National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University, No. 24 South Section 1, Yihuan Road, Chengdu Sichuan 610065, China.
State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, No. 17 People's South Road, Chengdu, Sichuan 610041, China.
J Dent. 2022 Jul;122:104107. doi: 10.1016/j.jdent.2022.104107. Epub 2022 Mar 24.
Periapical periodontitis and caries are common chronic oral diseases affecting most teenagers and adults worldwide. The purpose of this study was to develop an evaluation tool to automatically detect dental caries and periapical periodontitis on periapical radiographs using deep learning.
A modified deep learning model was developed using a large dataset (4129 images) with high-quality annotations to support the automatic detection of both dental caries and periapical periodontitis. The performance of the model was compared to the classification performance of dentists.
The deep learning model automatically distinguished dental caries with an F1-score of 0.829 and periapical periodontitis with an F1-score of 0.828. The comparison of model-only and expert-only detection performance showed that the accuracy of the fully automatic method was significantly higher than that of the young dentists. With deep learning assistance, the experts not only reached a higher diagnostic accuracy with an average F1-score of 0.7844 for dental caries and 0.8208 for periapical periodontitis compared to expert-only scenarios, but also increased inter-observer agreement from 0.585/0.590 to 0.726/0.713 for dental caries and from 0.623/0.563 to 0.752/0.740 for periapical periodontitis.
Based on these experimental results, deep learning can improve the accuracy and consistency of evaluating dental caries and periapical periodontitis on periapical radiographs.
Deep learning models can improve accuracy and consistency and reduce the workload of dentists, making artificial intelligence a powerful tool for clinical practice.
根尖周炎和龋齿是影响全球大多数青少年和成年人的常见慢性口腔疾病。本研究旨在开发一种评估工具，使用深度学习自动检测根尖片上的龋齿和根尖周炎。
使用带有高质量注释的大型数据集（4129 张图像）开发了一种经过修改的深度学习模型，以支持龋齿和根尖周炎的自动检测。将模型的性能与牙医的分类性能进行了比较。
深度学习模型自动区分龋齿的 F1 得分为 0.829，区分根尖周炎的 F1 得分为 0.828。模型与专家单独检测性能的比较表明，全自动方法的准确性明显高于年轻牙医。在深度学习的辅助下，专家不仅达到了更高的诊断准确性，龋齿的平均 F1 得分为 0.7844，根尖周炎的平均 F1 得分为 0.8208，而且与专家单独检测相比，龋齿的观察者间一致性从 0.585/0.590 增加到 0.726/0.713，根尖周炎的观察者间一致性从 0.623/0.563 增加到 0.752/0.740。
基于这些实验结果，深度学习可以提高根尖片上评估龋齿和根尖周炎的准确性和一致性。
深度学习模型可以提高准确性和一致性，减少牙医的工作量，使人工智能成为临床实践的有力工具。
