Department of Radiology, Hualien Armed Forces General Hospital, Hualien County, Taiwan.
Department of Health Beauty, Fooyin University, Kaohsiung City, Taiwan.
J Xray Sci Technol. 2023;31(6):1315-1332. doi: 10.3233/XST-230171.
Dental panoramic imaging plays a pivotal role in dentistry for diagnosis and treatment planning. However, correctly positioning patients can be challenging for technicians due to the complexity of the imaging equipment and variations in patient anatomy, leading to positioning errors. These errors can compromise image quality and potentially result in misdiagnoses.
This research aims to develop and validate a deep learning model capable of accurately and efficiently identifying multiple positioning errors in dental panoramic imaging.
This retrospective study used 552 panoramic images selected from a hospital Picture Archiving and Communication System (PACS). We defined six types of errors (E1-E6) namely, (1) slumped position, (2) chin tipped low, (3) open lip, (4) head turned to one side, (5) head tilted to one side, and (6) tongue against the palate. First, six Convolutional Neural Network (CNN) models were employed to extract image features, which were then fused using transfer learning. Next, a Support Vector Machine (SVM) was applied to create a classifier for multiple positioning errors, using the fused image features. Finally, the classifier performance was evaluated using 3 indices of precision, recall rate, and accuracy.
Experimental results show that the fusion of image features with six binary SVM classifiers yielded high accuracy, recall rates, and precision. Specifically, the classifier achieved an accuracy of 0.832 for identifying multiple positioning errors.
This study demonstrates that six SVM classifiers effectively identify multiple positioning errors in dental panoramic imaging. The fusion of extracted image features and the employment of SVM classifiers improve diagnostic precision, suggesting potential enhancements in dental imaging efficiency and diagnostic accuracy. Future research should consider larger datasets and explore real-time clinical application.
口腔全景成像在口腔医学的诊断和治疗计划中起着关键作用。然而，由于成像设备的复杂性和患者解剖结构的差异，技术人员正确定位患者可能具有挑战性，这会导致定位错误。这些错误会影响图像质量，并可能导致误诊。
本研究旨在开发和验证一种深度学习模型，该模型能够准确、高效地识别口腔全景成像中的多种定位错误。
这是一项回顾性研究，使用了从医院图像存档与通信系统（PACS）中选择的 552 张全景图像。我们定义了六种类型的错误（E1-E6），即（1）坐姿不正，（2）下巴过低，（3）嘴唇张开，（4）头向一侧倾斜，（5）头向一侧倾斜，（6）舌头抵住上颚。首先，使用六个卷积神经网络（CNN）模型提取图像特征，然后使用迁移学习融合这些特征。接下来，使用融合后的图像特征，通过支持向量机（SVM）创建一个用于多种定位错误的分类器。最后，使用精确率、召回率和准确率三个指标评估分类器的性能。
实验结果表明，将图像特征与六个二进制 SVM 分类器融合可以获得较高的准确率、召回率和精度。具体来说，该分类器识别多种定位错误的准确率为 0.832。
本研究表明，六个 SVM 分类器可有效识别口腔全景成像中的多种定位错误。提取的图像特征融合和 SVM 分类器的使用提高了诊断精度，这表明口腔成像效率和诊断准确性可能得到提高。未来的研究应考虑更大的数据集，并探索实时临床应用。
