Wang Q, Yang JS. Letter to the Editor: Traditional medical image interpretation and deep learning-based image analysis in predicting risk in patients with spontaneous intracerebral hemorrhage. World J Radiol 2026; 18(5): 118969 [DOI: 10.4329/wjr.v18.i5.118969]
Corresponding Author of This Article
Jian-She Yang, Department of Nuclear Medicine and Oncology Research, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, No. 301 Yanchang Road, Shanghai 200072, China. 2305499@tongji.edu.cn
Research Domain of This Article
Radiology, Nuclear Medicine & Medical Imaging
Article-Type of This Article
Correspondence
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Share the Article
Wang Q, Yang JS. Letter to the Editor: Traditional medical image interpretation and deep learning-based image analysis in predicting risk in patients with spontaneous intracerebral hemorrhage. World J Radiol 2026; 18(5): 118969 [DOI: 10.4329/wjr.v18.i5.118969]
World J Radiol. May 28, 2026; 18(5): 118969 Published online May 28, 2026. doi: 10.4329/wjr.v18.i5.118969
Letter to the Editor: Traditional medical image interpretation and deep learning-based image analysis in predicting risk in patients with spontaneous intracerebral hemorrhage
Qiang Wang, Jian-She Yang
Qiang Wang, Jian-She Yang, Basic Medical School, Gansu Medical College, Pingliang 744000, Gansu Province, China
Jian-She Yang, Department of Nuclear Medicine and Oncology Research, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai 200072, China
Author contributions: Yang JS and Wang Q contributed to the discussion and design of the manuscript, writing and editing of the manuscript, illustration, and literature review; Yang JS designed the overall concept and outline of the manuscript. All authors approved the final manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Jian-She Yang, Department of Nuclear Medicine and Oncology Research, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, No. 301 Yanchang Road, Shanghai 200072, China. 2305499@tongji.edu.cn
Received: January 16, 2026 Revised: February 22, 2026 Accepted: March 31, 2026 Published online: May 28, 2026 Processing time: 132 Days and 0.6 Hours
Abstract
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.
Core Tip: In this article, we discuss the interpretation of medical images for predicting early hematoma enlargement in spontaneous intracerebral hemorrhage based on radiological features extracted through deep learning and traditional manual interpretation. We argue that artificial intelligence-based computer-aided diagnostic methods used to predict hematoma enlargement in spontaneous intracerebral hemorrhage on computed tomography images can help clinicians identify patients who would benefit from positive surgical intervention soon after admission.
