Machine Learning for Onset Prediction of Patients with Intracerebral Hemorrhage

Abstract

Objective: Intracerebral hemorrhage (ICH) has a high mortality and long-term morbidity and thus has a significant overall health-economic impact. Outcomes are especially poor if the exact onset is unknown, but reliable imaging-based methods for onset estimation have not been established. We hypothesized that onset prediction of patients with ICH using artificial intelligence (AI) may be more accurate than human readers.

Material and methods: A total of 7421 computed tomography (CT) datasets between January 2007-July 2021 from the University Hospital Basel with confirmed ICH were extracted and an ICH-segmentation algorithm as well as two classifiers (one with radiomics, one with convolutional neural networks) for onset estimation were trained. The classifiers were trained based on the gold standard of 644 datasets with a known onset of >1 and <48 h. The results of the classifiers were compared to the ratings of two radiologists.

Results: Both the AI-based classifiers and the radiologists had poor discrimination of the known onsets, with a mean absolute error (MAE) of 9.77 h (95% CI (confidence interval) = 8.52-11.03) for the convolutional neural network (CNN), 9.96 h (8.68-11.32) for the radiomics model, 13.38 h (11.21-15.74) for rater 1 and 11.21 h (9.61-12.90) for rater 2, respectively. The results of the CNN and radiomics model were both not significantly different to the mean of the known onsets (p = 0.705 and p = 0.423).

Conclusions: In our study, the discriminatory power of AI-based classifiers and human readers for onset estimation of patients with ICH was poor. This indicates that accurate AI-based onset estimation of patients with ICH based only on CT-data may be unlikely to change clinical decision making in the near future. Perhaps multimodal AI-based approaches could improve ICH onset prediction and should be considered in future studies.

Keywords: artificial intelligence; intracerebral hemorrhage; machine learning; onset prediction.

Figure 1

Flowchart of inclusion of patients. Overview study design: On a subset of the original study population (1.) a hematoma segmentation model is trained (2.) which is used to generate hematoma segmentations for the entire study population (3.) Then, the radiomics-, CNN-, and human-based age prediction is performed (4.) (MAE: Mean absolute error; CNN: Convolutional Neural Network, nnU-Net: Neural network for semantic segmentation).

Figure 2

Mean density of intracerebral hemorrhage on admission imaging and association with symptom onset. HU = Hounsfield Units, age in h = time since onset in hours.