Letter to the Editor: Traditional medical image interpretation and deep learning-based image analysis in predicting risk in patients with spontaneous intracerebral hemorrhage

doi:10.4329/wjr.v18.i5.118969

Advanced Search

BPG is committed to discovery and dissemination of knowledge

Home / Archive / Volume 18, Issue 5

This Article

(0) (0) (0) (0) (9)

Peer-Review Report of This Article

CrossCheck and Google Search of This Article

Academic Rules and Norms of This Article

Citation of this article

Corresponding Author of This Article

Research Domain of This Article

Article-Type of This Article

Open-Access Policy of This Article

Times Cited Counts in Google of This Article

Journal Information of This Article

Publication Name

World Journal of Radiology

ISSN

1949-8470

Publisher of This Article

Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

Correspondence

Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

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

Core Tip

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.