School of Software, Shandong University, Shandong, 250101, China.
School of Nursing and Rehabilitation, Cheeloo College of Medicine, Shandong University, Shandong, 250012, China.
Clin Oral Investig. 2024 Sep 20;28(10):534. doi: 10.1007/s00784-024-05921-x.
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.
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.
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.
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.
Application of artificial intelligence to the evaluation of dental plaque distribution could enhance diagnostic accuracy and treatment efficiency and accuracy.
治疗牙菌斑的选择与不同牙齿上菌斑的状况密切相关。本研究验证了 CNN 模型在评估牙菌斑指数方面的能力。
在 70 名（20 名男性，50 名女性）健康成年人（18 至 55 岁）中，获得了菌斑显色剂染色的恒牙和乳牙的前视图和侧视图口腔内图像（210 张）。采用三阶段方法，首先使用 You Look Only Once 版本 8（YOLOv8）模型检测目标牙齿，然后使用基于提示的 Segment Anything Model（SAM）分割算法分割牙齿。应用两阶段方法后，获得了一个由 1400 张照片组成的新单牙数据集。最后，根据 Quigley-Hein 指数（QHI）评分系统，在基于 DeepPlaq 的多类分类模型上训练和评估牙菌斑指数的准确性。使用准确率、召回率、精度和 F1 分数来衡量分类性能。
牙齿探测器在识别带有菌斑显色剂的牙齿时，准确度（平均精度，mAP）约为 0.941±0.005。通过 DeepPlaq 进行牙菌斑指数评估的最大准确率为 0.84（DeepPlaq 与专家评分相同的概率），评分在 0 到 5 之间的平均评分误差小于 0.25。
三阶段方法在检测和分割目标牙齿方面表现出优异的性能，DeepPlaq 模型在评估牙菌斑指数方面也表现出很强的性能。
人工智能在评估牙菌斑分布方面的应用可以提高诊断准确性和治疗效率和准确性。
