10 research outputs found
Explainable artificial intelligence model to predict acute critical illness from electronic health records
We developed an explainable artificial intelligence (AI) early warning score
(xAI-EWS) system for early detection of acute critical illness. While
maintaining a high predictive performance, our system explains to the clinician
on which relevant electronic health records (EHRs) data the prediction is
grounded. Acute critical illness is often preceded by deterioration of
routinely measured clinical parameters, e.g., blood pressure and heart rate.
Early clinical prediction is typically based on manually calculated screening
metrics that simply weigh these parameters, such as Early Warning Scores (EWS).
The predictive performance of EWSs yields a tradeoff between sensitivity and
specificity that can lead to negative outcomes for the patient. Previous work
on EHR-trained AI systems offers promising results with high levels of
predictive performance in relation to the early, real-time prediction of acute
critical illness. However, without insight into the complex decisions by such
system, clinical translation is hindered. In this letter, we present our
xAI-EWS system, which potentiates clinical translation by accompanying a
prediction with information on the EHR data explaining it
SHARQnet – Sophisticated harmonic artifact reduction in quantitative susceptibility mapping using a deep convolutional neural network
Quantitative susceptibility mapping (QSM) reveals pathological changes in widespread diseases such as Parkinson's disease, Multiple Sclerosis, or hepatic iron overload. QSM requires multiple processing steps after the acquisition of magnetic resonance imaging (MRI) phase measurements such as unwrapping, background field removal and the solution of an ill-posed field-to-source-inversion. Current techniques utilize iterative optimization procedures to solve the inversion and background field correction, which are computationally expensive and lead to suboptimal or over-regularized solutions requiring a careful choice of parameters that make a clinical application of QSM challenging. We have previously demonstrated that a deep convolutional neural network can invert the magnetic dipole kernel with a very efficient feed forward multiplication not requiring iterative optimization or the choice of regularization parameters. In this work, we extended this approach to remove background fields in QSM. The prototype method, called SHARQnet, was trained on simulated background fields and tested on 3T and 7T brain datasets. We show that SHARQnet outperforms current background field removal procedures and generalizes to a wide range of input data without requiring any parameter adjustments. In summary, we demonstrate that the solution of ill-posed problems in QSM can be achieved by learning the underlying physics causing the artifacts and removing them in an efficient and reliable manner and thereby will help to bring QSM towards clinical applications
Explainable artificial intelligence model to predict acute critical illness from electronic health records
Acute critical illness is often preceded by deterioration of routinely measured clinical parameters, e.g., blood pressure and heart rate. Here, the authors develop an explainable artificial intelligence early warning score system for its early detection