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Articles Published Processes
3/2/2023 1:29:51 PM | Browse: 152 | Download: 489
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Received |
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2022-11-27 05:19 |
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Peer-Review Started |
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2022-11-27 05:21 |
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To Make the First Decision |
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Return for Revision |
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2023-01-19 08:47 |
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Revised |
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2023-01-27 04:47 |
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Second Decision |
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2023-02-13 04:06 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2023-02-13 07:16 |
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Articles in Press |
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2023-02-13 07:16 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2023-02-21 05:44 |
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Publish the Manuscript Online |
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2023-03-02 13:29 |
| ISSN |
2307-8960 (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 |
©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved. |
| 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 |
Orthopedics |
| Manuscript Type |
Retrospective Study |
| Article Title |
Deep learning-assisted diagnosis of femoral trochlear dysplasia based on magnetic resonance imaging measurements
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Sheng-Ming Xu, Dong Dong, Wei Li, Tian Bai, Ming-Zhu Zhu and Gui-Shan Gu |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Gui-Shan Gu, MD, Professor, Department of Orthopedic Surgery, The First Hospital of Jilin University, No. 71 Xinmin Street, Changchun 130000, Jilin Province, China. gugs@jlu.edu.cn |
| Key Words |
Femoral trochlear dysplasia; Deep learning; Artificial intelligence; Magnetic resonance imaging; Diagnosis |
| Core Tip |
Femoral trochlear dysplasia is an important risk factor for patellar instability. MRI has become the preferred method for evaluating femoral trochlear dysplasia. However, manually measuring femoral trochlea parameters on magnetic resonance imaging is tedious, time-consuming, and easily produces great variability. In this work, we propose an assisted diagnosis algorithm framework based on deep learning technology, which can quickly and accurately distinguish whether there is trochlear dysplasia in the femur. All values (The accuracy, sensitivity, specificity, etc.) were superior to junior doctors and intermediate doctors, similar to senior doctors. Our model is beneficial to both orthopedic surgeons and radiologists, especially, the young front-line clinicians with less experience. |
| Publish Date |
2023-03-02 13:29 |
| Citation |
Xu SM, Dong D, Li W, Bai T, Zhu MZ, Gu GS. Deep learning-assisted diagnosis of femoral trochlear dysplasia based on magnetic resonance imaging measurements. World J Clin Cases 2023; 11(7): 1477-1487 |
| URL |
https://www.wjgnet.com/2307-8960/full/v11/i7/1477.htm |
| DOI |
https://dx.doi.org/10.12998/wjcc.v11.i7.1477 |
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