher than RRMS, indicating greater heterogeneity. Further, voxel-wise analysis of the thresholded Grad-CAM confirmed the difference identified visually between RRMS and SPMS in discriminative brain regions occipital versus frontal and occipital, or temporal/parietal.

No study has examined the CAM methods together using clinical images. There is also lack of study on the impact of CNN architecture on heatmap outcomes, and of technologies to quantify heatmap patterns in clinical settings.

Grad-CAM outperforms CAM and Grad-CAM++. Integrating Grad-CAM, novel heatmap quantification approaches, and robust CNN models may be an effective strategy in identifying the most crucial brain areas underlying disease development in MS.
Grad-CAM outperforms CAM and Grad-CAM++. Integrating Grad-CAM, novel heatmap quantification approaches, and robust CNN models may be an effective strategy in identifying the most crucial brain areas underlying disease development in MS.
One of the most well-validated tools for DTI data analysis is TRACULA, part of the FreeSurfer software. TRACULA automatically segments 18 major white matter (WM) tracts. selleck compound Occasionally, tracts may be only partially reconstructed, thus requiring intervention to avoid biasing analyses. A majority of studies have not reported any quality control procedures and those that have tend to discard partially reconstructed tracts from group analyses if they cannot be salvaged during TRACULA reinitialization.

We propose a semi-automated method to improve the detection and recovery of incomplete WM tracts. We detail several steps to maximize the quality of preprocessed DTI data. The steps include (1) a visual inspection of eddy current corrected diffusion weighted images and (2) an automated evaluation of color- encoded FA images; (3) assessment of the volume of each tract saved in the TRACULA output file; (4) re-processing of tracts with a volume smaller than a specified threshold; (5) minimal manual editing of the control points for tracts that remained partially reconstructed; and (6) final re-initiation of TRACULA.
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