Publications by authors named "Boris Pfahringer"
Corrigendum to "Detection and analysis of cerebral aneurysms based on X-ray rotational angiography - the CADA 2020 challenge" [Medical Image Analysis, April 2022, Volume 77, 102333].
Med Image Anal
February 2025
Background: The Society for Cardiovascular Angiography and Interventions (SCAI) shock classification has been shown to provide robust mortality risk stratification in a variety of cardiovascular patients.
Objectives: This study sought to evaluate the SCAI shock classification in postoperative cardiac surgery intensive care unit (CSICU) patients.
Methods: This study retrospectively analyzed 26,792 postoperative CSICU admissions at a heart center between 2012 and 2022.
Article Synopsis
- The CADA challenge aimed to improve algorithms for detecting and analyzing cerebral aneurysms in 3D rotational angiography images by providing training on 109 anonymized datasets and testing on 22 additional ones.
- Participants from 22 countries created detection solutions primarily using U-Net, achieving a high F2 score of 0.92, which is comparable to expert performance, though smaller aneurysms were sometimes missed.
- The challenge also assessed rupture risk estimation, with the best methods combining various parameters to achieve an F2 score of 0.70, closely matching the 0.71 score when using expert-defined structures.
Despite advances in machine learning-based clinical prediction models, only few of such models are actually deployed in clinical contexts. Among other reasons, this is due to a lack of validation studies. In this paper, we present and discuss the validation results of a machine learning model for the prediction of acute kidney injury in cardiac surgery patients initially developed on the MIMIC-III dataset when applied to an external cohort of an American research hospital.
View Article and Find Full Text PDFAcute kidney injury (AKI) is a major complication after cardiothoracic surgery. Early prediction of AKI could prompt preventive measures, but is challenging in the clinical routine. One important reason is that the amount of postoperative data is too massive and too high-dimensional to be effectively processed by the human operator.
View Article and Find Full Text PDFBackground: The large amount of clinical signals in intensive care units can easily overwhelm health-care personnel and can lead to treatment delays, suboptimal care, or clinical errors. The aim of this study was to apply deep machine learning methods to predict severe complications during critical care in real time after cardiothoracic surgery.
Methods: We used deep learning methods (recurrent neural networks) to predict several severe complications (mortality, renal failure with a need for renal replacement therapy, and postoperative bleeding leading to operative revision) in post cardiosurgical care in real time.