Fatima Anum, Shafi Imran, Afzal Hammad, Mahmood Khawar, Díez Isabel de la Torre, Lipari Vivian, Ballester Julien Brito, Ashraf Imran
National Centre for Robotics, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan.
College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan.
Healthcare (Basel). 2023 Jan 25;11(3):347. doi: 10.3390/healthcare11030347.
Automated dental imaging interpretation is one of the most prolific areas of research using artificial intelligence. X-ray imaging systems have enabled dental clinicians to identify dental diseases. However, the manual process of dental disease assessment is tedious and error-prone when diagnosed by inexperienced dentists. Thus, researchers have employed different advanced computer vision techniques, as well as machine and deep learning models for dental disease diagnoses using X-ray imagery. In this regard, a lightweight Mask-RCNN model is proposed for periapical disease detection. The proposed model is constructed in two parts: a lightweight modified MobileNet-v2 backbone and region-based network (RPN) are proposed for periapical disease localization on a small dataset. To measure the effectiveness of the proposed model, the lightweight Mask-RCNN is evaluated on a custom annotated dataset comprising images of five different types of periapical lesions. The results reveal that the model can detect and localize periapical lesions with an overall accuracy of 94%, a mean average precision of 85%, and a mean insection over a union of 71.0%. The proposed model improves the detection, classification, and localization accuracy significantly using a smaller number of images compared to existing methods and outperforms state-of-the-art approaches.
自动牙科影像解读是利用人工智能进行研究的最为丰富的领域之一。X射线成像系统使牙科临床医生能够识别牙科疾病。然而，当由经验不足的牙医进行诊断时，牙科疾病评估的手工过程既繁琐又容易出错。因此，研究人员采用了不同的先进计算机视觉技术，以及用于使用X射线图像进行牙科疾病诊断的机器和深度学习模型。在这方面，提出了一种轻量级的Mask-RCNN模型用于根尖周疾病检测。所提出的模型由两部分构建而成：针对一个小数据集上的根尖周疾病定位，提出了一个轻量级的改进型MobileNet-v2主干和基于区域的网络（RPN）。为了衡量所提出模型的有效性，在一个包含五种不同类型根尖周病变图像的自定义注释数据集上对轻量级Mask-RCNN进行了评估。结果表明，该模型能够检测和定位根尖周病变，总体准确率为94%，平均精度为85%，交并比为71.0%。与现有方法相比，所提出的模型使用较少数量的图像显著提高了检测、分类和定位精度，并且优于现有最先进的方法。
