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Articles Published Processes
5/27/2026 3:21:49 AM | Browse: 6 | Download: 33
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Received |
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2026-01-16 07:39 |
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Peer-Review Started |
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2026-01-16 07:41 |
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First Decision by Editorial Office Director |
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2026-02-12 08:24 |
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Return for Revision |
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2026-02-12 08:24 |
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Revised |
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2026-02-22 03:13 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2026-03-31 02:35 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-03-31 10:21 |
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Articles in Press |
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2026-03-31 10:21 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-05-20 02:45 |
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Publish the Manuscript Online |
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2026-05-27 03:21 |
| ISSN |
1949-8470 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (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: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Radiology, Nuclear Medicine & Medical Imaging |
| Manuscript Type |
Correspondence |
| Article Title |
Letter to the Editor: Traditional medical image interpretation and deep learning-based image analysis in predicting risk in patients with spontaneous intracerebral hemorrhage
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Qiang Wang and Jian-She Yang |
| ORCID |
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| Funding Agency and Grant Number |
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| 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 |
| Key Words |
Spontaneous intracerebral hemorrhage; Hematoma enlargement; Risk of death; Computed tomography; Magnetic resonance imaging; Deep learning model |
| Core Tip |
In this Letter, 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. |
| Publish Date |
2026-05-27 03:21 |
| Citation |
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
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| URL |
https://www.wjgnet.com/1949-8470/full/v18/i5/118969.htm |
| DOI |
https://dx.doi.org/10.4329/wjr.v18.i5.118969 |
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