The aim of this study was to evaluate whether machine learning algorithms allow for the prediction of Child-Pugh classification on clinical multiphase computed tomography (CT). The authors found that the performance of convolutional neural networks (CNN) is comparable to that of experienced radiologists in assessing Child-Pugh class based on multiphase abdominal CT.
Key points
- Established machine learning algorithms can predict the Child-Pugh class of a liver based on a clinical multiphase computed tomography.
- The predictive performance of a convolutional neural network in assessing liver parenchyma has the potential to be comparable to that of experienced radiologists.
- Machine learning algorithms, in particular convolutional neural networks, may constitute an adjunct quantitative and objective tool to assess the functional liver status based on imaging information.
Article: Multiphase CT-based prediction of Child-Pugh classification: a machine learning approach
Authors: Johannes Thüring, Oliver Rippel, Christoph Haarburger, Dorit Merhof, Philipp Schad, Philipp Bruners, Christiane K. Kuhl & Daniel Truhn
