Peer-Reviewed Publication
J Periodontol2026March 9, 2026Journal Article
Usability of a deep learning platform for detecting radiographic bone loss and furcation involvement.
Chun-Teh Lee1, Tanjida Kabir2, Alaa Alsaffar1,3, Jiman Nelson1, Sally Sheng1, Hsiu-Wan Meng1, Mohammad Qali4,5,6, Muhammad F Walji2,7, Xiaoqian Jiang2, Shayan Shams2
1Department of Periodontics and Dental Hygiene, The University of Texas Health Science Center at Houston School of Dentistry, Houston, Texas, USA.
2Department of Health Data Science and Artificial Intelligence, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
3Kuwait Ministry of Health, Kuwait City, Kuwait.
4Department of Surgical Sciences, College of Dentistry, Kuwait University, Kuwait City, Kuwait.
5Department of Orthodontics, School of Dental Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
6Department of Periodontology, Maurice H. Kornberg School of Dentistry, Temple University, Philadelphia, Pennsylvania, USA.
7Department of Diagnostic and Biomedical Sciences, The University of Texas Health Science Center at Houston School of Dentistry, Houston, Texas, USA.
Abstract
BACKGROUND: Assessing radiographic bone condition is important for periodontal diagnosis. The accuracy of radiographic interpretation depends highly on a clinician's experience and knowledge. This study aimed to develop a deep learning-based online platform that aids clinicians in diagnosing periodontitis based on periapical radiographs and to evaluate the platform's usability using a validated su…
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