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10/31/2024 10:55:01 AM | Browse: 86 | Download: 264
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Received |
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2024-08-16 08:17 |
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Peer-Review Started |
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2024-07-20 16:05 |
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To Make the First Decision |
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Return for Revision |
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2024-09-19 04:44 |
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Revised |
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2024-09-23 13:02 |
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Second Decision |
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2024-10-24 02:43 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2024-10-24 11:12 |
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Articles in Press |
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2024-10-24 11:12 |
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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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2024-10-25 11:11 |
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Publish the Manuscript Online |
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2024-10-31 07:40 |
| ISSN |
2220-315x (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) 2024. 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 |
Radiology, Nuclear Medicine & Medical Imaging |
| Manuscript Type |
Retrospective Study |
| Article Title |
HIPPO artificial intelligence: Correlating automated radiographic femoroacetabular measurements with patient-reported outcomes in developmental hip dysplasia
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Ahmed Alshaikhsalama, Holden Archer, Yin Xi, Richard Ljuhar, Joel E Wells and Avneesh Chhabra |
| Funding Agency and Grant Number |
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| Corresponding Author |
Ahmed Alshaikhsalama, Department of Radiology, University of Texas Southwestern, 5323 Harry Hines Blvd, Dallas, TX 75390, United States. ahmed.alshaikhsalama@utsouthwestern.edu |
| Key Words |
Hip dysplasia; Patient reported outcome measures; Deep-learning; Artificial intelligence; Radiographs; Lateral center edge angle |
| Core Tip |
In this study, we compared an artificial intelligence (AI) tool measuring anteroposterior hip radiographs against manual readers for assessing hip dysplasia (HD) associations with patient-reported outcome measures (PROMs). The AI tool, HIPPO, efficiently generated radiographic measurements but showed poor correlations with PROMs, highlighting its current limitations in predicting clinical outcomes solely from radiological data. This indicates that while AI can aid radiographic assessments, PROMs remain crucial for capturing subjective patient experiences. The findings underscore the importance of integrating PROMs as an additional element in the clinical decision-making processes for HD, while also incorporating efficient radiographic assessment by AI tools. |
| Publish Date |
2024-10-31 07:40 |
| Citation |
<p>Alshaikhsalama A, Archer H, Xi Y, Ljuhar R, Wells JE, Chhabra A. HIPPO artificial intelligence: Correlating automated radiographic femoroacetabular measurements with patient-reported outcomes in developmental hip dysplasia. <i>World J Exp Med</i> 2024; 14(4): 99359</p> |
| URL |
https://www.wjgnet.com/2220-315x/full/v14/i4/99359.htm |
| DOI |
https://dx.doi.org/10.5493/wjem.v14.i4.99359 |
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