Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.
Department of Oral Pathology, Aichi-Gakuin University School of Dentistry, Nagoya, Japan.
Oral Radiol. 2021 Jul;37(3):487-493. doi: 10.1007/s11282-020-00485-4. Epub 2020 Sep 19.
This study aimed to examine the performance of deep learning object detection technology for detecting and identifying maxillary cyst-like lesions on panoramic radiography.
Altogether, 412 patients with maxillary cyst-like lesions (including several benign tumors) were enrolled. All panoramic radiographs were arbitrarily assigned to the training, testing 1, and testing 2 datasets of the study. The deep learning process of the training images and labels was performed for 1000 epochs using the DetectNet neural network. The testing 1 and testing 2 images were applied to the created learning model, and the detection performance was evaluated. For lesions that could be detected, the classification performance (sensitivity) for identifying radicular cysts or other lesions were examined.
The recall, precision, and F-1 score for detecting maxillary cysts were 74.6%/77.1%, 89.8%/90.0%, and 81.5%/83.1% for the testing 1/testing 2 datasets, respectively. The recall was higher in the anterior regions and for radicular cysts. The sensitivity was higher for identifying radicular cysts than for other lesions.
Using deep learning object detection technology, maxillary cyst-like lesions could be detected in approximately 75-77%.
本研究旨在探讨深度学习目标检测技术在检测和识别全景片上上颌囊肿样病变中的性能。
共纳入 412 例上颌囊肿样病变（包括几种良性肿瘤）患者。所有全景片均被随机分配到研究的训练、测试 1 和测试 2 数据集。使用 DetectNet 神经网络对训练图像和标签进行 1000 个时期的深度学习。将创建的学习模型应用于测试 1 和测试 2 图像，评估检测性能。对于可以检测到的病变，检查识别根囊肿或其他病变的分类性能（敏感性）。
在测试 1/测试 2 数据集上，检测上颌囊肿的召回率、精确率和 F1 得分为 74.6%/77.1%、89.8%/90.0%和 81.5%/83.1%。在前部区域和根囊肿中，召回率较高。识别根囊肿的敏感性高于其他病变。
使用深度学习目标检测技术，可以检测到大约 75-77%的上颌囊肿样病变。
