State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, #237 Luoyu Road, Hongshan District, Wuhan, China.
Center for Orthodontics and Pediatric Dentistry at Optics Valley Branch, School & Hospital of Stomatology, Wuhan University, Wuhan, China.
Sci Rep. 2024 Aug 26;14(1):19780. doi: 10.1038/s41598-024-70311-y.
Gingival inflammation grade serves as a well-established index in periodontitis. The aim of this study was to develop a deep learning network utilizing a novel feature extraction method for the automatic assessment of gingival inflammation. T-distributed Stochastic Neighbor Embedding (t-SNE) was utilized for dimensionality reduction. A convolutional neural network (CNN) model based on DenseNet was developed for the identification and evaluation of gingival inflammation. To enhance the performance of the deep learning (DL) model, a novel teeth removal algorithm was implemented. Additionally, a Grad-CAM + + encoder was applied to generate heatmaps for computer visual attention analysis. The mean Intersection over Union (MIoU) for the identification of gingivitis was 0.727 ± 0.117. The accuracy rates for the five inflammatory degrees were 77.09%, 77.25%, 74.38%, 73.68% and 79.22%. The Area Under the Receiver Operating Characteristic (AUROC) values were 0.83, 0.80, 0.81, 0.81 and 0.84, respectively. The attention ratio towards gingival tissue increased from 37.73% to 62.20%, and within 8 mm of the gingival margin, it rose from 21.11% to 38.23%. On the gingiva, the overall attention ratio increased from 51.82% to 78.21%. The proposed DL model with novel feature extraction method provides high accuracy and sensitivity for identifying and grading gingival inflammation.
牙龈炎症分级是牙周炎的一个成熟指标。本研究旨在开发一种利用新的特征提取方法的深度学习网络，用于自动评估牙龈炎症。采用 t 分布随机邻嵌入（t-SNE）进行降维。开发了基于 DenseNet 的卷积神经网络（CNN）模型，用于识别和评估牙龈炎症。为了提高深度学习（DL）模型的性能，实现了一种新的牙齿去除算法。此外，应用 Grad-CAM + 编码器生成热图，进行计算机视觉注意力分析。识别牙龈炎的平均交并比（MIoU）为 0.727 ± 0.117。五个炎症程度的准确率分别为 77.09%、77.25%、74.38%、73.68%和 79.22%。接收器操作特征（AUROC）曲线下的面积分别为 0.83、0.80、0.81、0.81 和 0.84。对牙龈组织的关注比例从 37.73%增加到 62.20%，在牙龈边缘 8mm 范围内，从 21.11%增加到 38.23%。在牙龈上，整体关注比例从 51.82%增加到 78.21%。提出的具有新特征提取方法的深度学习模型对识别和分级牙龈炎症具有较高的准确性和敏感性。
