Hubei Bioinformatics and Molecular Imaging Key Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
Department of Dental General and Emergency, The Affiliated Stomatological Hospital, Jiangxi Medical College, Nanchang University, Jiangxi Province Key Laboratory of Oral Biomedicine, Jiangxi Province Clinical Research Center for Oral Diseases, Nanchang, 330038, Jiangxi Province, China.
Int J Comput Assist Radiol Surg. 2024 Apr;19(4):779-790. doi: 10.1007/s11548-023-03047-1. Epub 2024 Jan 3.
Dental health has been getting increased attention. Timely detection of non-normal teeth (caries, residual root, retainer, teeth filling, etc.) is of great importance for people's health, well-being, and quality of life. This work proposes a rapid detection of non-normal teeth based on improved Mask R-CNN, aiming to achieve comprehensive screening of non-normal teeth on dental X-ray images.
An improved Mask R-CNN based on attention mechanism was used to develop a non-normal teeth detection method trained on a high-quality annotated dataset, which can segment the whole mask of each non-normal tooth on the dental X-ray image immediately.
The average precision (AP) of the proposed non-normal teeth detection was 0.795 with an intersection-over-union of 0.5 and max detections (maxDets) of 32, which was higher than that of the typical Mask R-CNN method (AP = 0.750). In addition, validation experiments showed that the evaluation metrics (AP, recall, precision-recall (P-R) curve) of the proposed method were superior to those of the Mask R-CNN method. Furthermore, the experimental results indicated that proposed method exhibited a high sensitivity (95.65%) in detecting secondary caries. The proposed method took about 0.12 s to segment non-normal teeth on one dental X-ray image using the laptop (8G memory, NVIDIA RTX 3060 graphics processing unit), which was much faster than conventional manual methods.
The proposed method enhances the accuracy and efficiency of abnormal tooth diagnosis for practitioners, while also facilitating early detection and treatment of dental caries to substantially lower patient costs. Additionally, it can enable rapid and objective evaluation of student performance in dental examinations.
人们越来越关注口腔健康。及时发现非正常牙齿（龋齿、残根、保持器、补牙等）对人们的健康、幸福和生活质量至关重要。本工作提出了一种基于改进的 Mask R-CNN 的快速非正常牙齿检测方法，旨在实现对口腔 X 射线图像中非正常牙齿的全面筛查。
使用基于注意力机制的改进 Mask R-CNN 开发了一种非正常牙齿检测方法，该方法在高质量标注数据集上进行训练，可以立即对口腔 X 射线图像中的每个非正常牙齿的整个掩模进行分割。
所提出的非正常牙齿检测的平均精度（AP）为 0.795，交并比（IoU）为 0.5，最大检测数（maxDets）为 32，高于典型的 Mask R-CNN 方法（AP=0.750）。此外，验证实验表明，所提出方法的评估指标（AP、召回率、精度-召回率（P-R）曲线）均优于 Mask R-CNN 方法。此外，实验结果表明，该方法在检测继发性龋齿方面具有很高的灵敏度（95.65%）。该方法使用笔记本电脑（8G 内存，NVIDIA RTX 3060 图形处理单元）对一张口腔 X 射线图像进行非正常牙齿分割大约需要 0.12 秒，比传统的手动方法快得多。
该方法提高了临床医生对异常牙齿诊断的准确性和效率，同时也有利于早期发现和治疗龋齿，从而降低患者的成本。此外，它还可以实现对口腔检查中学生表现的快速和客观评估。
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