Flow reversal during stroke thrombectomy.

When performing mechanical thrombectomy for stroke patients, some physicians use balloon guide catheters (BGCs) in order to achieve flow reversal and thereby improve reperfusion quality. There is substantial evidence favoring the use of BGCs to improve reperfusion rates and clinical outcomes for thrombectomy patients; however, as we will outline in this review, there is also evidence that BGCs do not achieve reliable flow reversal in many circumstances. Therefore, if we are able to modify our techniques to improve the likelihood of flow reversal during thrombectomy maneuvers, we may be able to further improve reperfusion quality and clinical outcomes. This paper provides an overview of concepts on this topic and outlines some potential techniques to facilitate flow reversal more consistently, including a method to visually confirm it, with the aim of making iterative improvements towards optimal reperfusion for stroke patients.

Automated pneumothorax triaging in chest X-rays in the New Zealand population using deep-learning algorithms.

INTRODUCTION: The primary aim was to develop convolutional neural network (CNN)-based artificial intelligence (AI) models for pneumothorax classification and segmentation for automated chest X-ray (CXR) triaging. A secondary aim was to perform interpretability analysis on the best-performing candidate model to determine whether the model's predictions were susceptible to bias or confounding. METHOD: A CANDID-PTX dataset, that included 19,237 anonymized and manually labelled CXRs, was used for training and testing candidate models for pneumothorax classification and segmentation. Evaluation metrics for classification performance included Area under the receiver operating characteristic curve (AUC-ROC), sensitivity and specificity, whilst segmentation performance was measured using mean Dice and true-positive (TP)-Dice coefficients. Interpretability analysis was performed using Grad-CAM heatmaps. Finally, the best-performing model was implemented for a triage simulation. RESULTS: The best-performing model demonstrated a sensitivity of 0.93, specificity of 0.95 and AUC-ROC of 0.94 in identifying the presence of pneumothorax. A TP-Dice coefficient of 0.69 is given for segmentation performance. In triage simulation, mean reporting delay for pneumothorax-containing CXRs is reduced from 9.8 ± 2 days to 1.0 ± 0.5 days (P-value < 0.001 at 5% significance level), with sensitivity 0.95 and specificity of 0.95 given for the classification performance. Finally, interpretability analysis demonstrated models employed logic understandable to radiologists, with negligible bias or confounding in predictions. CONCLUSION: AI models can automate pneumothorax detection with clinically acceptable accuracy, and potentially reduce reporting delays for urgent findings when implemented as triaging tools.