Tianjin First Central Hospital, Tianjin 300192, China.
J Healthc Eng. 2021 Sep 21;2021:4676316. doi: 10.1155/2021/4676316. eCollection 2021.
In order to deeply study oral three-dimensional cone beam computed tomography (CBCT), the diagnosis of oral and facial surgical diseases based on deep learning was studied. The utility model related to a deep learning-based classification algorithm for oral neck and facial surgery diseases (deep diagnosis of oral and maxillofacial diseases, referred to as DDOM) is brought out; in this method, the DDOM algorithm proposed for patient classification, lesion segmentation, and tooth segmentation, respectively, can effectively process the three-dimensional oral CBCT data of patients and carry out patient-level classification. The segmentation results show that the proposed segmentation method can effectively segment the independent teeth in CBCT images, and the vertical magnification error of tooth CBCT images is clear. The average magnification rate was 7.4%. By correcting the equation of value and CBCT image vertical magnification rate, the magnification error of tooth image length could be reduced from 7.4. According to the CBCT image length of teeth, the distance from tooth center to FOV center, and the vertical magnification of CBCT image, the data closer to the real tooth size can be obtained, in which the magnification error is reduced to 1.0%. Therefore, it is proved that the 3D oral cone beam electronic computer based on deep learning can effectively assist doctors in three aspects: patient diagnosis, lesion localization, and surgical planning.
为了深入研究口腔三维锥形束 CT（CBCT），基于深度学习的口腔颌面外科疾病诊断研究。提出了一种基于深度学习的口腔颌面外科疾病分类算法（口腔颌面疾病的深度诊断，简称 DDOM）；在该方法中，分别为患者分类、病变分割和牙齿分割提出的 DDOM 算法，可以有效地处理患者的三维口腔 CBCT 数据，并进行患者级分类。分割结果表明，所提出的分割方法可以有效地分割 CBCT 图像中的独立牙齿，并且牙齿 CBCT 图像的垂直放大误差明显。平均放大率为 7.4%。通过修正 值方程和 CBCT 图像垂直放大率，可以将牙齿图像长度的放大误差从 7.4 降低。根据牙齿的 CBCT 图像长度、从牙齿中心到 FOV 中心的距离和 CBCT 图像的垂直放大率，可以获得更接近真实牙齿尺寸的数据，其中放大误差降低到 1.0%。因此，证明了基于深度学习的 3D 口腔锥形束电子计算机可以有效地辅助医生在三个方面进行工作：患者诊断、病变定位和手术规划。
