Hematoma expansion prediction in patients with intracerebral hemorrhage using a deep learning approach

Abstract

Background: Intracerebral hemorrhage (ICH) constitutes a life-threatening medical emergency characterized by a high mortality rate. The precise prediction of hematoma expansion (HE) in individuals with ICH is crucial for guiding clinical decision-making. However, we lack a standardized automated system that harnesses artificial intelligence for the timely and accurate prediction of HE in ICH cases, particularly when non-contrast computed tomography (NCCT) imaging is employed in emergency settings. Therefore, we developed a deep learning-based methodology NCCT for the purpose of HE prediction.

Methods: Our deep learning model automatically segments ICHs and stratifies them using NCCT data. We comprehensively investigated various input methods and deep learning algorithms to enhance the predictive performance of our model.

Results: The model demonstrated a competitive performance, with a notable improvement evident when using volumetric NCCT data and emphasizing slices containing hemorrhagic regions. Among established deep learning algorithms, the modified Swin-UNETER model emerges as a promising performer (accuracy: 0.74, precision: 0.76, and specificity: 0.90).

Conclusions: Collectively, we present a novel approach to HE in patients with ICH by employing deep learning and NCCT data. The capacity of the model for automated ICH segmentation and its improved predictive accuracy with volumetric NCCT data highlight its potential clinical utility. These findings contribute to advancing early HE prediction and providing valuable insights to enhance patient care and outcomes.