DeepPlaq: Dental plaque indexing based on deep neural networks.

Autor: Chen X; School of Software, Shandong University, Shandong, 250101, China., Shen Y; School of Software, Shandong University, Shandong, 250101, China., Jeong JS; School of Nursing and Rehabilitation, Cheeloo College of Medicine, Shandong University, Shandong, 250012, China., Perinpanayagam H; Schulich School of Medicine & Dentistry, University of Western Ontario, London, ON, Canada., Kum KY; Department of Conservative Dentistry, Dental Research Institute, National Dental Care Center for the Disabled, Seoul National University Dental Hospital, Seoul National University School of Dentistry, 03080 101 Deahak-Ro, Jondro-Gu, Seoul, Republic of Korea., Gu Y; Department of Endodontics, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong Key Laboratory of Oral Tissue Regeneration & Shandong Engineering Research Center of Dental Materials and Oral Tissue Regeneration & Shandong Provincial Clinical Research Center for Oral Diseases, Shandong University, No. 44 Wenhua Road West, Jinan, 250012, Shandong, China. guyu@sdu.edu.cn.
Jazyk: angličtina
Zdroj: Clinical oral investigations [Clin Oral Investig] 2024 Sep 20; Vol. 28 (10), pp. 534. Date of Electronic Publication: 2024 Sep 20.
DOI: 10.1007/s00784-024-05921-x
Abstrakt: Objectives: The selection of treatment for dental plaque is closely related to the condition of the plaque on different teeth. This study validated the ability of CNN models in assessing the dental plaque indices.
Materials and Methods: In 70 (20 male and 50 female) healthy adults (18 to 55 years old), frontal and lateral view intraoral images (210) of plaque disclosing agent stained permanent and deciduous dentitions were obtained. A three-stage method was employed, where the You Look Only Once version 8 (YOLOv8) model was first used to detect the target teeth, followed by the prompt-based Segment Anything Model (SAM) segmentation algorithm to segment teeth. A new single-tooth dataset consisting of 1400 photographs was obtained after applying a two-stage method. Finally, a multi-class classification model DeepPlaq was trained and evaluated on the accuracy of dental plaque indexing based on the Quigley-Hein Index (QHI) scoring system. Classification performance was measured using accuracy, recall, precision, and F1-score.
Results: The teeth detector exhibited an accuracy (mean average precision, mAP) of approximately 0.941 ± 0.005 in identifying teeth with plaque disclosing agents. The maximum accuracy attained in the plaque indexing through DeepPlaq was 0.84 (probability that DeepPlaq scored identical to experts), and the smallest average scoring error was less than 0.25 on a 0 to 5 scale for scoring.
Conclusions: A three-stage approach demonstrated excellent performance in detecting and segmenting target teeth, and DeepPlaq model also showed strong performance in assessing dental plaque indices.
Clinical Relevance: Application of artificial intelligence to the evaluation of dental plaque distribution could enhance diagnostic accuracy and treatment efficiency and accuracy.
(© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)
Databáze: MEDLINE
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