External validation of AI-based scoring systems in the ICU: a systematic review and meta-analysis.

Background: Machine learning (ML) is increasingly used to predict clinical deterioration in intensive care unit (ICU) patients through scoring systems. Although promising, such algorithms often overfit their training cohort and perform worse at new hospitals. Thus, external validation is a critical - but frequently overlooked - step to establish the reliability of predicted risk scores to translate them into clinical practice.

Deep learning-based multiclass segmentation in aneurysmal subarachnoid hemorrhage.

Introduction: Radiological scores used to assess the extent of subarachnoid hemorrhage are limited by intrarater and interrater variability and do not utilize all available information from the imaging. Image segmentation enables precise identification and delineation of objects or regions of interest and offers the potential for automatization of score assessments using precise volumetric information. Our study aims to develop a deep learning model that enables automated multiclass segmentation of structures and pathologies relevant for aneurysmal subarachnoid hemorrhage outcome prediction.

Radiofrequency ablation (RFA) in unresectable pancreatic adenocarcinoma: meta-analysis & systematic review.

Background: Pancreatic adenocarcinoma remains a challenging malignancy with a poor prognosis. Radiofrequency ablation (RFA) has emerged as a potential treatment for unresectable pancreatic adenocarcinoma (UPAC) aimed at improving survival and quality of life. This meta-analysis and systematic review evaluates the outcomes of RFA in UPAC.

Generative modeling of the Circle of Willis using 3D-StyleGAN.

The circle of Willis (CoW) is a network of cerebral arteries with significant inter-individual anatomical variations. Deep learning has been used to characterize and quantify the status of the CoW in various applications for the diagnosis and treatment of cerebrovascular disease. In medical imaging, the performance of deep learning models is limited by the diversity and size of training datasets.