Letter to the Editor: Traditional medical image interpretation and deep learning-based image analysis in predicting...

We read with great interest the study by Yang and Li in World Journal of Radiology entitled "Deep learning-based imaging model to predict early hematoma enlargement and hospital mortality in spontaneous intracerebral hemorrhage". The advantages and disadvantages of computed tomography, magnetic resonance imaging, and deep-learning models in predicting hematoma expansion and mortality risk in patients with spontaneous intracerebral hemorrhage are discussed in this article. Manual image interpretation remains irreplaceable in emergency settings; however, its subjectivity and limitations limit its ability to predict hematoma expansion and mortality risk. Deep learning models significantly improve prediction accuracy through automated analysis and feature extraction, providing robust support for clinical decision making. However, the data requirements and complexity of these models hinder their widespread adoption. Future research should focus on optimizing model architectures, reducing data dependency, and strengthening multicenter validation to enhance their clinical applicability. Moreover, a hybrid evaluation system that combines the strengths of conventional and deep-learning approaches has the potential to enable more precise early intervention strategies for spontaneous intracerebral hemorrhage patients.