Improving the Robustness of Deep-Learning Models in Predicting Hematoma Expansion from Admission Head CT.

Background And Purpose: Robustness against input data perturbations is essential for deploying deep-learning models in clinical practice. Adversarial attacks involve subtle, voxel-level manipulations of scans to increase deep-learning models' prediction errors. Testing deep-learning model performance on examples of adversarial images provides a measure of robustness, and including adversarial images in the training set can improve the model's robustness.

Genal: a Python toolkit for genetic risk scoring and Mendelian randomization.

Motivation: The expansion of genetic association data from genome-wide association studies has increased the importance of methodologies like Polygenic Risk Scores (PRS) and Mendelian Randomization (MR) in genetic epidemiology. However, their application is often impeded by complex, multi-step workflows requiring specialized expertise and the use of disparate tools with varying data formatting requirements. Existing solutions are frequently standalone packages or command-line based-largely due to dependencies on tools like PLINK-limiting accessibility for researchers without computational experience.

A Hybrid Transformer-Convolutional Neural Network for Segmentation of Intracerebral Hemorrhage and Perihematomal Edema on Non-Contrast Head Computed Tomography (CT) with Uncertainty Quantification to Improve Confidence.

Intracerebral hemorrhage (ICH) and perihematomal edema (PHE) are key imaging markers of primary and secondary brain injury in hemorrhagic stroke. Accurate segmentation and quantification of ICH and PHE can help with prognostication and guide treatment planning. In this study, we combined Swin-Unet Transformers with nnU-NETv2 convolutional network for segmentation of ICH and PHE on non-contrast head CTs.

Machine Learning Models for 3-Month Outcome Prediction Using Radiomics of Intracerebral Hemorrhage and Perihematomal Edema from Admission Head Computed Tomography (CT).

: Intracerebral hemorrhages (ICH) and perihematomal edema (PHE) are respective imaging markers of primary and secondary brain injury in hemorrhagic stroke. In this study, we explored the potential added value of PHE radiomic features for prognostication in ICH patients. : Using a multicentric trial cohort of acute supratentorial ICH ( = 852) patients, we extracted radiomic features from ICH and PHE lesions on admission non-contrast head CTs.

Cost-Effectiveness Analysis of a Novel Fetal Vesicoamniotic Shunt-The Vortex Shunt.

Objective: We estimated the potential outcomes, costs, and cost-effectiveness of the Vortex shunt, a novel fetal vesicoamniotic shunt (VAS), compared to standard shunts for treating fetal lower urinary tract obstruction (LUTO).

Method: We designed a decision-analytic model comparing the Vortex shunt to current shunts using a theoretical cohort of 1000 pregnancies equivalent to the annual U.S.