Fluid structure Interaction analysis for rupture risk assessment in patients with middle cerebral artery aneurysms.

Accurate rupture risk assessment is essential for optimizing treatment decisions in patients with cerebral aneurysms. While computational fluid dynamics (CFD) has provided critical insights into aneurysmal hemodynamics, most analyses focus on blood flow patterns, neglecting the biomechanical properties of the aneurysm wall. To address this limitation, we applied Fluid-Structure Interaction (FSI) analysis, an integrative approach that simulates the dynamic interplay between hemodynamics and wall mechanics, offering a more comprehensive risk assessment.

Machine learning prediction of unexpected readmission or death after discharge from intensive care: A retrospective cohort study.

Background: Intensive care units (ICUs) harbor the sickest patients with the utmost needs of medical care. Discharge from ICU needs to consider the reason for admission and stability after ICU care. Organ dysfunction or instability after ICU discharge constitute potentially life-threatening situations for patients.

Machine learning based outcome prediction of microsurgically treated unruptured intracranial aneurysms.

Machine learning (ML) has revolutionized data processing in recent years. This study presents the results of the first prediction models based on a long-term monocentric data registry of patients with microsurgically treated unruptured intracranial aneurysms (UIAs) using a temporal train-test split. Temporal train-test splits allow to simulate prospective validation, and therefore provide more accurate estimations of a model's predictive quality when applied to future patients.

Using emergency department triage for machine learning-based admission and mortality prediction.

Aims: Patient admission is a decision relying on sparsely available data. This study aims to provide prediction models for discharge versus admission for ward observation or intensive care, and 30 day-mortality for patients triaged with the Manchester Triage System.

Methods: This is a single-centre, observational, retrospective cohort study from data within ten minutes of patient presentation at the interdisciplinary emergency department of the Kepler University Hospital, Linz, Austria.

Workflow assessment of an augmented reality application for planning of perforator flaps in plastic reconstructive surgery: Game or game changer?

Objective: In contrast to the rising amount of financial investments for research and development in medical technology worldwide is the lack of usability and clinical readiness of the produced systems. We evaluated an augmented reality (AR) setup under development for preoperative perforator vessel mapping for elective autologous breast reconstruction.

Methods: In this grant-supported research pilot, we used magnetic resonance angiography data (MR-A) of the trunk to superimpose the scans on the corresponding patients with hands-free AR goggles to identify regions-of-interest for surgical planning.