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Articles Published Processes
8/4/2025 12:11:27 PM | Browse: 6 | Download: 26
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Received |
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2025-01-27 07:25 |
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Peer-Review Started |
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2025-02-05 01:25 |
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To Make the First Decision |
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Return for Revision |
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2025-03-20 06:55 |
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Revised |
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2025-03-23 13:37 |
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Second Decision |
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2025-04-16 02:49 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2025-04-16 08:15 |
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Articles in Press |
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2025-04-16 08:15 |
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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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2025-05-30 12:51 |
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Publish the Manuscript Online |
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2025-08-04 12:11 |
| ISSN |
2222-0682 (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: http://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2025. 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 |
Dentistry, Oral Surgery & Medicine |
| Manuscript Type |
Systematic Reviews |
| Article Title |
Artificial intelligence for early diagnosis and risk prediction of periodontal-systemic interactions: Clinical utility and future directions
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Neelam Das, Keertana R Gade and Pavan K Addanki |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Neelam Das, Associate Professor, Department of Periodontology, Sri Sai College of Dental Surgery, 1-2-64/1 and 2, Kothrepally, Alampally, Vikarabad 501102, Telangana, India. dasneelam423@gmail.com |
| Key Words |
Artificial intelligence; Early diagnosis; Risk prediction; Periodontal-systemic interactions; Type 2 diabetes mellitus; Hypertension; Pancreatic cancer; Artificial intelligence in healthcare; Systematic review |
| Core Tip |
This article evaluates the impact of artificial intelligence (AI) in diagnosing and predicting periodontal-systemic interactions from 2010 to 2024. AI models integrating multi-omics data and imaging techniques like cone beam computed tomography and magnetic resonance imaging improved diagnostic accuracy (up to 92%) and reduced diagnostic time by 40%. cone beam computed tomography reduced diagnostic errors by 35%, while magnetic resonance imaging enhanced soft-tissue evaluation by 25%. AI-driven approaches improved predictive accuracy by 25%, highlighting the value of multi-omics integration and advanced imaging in enhancing precision healthcare and early disease management. |
| Publish Date |
2025-08-04 12:11 |
| Citation |
<p>Das N, Gade KR, Addanki PK. Artificial intelligence for early diagnosis and risk prediction of periodontal-systemic interactions: Clinical utility and future directions. <i>World J Methodol</i> 2025; 15(4): 105516</p> |
| URL |
https://www.wjgnet.com/2222-0682/full/v15/i4/105516.htm |
| DOI |
https://dx.doi.org/10.5662/wjm.v15.i4.105516 |
© 2004-2025 Baishideng Publishing Group Inc. All rights reserved. 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
California Corporate Number: 3537345
